Metabolic and Functional Imaging with Radiation Therapy

Volume 21, Number 2

April 2011

Introduction: Metabolic and Functional Imaging in Radiation Therapy

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hank you for taking the time to read this issue of Seminars in Radiation Oncology. In recent years, a number of developments have occurred that have most likely permanently embedded advanced imaging into the routine and experimental practices of radiation oncology. Although Seminars in Radiation Oncology, as well as several other journals, have reviewed various aspects of imaging in cancer management, the significant advances in a subset of imaging, specifically molecular and physiological imaging as applied to the radiotherapy process, warrant special attention. Molecular imaging is certainly not new. Long before the suggestion of a biological target volume,1 Positron emission tomography (PET) scans were routinely performed and applied quantitatively as well as qualitatively for treatment management.2 Similarly, simple physiological magnetic resonance imaging (eg, blood oxygen level dependent) was suggested in the 1990s as a means of avoiding the sensorimotor cortex.3 Additional early work using molecular and physiological imaging included efforts to select persistent or active tumor subregions in the prostate for dose boosting4 and the hypothesis that the lung contains a heterogeneous distribution of functional regions that can be identified using ventilation and/or perfusion images for functional avoidance to lessen toxicity of an otherwise equivalent tumor treatment.5 These examples lay the groundwork for what could be a revolution in imaging applications in radiation oncology. There are international trials proposed or underway in which imaging, as a biomarker, is intended to modify therapeutic plans. Furthermore, national efforts exist to establish quantitative imaging as a biomarker to aid in prognosis, individualization, and response assessment to therapy. These efforts hint at the significant practice and infrastructure changes necessary to move modern imaging from a useful qualitative methodology into a quantitative means from which discriminatory decisions and longitudinal evaluations can be supported faithfully. Although led in part by the need to support large-scale pharmaceutical trials, the radiation oncology community is rapidly assimilating the benefits of these efforts. Nonetheless, some of the early concepts being adopted in practice and clinical trials are not without controversy, and the various authors in this issue weigh in on some of the concerns that need to be addressed.

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This issue has 8 articles. The first 2 of these articles deal with molecular agents. Grimm and Scheinberg review the advances in nanotechnology for imaging and therapy. Wahl et al describe the advancing role of PET and single photon emission computed tomography (SPECT) agents in radiation oncology. Bentzen and Gregoire discuss the debatable issue of dose painting using functional maps as a guide. Das and Ten Haken overview the process of integrating functional maps into radiotherapy treatment plan optimization. The article from Padhani overviews diffusion-weighted imaging and discusses potential applications in the body. Gerstner and Sorensen extend this discussion to include diffusion tensor imaging and applications in the central nervous system. Cao describes evolving experimental evidence supporting dynamic contrast-enhanced imaging of perfusion as a biomarker in various treatment sites. Finally, Bayouth et al briefly overview example applications of quantitative imaging and discuss some of the needed education, effort, and infrastructure to support their advancement through clinical trials and into practice. It has been quite enjoyable assembling this wonderful collection of expert opinions and reviews for this issue. I hope you find it to be worthwhile and intellectually stimulating reading. James Balter, PhD, FAAPM Guest Editor

References 1. Ling CC, Humm J, Larson S, et al: Towards multidimensional radiotherapy (MD-CRT): Biological imaging and biological conformality. Int J Radiat Oncol Biol Phys 47:551-560, 2000 2. Schad LR, Boesecke R, Schlegel W, et al. Three dimensional image correlation of CT, MR, and PET studies in radiotherapy treatment planning of brain tumors. J Comput Assist Tomogr 11:948-954, 1987 3. Hamilton RJ, Sweeney PJ, Pelizzari CA, et al: Functional imaging in treatment planning of brain lesions. Int J Radiat Oncol Biol Phys 37:181188, 1997 4. Zaider M, Zelefsky MJ, Lee EK, et al. Treatment planning for prostate implants using magnetic-resonance spectroscopy imaging. Int J Radiat Oncol Biol Phys 47:1085-1096, 2000 5. Marks LB, Spencer DP, Bentel GC, et al. The utility of SPECT lung perfusion scans in minimizing and assessing the physiologic consequences of thoracic irradiation. Int J Radiat Oncol Biol Phys 26:659-668, 1993

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Will Nanotechnology Influence Targeted Cancer Therapy? Jan Grimm, MD, PhD, and David A. Scheinberg, MD, PhD The rapid development of techniques that enable synthesis (and manipulation) of matter on the nanometer scale and the development of new nanomaterials will play a large role in disease diagnosis and treatment, specifically in targeted cancer therapy. Targeted nanocarriers are an intriguing means to selectively deliver high concentrations of cytotoxic agents or imaging labels directly to the cancer site. Often, solubility issues and an unfavorable biodistribution can result in a suboptimal response of novel agents even though they are very potent. New nanoparticulate formulations allow simultaneous imaging and therapy (“theranostics”), which can provide a realistic means for the clinical implementation of such otherwise suboptimal formulations. In this review, we did not attempt to provide a complete overview of the rapidly enlarging field of nanotechnology in cancer; rather, we presented properties specific to nanoparticles and examples of their uses, which show their importance for targeted cancer therapy. Semin Radiat Oncol 21:80-87 © 2011 Elsevier Inc. All rights reserved.

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anotechnology is defined as the technology developing and working with materials smaller than 100 nm in one dimension. As a reference, the length of a typical carboncarbon bond is about 0.12 to 0.15 nm; the diameter of a DNA strand is about 2 nm. As such, this includes manmade structures but also biological entities, such as viruses or proteins. It excludes structures with a metabolism because the smallest cellular life forms (from the genus Mycoplasm) are 100 to 200 nm in size. The concept of nanotechnology was first described in a talk by the physicist and Nobel laureate Richard Feynman at a meeting of the American Physical Society in 1959 at Caltech.1 In this talk, Feynman described the concept of “plenty of room at the bottom,” considering the possibility to manipulate individual atoms directly. Thirty years later, in September 1989, the physicist Donald M. Eigler at the IBM Almaden Research Center precisely arranged 35 xenon atoms to create “IBM” on the atomic scale (Fig. 1) using the scanning tunnel microscope he developed.2 Other important milestones are summarized in a recent review.3 This also shows that the refinement of tools to explore the nanoscale world

Molecular Pharmacology and Chemistry Program, Memorial Sloan-Kettering Cancer Center, New York, NY. DAS is a consultant to Encyse Pharmaceuticals, a nanotechnology company. Address reprint requests to David A. Scheinberg, MD, PhD, Molecular Pharmacology and Chemistry Program, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10065. E-mail: [email protected]

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(such as electron microscopes, scanning tunnel microscopes, or atomic force microscopes) is an important factor to actually drive the field forward.

Properties of Nanoparticles Interestingly, a range of properties displayed by nanosized materials differs significantly from their mesoscale relatives made of the same materials. These include mechanical, thermal, optical, mechanical, and catalytic properties. This is of importance because a large portion of the universe of biological activities that proceeds at the level of the cell surface in glomerular filtration, protein and DNA synthesis, modification and degradation, or hepatic clearance occurs at the nanoscale. Thus, there are important differences if these biological structures and processes encounter a macroscopic or even mesoscopic material or one with a size of their own. The very small size of nanomaterials also implicates a large ratio of surface atoms to volume of the material; this allows the generation of high specific activity imaging reagents (10-100 times that possible with conventional agents in current use). In addition, it provides a high surface charge in a narrow spatial confinement.4 These characteristics, in turn, may substantially modify properties of the bulk materials. For example, metallic nanoparticles can be used as very active catalysts,5 and the large surface area can be modified with a variety of functional groups.6 An increasing range of nanomaterials is described,3 including targeted nanogenerators for radiotherapy7 or even nano-

Nanotechnology and cancer therapy

Figure 1 A patterned array of xenon atoms on a nickel surface (atomic structure not resolved). Each letter is 50 nm from top to bottom. (Reprinted with permission.2)

machines, such as “walking” DNA strands8 or a DNA-based DNA nanomotor,9 paving the way to a molecular robotics. Although the latter may play a role in the future (eg, as the nanosurgeons envisioned by Feynman1), we concentrate on the role of nanoparticles, which offer an immense variety of sizes, shapes, dispersities, surface charges, valencies, and structural designs. Nanoparticles are particularly appealing for several reasons: 1. Multivalency and multimodality: nanoparticles provide very large surface to volume ratios. A single-wall carbon nanotube with a total volume similar to a typical large protein (eg, 100-150 kDa) provides a 15⫻ increased

81 surface compared with a large protein with a comparable volume.3 The enlarged surface area can be used to couple a large amount of targeting ligands to the particle. This increased amount of ligands on the surface may enhance binding of the particle to the desired target significantly (Fig. 2A).6 A recent report showed that even weak small organic molecules as ligands can significantly enhance the avidity of the particle towards its target by up to 4 orders of magnitude through multivalent interactions.10 Furthermore, in addition to targeting ligands, imaging labels can be attached to the particle’s surface (eg, fluorochromes for optical imaging, metals for magnetic resonance imaging [MRI], or radioactive nuclides for nuclear imaging, allowing for simultaneous multimodal imaging [Fig. 2B]). In this case, the same particle can be used for MRI and optical imaging,11,12 using the high sensitivity of optical imaging in combination with the high resolution and soft-tissue contrast of MRI. Adding nuclides to this construct allows for magnetic resonance/optical/nuclear imaging.13 With the advancements of clinical multimodality imaging (eg, the combination of positron emission tomography with a computed tomography [CT] scan or MRI), this property will become increasingly important. This is particularly valuable in drug development in preclinical imaging and biodistribution studies and also in early-phase trials in humans. In addition, the ability to attach large numbers of imaging agents to a single particle will dramatically enhance sensitivity by increasing signal intensity. In one interesting instance, the containment of gadolinium within a nanotube further enhanced its sensitivity by 40-fold.

Figure 2 (A) The principle of multivalency. Antibodies are bivalent while small peptides or small molecules are monovalent. A particle with multiple targeting moieties provides multiple binding sides and is multivalent. Multivalency increases the validity to the target considerably. (B) A monomodal imaging agent is suitable to be detected with 1 modality; a multimodal agent can be detected with several imaging modalities because it contains several different labels. This allows the advantages of various modalities in 1 particle to be combined.

J. Grimm and D.A. Scheinberg

82 2. Size and coating: the biodistribution of nanoparticles can be substantially influenced by their surface coating and size. Dendrimers (repeatedly branched, roughly spherical large molecules) or other metallic and organic particles can consist entirely of polymers or crystals, but they can also contain a core material that is usually surrounded by a biocompatible coating material. The coating permits or increases the solubility of the particle’s central core material and in some cases (such as with quantum dots (QD) that contain potentially toxic heavy metals) allows for protection of the recipient. Therefore, the coating is especially important for materials that are not easily soluble in aqueous solutions and therefore in serum, particularly for metallic particles, such as QD, gold or iron oxide particles.14-17 Furthermore, by choosing the right properties of the coating (foremost charge), the particles can be made relatively invisible to a large nonspecific uptake sink in the body, the mononuclear phagocyte system (MPS) (also called the reticuloendothelial system). Polyethylene glycol (PEG) is so far the most frequently used coating material, but numerous other polymeric and monomeric coatings have been used. PEG molecules are thought to form a protective hydrophilic layer on the particle’s surface that inhibits interaction with opsonization-facilitating blood components, thus reducing the uptake of PEG-coated particles in macrophages and Kupffer cells.18 As a result, the PEG coating reduces uptake by macrophages of the MPS and provides relatively long plasma residence times. However, even the length of the coating PEG chains has an influence on the particle’s properties; varying PEG chain length results in major changes in organ-/tissue-selective biodistribution and clearance from the body.19 By contrast, it is known that positive charges facilitate the uptake of particles in cells, which has been used for cell labeling.20-22 Furthermore, the size of the particles has a significant influence on the distribution of the particles. Larger particles tend to have a more rapid uptake into the MPS (especially liver and spleen) and consequently a more rapid clearance from the circulation. This is likely related to the finding that the size of the nanoparticle has a substantial effect on the protein absorption, probably secondary to a higher surface density of the organic coating on the surface of smaller nanoparticles.23 Therefore, small nanoparticles have a longer blood half-life and also a higher rate of permeation into tissues, showing the importance of particle size and surface composition for achieving effective, targeted delivery and elimination from the body. By contrast, very small particles (smaller than 5 nm or carbon nanotubes with one dimension of 1 nm) may rapidly clear through glomerular filtration. Furthermore, the dispersion of the particles (ie, the variation of the particle sizes) plays an important role.24 Monodispersed particles are all uniform in size, whereas polydispersed particles show a wide range of size (Fig. 3). Polydispersed or even oligodispersed particles tend to have a range of retention

Figure 3 The dispersion of particles as an indicator of the size variability between particles. Monodispersed particles are all uniform in size without variability; this is the ideal particle preparation. Oligodispersed particles have some variability in size, whereas polydispersed particles show a large range of sizes. Accordingly, oligo- and polydispersed particles show different rates of excretions.

times and biodistributions in an organism representative of the different sizes in the particle population, which makes evaluation of the particle’s impact complex. Furthermore, the shape of a nanoparticle also contributes to its fate in vivo. It especially influences the internalization of particles into cells. For example, it has been shown that rod-like particles have an appreciable advantage over spherical particles in the internalization rates. This is reminiscent of the advantage that many rod-like bacteria have for internalization in nonphagocytic cells.25 3. Controlled synthesis: nanoparticles can be synthesized in a very controlled environment, ideally creating particles that all display the same properties in size, shape, and charge, and, if functionalized, the same amount of functional groups bound to the particles surface. This is immensely important because only monodispersed particles are expected to display the same distribution, biological half-life, and target affinity in vivo, which has become an important criterion for current approval processes. This requirement has led to very sophisticated production methods for some nanoparticles. A new particle fabrication process, “particle replication in nonwetting templates,” has been developed to strictly control physical and chemical traits of the particles.26 This technology uses perfluoropolyether molds, resulting in the production of polymer-based nanoparticles with precise control of size, shape, chemical composition, and surface functionality, tailored to the specific properties required to target a variety of diseases.27 Importantly, all these particles are monodispersed and very well defined. A similar process has been devised to create quantum dots.28 4. Core properties: although nanoparticles can consist entirely of a polymer (such as a dendrimer or a carbon nanotube), they can also contain a central core. This

Nanotechnology and cancer therapy

Figure 4 A schematic representation of advances in magnetic nanoparticle (MNP) design. MNP agents currently under development will have significantly higher relaxivities than earlier generations of MNP and will also have improved synthetic coats for targeted imaging. The red box indicates an approved agent. The green box indicates the next generation of particles. (Reprinted with permission.30)

can be a semiconductive material in the case of quantum dots or a metallic core as exemplified by iron oxide or gold nanoparticles. An iron oxide core renders the particle superparamagnetic and makes it suitable for MRI.16,29 A whole genealogy of iron oxide particles exits (Fig. 4).30 Resovist (Bayer-Schering Pharma, Berlin, Germany) was a particle that has been used clinically but has since been withdrawn from the market. Several others have made it into clinical trials but never received Food and Drug Administration approval (eg, Feridex; AMAG Pharmaceuticals, Lexington, MA). Currently, the only solid particle in clinical use is ferumoxytol (Feraheme, AMAG Pharmaceuticals), which is approved for the treatment of iron deficiency anemia in adult patients with chronic kidney disease; however, these small particles are also suitable for MRI.31,32 Because of their small size, below 50 nm, they are slowly eliminated out of the circulation and can be used as intravascular contrast agents. (They are not currently Food and Drug Administration approved for this application.) In contrast to the standard clinical magnetic resonance contrast agent (chelated gadolinium), iron oxide particles combine not 1 contrast producing atom in 1 molecule, but a cluster of thousands of individual atoms, which is one reason for the higher sensitivity of magnetic resonance for iron oxide particles. This high concentration of paramagnetic nuclei can perturb the local homogeneity of the magnetic field, which is very sensitively detected by magnetic resonance scanners. This makes iron oxide particles uniquely suited to detect very low concentrations on a molecular level, as exemplified by multiple publications, using iron oxide particles for targeted approaches,33-35 cell tracking,22,36 or even activatable imaging agents.37,38 Clinically, iron

83 oxide particles have been used as a sensitive method to detect and characterize liver lesions39 and to detect lymph node metastases.40,41 However, there are also approaches to increase the relaxivity of gadolinium chelates (eg, by combining them with carbon nanotubes). Similar to iron oxide particles, the exterior surface of these nanotubes provides a scaffold for the attachment of chemical groups for solubilizing or targeting purposes. However, their internal space within the hollow tube allows for the encapsulation of atoms, ions, and even small molecules.42,43 Filling ultrashort nanotubes with gadolinium atoms increased the relaxivity (a measurement of the strength of a magnetic resonance contrast agent) dramatically compared with clinically used gadolinium chelates, depending on the magnetic field strength. At the standard magnetic resonance field strength, the relaxivity was nearly 40 times larger, but the effect was even more pronounced (nearly 90 times larger) at very low fields.44 Another interesting new approach is the use of nanodiamonds modified with gadolinium, resulting in a 10-fold higher relaxivity compared with clinically used gadolinium chelates. These nanodiamonds may also induce less oxidative stress to cells than other carbon nanomaterials.45 The exploration of gold nanoparticles began over 150 years ago with colloidal gold. Michael Faraday noted that colloidal gold solutions have properties differing significantly from those of bulk gold. Gold particles have been used for many different applications in cellular biology (eg, to detect different messenger RNA and also for electron microscopy).46,47 Gold nanoparticles are also being studied as contrast agents for CT scans. Targeted contrast agents for CT scans are particularly interesting because of the wide availability of CT scanners. However, CT scanners also require a very high concentration of the currently used iodine in organic molecules even for nontargeted imaging, making targeted imaging with these molecules not feasible because the local concentrations of targeted agents are way too small to be detected with a CT scanner using iodine. Therefore, research is focusing on nanoparticles, such as gold48,49 or bismuth particles,50 that can deliver dense loads of metallic ions for CT imaging (Fig. 5). 5. Beyond imaging—theranostics: besides using particles solely for imaging purposes, they can also carry another payload (a therapeutic agent). In conjunction with the imaging moiety, this creates a theranostic agent (Fig. 6). These are agents that can be used to diagnose disease foci, deliver targeted therapy, and monitor the response to therapy at the same time.51 The integration of diagnostic imaging capability with therapeutic efficiency is believed to be critical to addressing the challenges of cancer heterogeneity and adaptation. Therapeutic agents have been used mostly in the form of liposomes to carry antibiotics52 or anticancer agents53 as well as novel experimental approaches like small interfering RNA.54 Carbon nanotubes have been used for radio-

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allows the accumulation and subsequent retention of macromolecules. Consequently, in such areas (mostly tumors and inflamed or infarcted tissues), large molecules and particles ranging from 10 to 500 nm in size can leave the leaky neovasculature and accumulate in the interstitial space.58,59 Small molecules and particles do not accumulate in tumors likely because of their ability to diffuse back to the circulation.60 EPR-mediated drug delivery is seen as an effective way to bring drugs to tumors, especially macromolecular drugs and drug-loaded pharmaceutical nanocarriers. Therefore, this process is also called “passive targeting.” It is advantageous to use EPR when appropriate targeting ligands are not available, and, even when they are available, EPR may enhance selective delivery to the target site.

Toxicity Issues

Figure 5 CT imaging of a lymph node of a mouse with bismuth nanoparticles. (A and B) Three-dimensional volume renderings of the CT dataset; the length of the reconstruction is 3.8 cm. (C) Coronal slice (length of the slice 2.3 cm). (D) Transverse slice at the height indicated by the horizontal lines in B. The maximal diameter of the mouse is 1.8 cm. The position of the lymph node under the right shoulder is indicated by the ovals. The injection side is in the ipsilateral lower front leg. Note the lack of contrast in the corresponding contralateral (left shoulder) lymph node. (Reprinted with permission from.50)

therapy. Because of their high aspect ratios (great length and narrow width), they have a high surface area for functionalization, allowing the delivery of higher specific activities.55 Because liposomes carry their payload inside of the particle, mechanisms need to be provided for a release of the drug, ideally triggered by the target tissue. For liposomes, for example, an enzymatic trigger mechanism has been developed in which phospholipase releases the drug.56 Some formulations are built to release the drug under environmental conditions found in tumors. A biocompatible polymeric multimodal, multivalent theranostic nanoparticle was constructed that released the drug at a lower pH. This particle allowed for MRI, and optical imaging (via an iron oxide core and fluorochromes as payload) carried multiple folate molecules as targeting ligands and a therapeutic payload of taxol.57

Enhanced Permeability and Retention The enhanced permeability and retention (EPR) effect describes the relative and passive accumulation of macromolecules inside of tumors. This is generally explained by the presence of the high permeability of the tumor vasculature in combination with compromised lymphatic drainage, which

The safety of nanoparticles is attracting the attention of the Food and Drug Administration and also the public and especially the EPA, which is starting to look into the regulation of nanotechnology. As nanotechnology progresses to commercialization and more widespread applications, it is likely that nanomaterials and nanoproducts will be released into the environment, potentially causing harm to the public. It is conceivable that hazards introduced by nanoparticles are different from those posed by conventional chemicals, partially because of the ability of the very small nanoparticles to move readily cross the various biological barriers and their somewhat different physicochemical properties.61 A first reminder of possible hazards associated with the excessive exposure to nanoparticles was published in late 2009.62 Seven female workers were exposed in China to poly-

Figure 6 A schematic drawing of the theranostic approach. The diagnostic particle consists of a diagnostic moiety (yellow) and a coating modified with a binding ligand (green) and targeting an antigen (brown) (here it is deposited between the tumor cells). Imaging is performed with MRI and PET. The particle can carry a payload drug (red) that is released in the tumor creating a theranostic agent and allowing for imaging and therapy of the tumor.

Nanotechnology and cancer therapy acrylate nanoparticles at the workplace and presented with shortness of breath and pleural effusions. Lung biopsies revealed nonspecific pulmonary inflammation, pulmonary fibrosis, and pleural foreign-body granulomas. Electron microscopy showed nanoparticles in pulmonary epithelial and mesothelial cells and in the chest fluid. These cases raised concern that long-term exposure to at least some nanoparticles without protective measures may cause serious damage to the lungs. This concern has not been diminished by other reports on the toxicity of these materials in animals. Around 2009, reports on pulmonary toxicity of carbon nanotubes emerged63,64 as well as a report on the induction of mesotheliomas in rats65 and on liver damage66 by multiwalled carbon nanotubes. In addition, metal nanoparticles, metal-oxide nanoparticles, quantum dots, fullerenes, and fibrous nanomaterials were found to cause chromosomal fragmentation, DNA-strand breakages, point mutations, oxidative DNA adducts, and alterations in gene expression profiles even though inconsistencies in many studies were also found.67 These reports lead to the calls for more governmental oversight, nanodosimetry, maximum-tolerated doses, and the generation of the field of nanotoxicology. As a potential solution to the problem of the toxicity of nanomaterials (most obvious in the example of cadmiumcontaining quantum dots), a rapid elimination of these materials from the body by renal clearance has been suggested. The requirements for renal filtration and urinary excretion of inorganic, metal-containing nanoparticles have been defined.68 A final hydrodynamic diameter below 5.5 nm resulted in rapid and efficient urinary excretion and the elimination of quantum dots from the body. Zwitterionic or neutral organic coating of the particles prevented their adsorption to serum proteins, thus stabilizing the hydrodynamic diameter to below 15 nm and promoting renal excretion. The same principle has been recently used for silica-coated fluorochrome nanoparticles69 and even carbon nanotubes.70

Conclusions Will nanotechnology influence targeted cancer therapy? In this review, we focused on nanoparticles and discussed that these particles can be applied to fit the requirements and needs for individual applications. By choosing the right size, shape, coating, and charge as well as targeting moiety (Fig. 7), the fate of the particles in the body can be well predicted. Nonspecific targeting of the particles can already be achieved via the EPR effect and can be made more efficient by using targeting moieties on the surface, increasing the binding affinity and avidity further. Drugs can be delivered this way by specifically targeting them to the tumor where the release of the drug is triggered by the tumor’s environment (pH or enzyme mediated). Adding an imaging label to the particle enables direct monitoring of the biodistribution, predictions of toxicity, and therapeutic effects (and might in the future also aid in visualizing the triggered release of the payload). By contrast, the particles can be used entirely for imaging as exemplified with iron oxide nanoparticles. Toxicity issues

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Figure 7 The main properties influencing the distribution, elimination, and targeting of particles to tumors. Refer to the main text for details.

have been raised for some particles and materials but can probably be circumvented by constructing particles that are eliminated quickly from the body without any retention. Taking all this into account, we believe that the answer to the opening question is a sound “yes, in a positive way.”

References 1. Feynman RP, Robbins J, Dyson FJ: The Pleasure of Finding Things Out. Cambridge, MA, Perseus Books, 1999 2. Eigler DM, Schweizer EK: Positioning single atoms with a scanning tunnelling microscope. Nature 344:524-526, 1990 3. Scheinberg DA, Villa CH, Escorcia FE, et al: Conscripts of the infinite armada: Systemic cancer therapy using nanomaterials. Nat Rev Clin Oncol 7:266-276, 2010 4. Cao G: Nanostructures and Nanomaterials: Synthesis, Properties and Applications. London, Imperial College Press, 2004 5. Gao L, Zhuang J, Nie L, et al: Intrinsic peroxidase-like activity of ferromagnetic nanoparticles. Nat Nanotechnol 2:577-583, 2007 6. Montet X, Funovics M, Montet-Abou K, et al: Multivalent effects of RGD peptides obtained by nanoparticle display. J Med Chem 49:60876093, 2006 7. McDevitt MR, D Ma, Lai LT, et al: Tumor therapy with targeted atomic nanogenerators. Science 294:1537-1540, 2001 8. Shin JS, Pierce NA: A synthetic DNA walker for molecular transport. J Am Chem Soc 126:10834-10835, 2004 9. Klapper Y, Sinha N, Ng TW, et al: A rotational DNA nanomotor driven by an externally controlled electric field. Small 6:44-47, 2010 10. Tassa C, Duffner JL, Lewis TA, et al: Binding affinity and kinetic analysis of targeted small molecule-modified nanoparticles. Bioconjug Chem 21:14-19, 2010 11. Josephson L, Kircher MF, Mahmood U, et al: Near-infrared fluorescent nanoparticles as combined MR/optical imaging probes. Bioconjug Chem 13:554-560, 2002 12. Tannous BA, Grimm J, Perry KF, et al: Metabolic biotinylation of cell surface receptors for in vivo imaging. Nat Methods 3:391-396, 2006 13. Nahrendorf M, Zhang H, Hembrador S, et al: Nanoparticle PET-CT imaging of macrophages in inflammatory atherosclerosis. Circulation 117:379-387, 2008 14. Grimm J, Karger N, Lusse S, et al: Characterization of ultrasmall magnetite [correction of paramagnetic magnetite] particles as superparamagnetic contrast agents in MRI. Invest Radiol 35:553-556, 2000 15. Romberg B, Hennink WE, Storm G: Sheddable coatings for long-circulating nanoparticles. Pharmacol Res 25:55-71, 2008

86 16. Wunderbaldinger P, Josephson L, Weissleder R: Crosslinked iron oxides (clio): A new platform for the development of targeted MR contrast agents. Acad Radiol 9:S304-S306, 2002 (suppl 2) 17. Yu WW: Semiconductor quantum dots: Synthesis and water-solubilization for biomedical applications. Expert Opin Biol Ther 8:15711581, 2008 18. Bazile D, Prud’Homme C, Bassoullet MT, et al: Stealth me. PEG-PLA nanoparticles avoid uptake by the mononuclear phagocytes system. J Pharmacol Sci 84:493-498, 1995 19. Choi HS, Ipe BI, Misra P, et al: Tissue- and organ-selective biodistribution of NIR fluorescent quantum dots. Nano Lett 9:2354-2359, 2009 20. Grimm J, Kircher MF, Weissleder R: Cell tracking: Principles and applications. Radiologe 47:25-33, 2007 21. Josephson L, Tung CH, Moore A, et al: High-efficiency intracellular magnetic labeling with novel superparamagnetic-Tat peptide conjugates. Bioconjug Chem 10:186-191, 1999 22. Kircher MF, Allport JR, Graves EE, et al: In vivo high resolution threedimensional imaging of antigen-specific cytotoxic T-lymphocyte trafficking to tumors. Cancer Res 63:6838-6846, 2003 23. Fang C, Shi B, Pei YY, et al: In vivo tumor targeting of tumor necrosis factor-alpha-loaded stealth nanoparticles: Effect of MePEG molecular weight and particle size. Eur J Pharm Sci 27:27-36, 2006 24. Alexis F, Pridgen E, Molnar LK, et al: Factors affecting the clearance and biodistribution of polymeric nanoparticles. Mol Pharm 5:505-515, 2008 25. Gratton SE, Ropp PA, Pohlhaus PD, et al: The effect of particle design on cellular internalization pathways. Proc Natl Acad Sci U S A 105: 11613-11618, 2008 26. Rolland JP, Maynor BW, Euliss LE, et al: Direct fabrication and harvesting of monodisperse, shape-specific nanobiomaterials. J Am Chem Soc 127:10096-10100, 2005 27. Jeong W, Napier ME, DeSimone JM: Challenging nature’s monopoly on the creation of well-defined nanoparticles. Nanomedicine (Lond) 5:633-639, 2010 28. Hampton MJ, Templeton JL, DeSimone JM: Direct patterning of CdSe quantum dots into sub-100 nm structures. Langmuir 26:3012-3015, 2010 29. Weissleder R, Elizondo G, Wittenberg J, et al: Ultrasmall superparamagnetic iron oxide: Characterization of a new class of contrast agents for MR imaging. Radiology 175:489-493, 1990 30. Sosnovik DE, Nahrendorf M, Weissleder R: Magnetic nanoparticles for MR imaging: Agents, techniques and cardiovascular applications. Basic Res Cardiol 103:122-130, 2008 31. Balakrishnan VS, Rao M, Kausz AT, et al: Physicochemical properties of ferumoxytol, a new intravenous iron preparation. Eur J Clin Invest 39:489-496, 2009 32. Provenzano R, Schiller B, Rao M, et al: Ferumoxytol as an intravenous iron replacement therapy in hemodialysis patients. Clin J Am Soc Nephrol 4:386-393, 2009 33. Allkemper T, Bremer C, Matuszewski L, et al: Optimization of iron oxides for contrast-enhanced MRA: Results of an expereiemental study. Eur Radiol 9:76, 1999 (suppl, abstr) 34. Lee JH, Huh YM, YW, Seo JW, et al: Artificially engineered magnetic nanoparticles for ultra-sensitive molecular imaging. Nat Med 13:95-99, 2007 35. Montet X, Montet-Abou K, Reynolds F, et al: Nanoparticle imaging of integrins on tumor cells. Neoplasia 8:214-222, 2006 36. Arbab AS, Yocum GT, Kalish H, et al: Efficient magnetic cell labeling with protamine sulfate complexed to ferumoxides for cellular MRI. Blood 104:1217-1223, 2004 37. Grimm J, Perez JM, Josephson L, et al: Novel nanosensors for rapid analysis of telomerase activity. Cancer Res 64:639-643, 2004 38. Perez JM, Josephson L, O’Loughlin T, et al: Magnetic relaxation switches capable of sensing molecular interactions. Nat Biotechnol 20: 816-820, 2002 39. Reimer P, Balzer T: Ferucarbotran (resovist): A new clinically approved RES-specific contrast agent for contrast-enhanced MRI of the liver: Properties, clinical development, and applications. Eur Radiol 13: 1266-1276, 2003

J. Grimm and D.A. Scheinberg 40. Harisinghani MG, Barentsz J, Hahn PF, et al: Noninvasive detection of clinically occult lymph-node metastases in prostate cancer. N Engl J Med 348:2491-2499, 2003 41. Harisinghani MG, Weissleder R: Sensitive, noninvasive detection of lymph node metastases. PLoS Med 1:e66, 2004 42. Mackeyev YA, Marks JW, Rosenblum MG, et al: Stable containment of radionuclides on the nanoscale by cut single-wall carbon nanotubes. J Phys Chem B 109:5482-5484, 2005 43. Monthioux M: Filling single-wall carbon nanotubes. Carbon 40:18091823, 2002 44. Sitharaman B, Kissell KR, Hartman KB, et al: Superparamagnetic gadonanotubes are high-performance MRI contrast agents. Chem Commun (Camb) 31:3915-3917, 2005 45. Manus LM, Mastarone DJ, Waters EA, et al: Gd(III)-nanodiamond conjugates for MRI contrast enhancement. Nano Lett 10:484-489, 2010 46. Giljohann DA, Seferos DS, Daniel WL, et al: Gold nanoparticles for biology and medicine. Angew Chem Int Ed Engl 49:3280-3294, 2010 47. Seferos DS, Giljohann DA, Hill HD, et al: Nano-flares: probes for transfection and mRNA detection in living cells. J Am Chem Soc 129:1547715479, 2007 48. Park JA, Kim HK, Kim JH, et al: Gold nanoparticles functionalized by gadolinium-DTPA conjugate of cysteine as a multimodal bioimaging agent. Bioorg Med Chem Lett 20:2287-2291, 2010 49. Aydogan B, Li J, Rajh T, et al: AuNP-DG: deoxyglucose-labeled gold nanoparticles as X-ray computed tomography contrast agents for cancer imaging. Mol Imaging Biol 12:463-467, 2010 50. Rabin O, Perez JM, Grimm J, et al: An X-ray computed tomography imaging agent based on long-circulating bismuth sulphide nanoparticles. Nat Mater 5:118-122, 2006 51. Sumer B, Gao J: Theranostic nanomedicine for cancer. Nanomedicine (Lond) 3:137-140, 2008 52. Drulis-Kawa Z, Dorotkiewicz-Jach A: Liposomes as delivery systems for antibiotics. Int J Pharm 387:187-198, 2010 53. Alexis F, Pridgen EM, Langer R, et al: Nanoparticle technologies for cancer therapy. Handb Exp Pharmacologist 197:55-86, 2010 54. Ozpolat B, Sood AK, Lopez-Berestein G: Nanomedicine based approaches for the delivery of siRNA in cancer. J Intern Med 267:44-53, 2010 55. Escorcia FE, McDevitt MR, Villa CH, et al: Targeted nanomaterials for radiotherapy. Nanomedicine (Lond) 2:805-815, 2007 56. Pedersen PJ, Adolph SK, Subramanian AK, et al: Liposomal formulation of retinoids designed for enzyme triggered release. J Med Chem 53: 3782-3792, 2010 57. Santra S, Kaittanis C, Grimm J, et al: Drug/dye-loaded, multifunctional iron oxide nanoparticles for combined targeted cancer therapy and dual optical/magnetic resonance imaging. Small 5:1862-1868, 2009 58. Matsumura Y, Maeda H: A new concept for macromolecular therapeutics in cancer chemotherapy: Mechanism of tumoritropic accumulation of proteins and the antitumor agent smancs. Cancer Res 46:6387-6392, 1986 59. Torchilin V: Tumor delivery of macromolecular drugs based on the EPR effect. Adv Drug Deliv Rev 2010 (in press) 60. Maeda H, Sawa T, Konno T: Mechanism of tumor-targeted delivery of macromolecular drugs, including the EPR effect in solid tumor and clinical overview of the prototype polymeric drug SMANCS. J Control Release 74:47-61, 2001 61. Shvedova AA, Kagan VE: The role of nanotoxicology in realizing the ’helping without harm’ paradigm of nanomedicine: Lessons from studies of pulmonary effects of single-walled carbon nanotubes. J Intern Med 267:106-118, 2010 62. Song Y, Li X, Du X: Exposure to nanoparticles is related to pleural effusion, pulmonary fibrosis and granuloma. Eur Respir J 34:559-567, 2009 63. Ryman-Rasmussen JP, Cesta MF, Brody AR, et al: Inhaled carbon nanotubes reach the subpleural tissue in mice. Nat Nanotechnol 4:747-751, 2009 64. Ryman-Rasmussen JP, Tewksbury EW, Moss OR, et al: Inhaled multiwalled carbon nanotubes potentiate airway fibrosis in murine allergic asthma. Am J Respir Cell Mol Biol 40:349-358, 2009 65. Sakamoto Y, Nakae D, Fukumori N, et al: Induction of mesothelioma by a single intrascrotal administration of multi-wall carbon nanotube in intact male Fischer 344 rats. J Toxicol Sci 34:65-76, 2009

Nanotechnology and cancer therapy 66. Zhang D, Deng X, Ji Z, et al: Long-term hepatotoxicity of polyethyleneglycol functionalized multi-walled carbon nanotubes in mice. Nanotechnology 21:175101, 2010 67. Singh N, Manshian B, Jenkins GJ, et al: NanoGenotoxicology: The DNA damaging potential of engineered nanomaterials. Biomaterials 30: 3891-3914, 2009

87 68. Choi HS, Liu W, Misra P, et al: Renal clearance of quantum dots. Nat Biotechnol 25:1165-1170, 2007 69. Burns AA, Vider J, Ow H, et al: Fluorescent silica nanoparticles with efficient urinary excretion for nanomedicine. Nano Lett 9:442-448, 2009 70. Ruggiero A, Villa CH, Bander E, et al: Paradoxical glomerular filtration of carbon nanotubes. Proc Natl Acad Sci U S A 107:12369-12374, 2010

The Promise and Pitfalls of Positron Emission Tomography and Single-Photon Emission Computed Tomography Molecular Imaging–Guided Radiation Therapy Richard L. Wahl, MD,*,† Joseph M. Herman, MD, MSc,‡ and Eric Ford, PhD‡ External beam radiation therapy procedures have, until recently, been planned almost exclusively using anatomic imaging methods. Molecular imaging using hybrid positron emission tomography (PET)/computed tomography scanning or single-photon emission computed tomography (SPECT) imaging has provided new insights into the precise location of tumors (staging) and the extent and character of the biologically active tumor volume (BTV) and has provided differential response information during and after therapy. In addition to the commonly used radiotracer 18F-fluoro- 2-deoxyD-glucose (FDG), additional radiopharmaceuticals are being explored to image major physiological processes as well as tumor biological properties, such as hypoxia, proliferation, amino acid accumulation, apoptosis, and receptor expression, providing the potential to target or boost the radiation dose to a biologically relevant region within a tumor, such as the most hypoxic or most proliferative area. Imaging using SPECT agents has furthered the possibility of limiting dose to functional normal tissues. PET can also portray the distribution of particle therapy by displaying activated species in situ. With both PET and SPECT imaging, fundamental physical issues of limited spatial resolution relative to the biological process, partial volume effects for quantification of small volumes, image misregistration, motion, and edge delineation must be carefully considered and can differ by agent or the method applied. Molecular imaging– guided radiation therapy (MIGRT) is a rapidly evolving and promising area of investigation and clinical translation. As MIGRT evolves, evidence must continue to be gathered to support improved clinical outcomes using MIGRT versus purely anatomic approaches. Semin Radiat Oncol 21:88-100 © 2011 Published by Elsevier Inc.

T

he rapid evolution of precision radiation therapy now permits the delivery of high-dose rates to tumors located near normal structures with exquisitely sculpted dose sparing of the normal tissues. Currently, anatomic imaging is essential and the primary means applied to guide precision radiation therapy planning and serves as an important tool for providing attenuation maps to facilitate accurate dose

*Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD. †Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD. ‡Department of Molecular Radiation Oncology, Johns Hopkins University School of Medicine, Baltimore, MD. Address reprint requests to Richard L. Wahl, MD, Division of Nuclear Medicine, 601 North Caroline Street, Room 3223, JHOC, Baltimore, MD 21287-0817. E-mail: [email protected]

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1053-4296/11/$-see front matter © 2011 Published by Elsevier Inc. doi:10.1016/j.semradonc.2010.11.004

calculation.1 Anatomic imaging is, however, limited in sensitivity for defining tumor extent, capacity to probe the selective biology of tumors and normal tissues, and potential for monitoring response of tumors and normal tissues to treatment. The concept of a “biological tumor volume”(BTV) was introduced by Ling et al2 over a decade ago to describe radiation treatment planning, which included imaging data specific to tumor biology beyond that provided by anatomy alone. In this review, the term “molecular imaging– guided radiation therapy” (MIGRT) is used to describe imaging processes from which the signal is expected to map molecular distributions and their surrogates and can be used to guide external beam therapy plans. Although the discussion focuses on molecular imaging, it is understood that these images operate in concert with anatomic imaging to guide precision radiation therapy.

Promise and pitfalls of PET and SPECT MIGRT Although 18F-fluoro- 2-deoxyD-glucose (FDG) positron emission tomography (PET) has had a reasonably long history of use as an adjunct to anatomic imaging for radiation treatment planning, a large number of interesting (and some targeted) agents for both PET and single-photon emission computed tomography (SPECT) have been introduced or are under investigation to probe other aspects of tumor biology and normal tissue physiology. Internally administered radiopharmaceuticals that target cancer after systemic administration include, among others, radiolabeled monoclonal antibodies, such as anti-CD20 reagents; radiopeptides binding to the somatostatin receptors; 131I-meta-iodo-benzylguanidine (MIBG); bone-targeting tracers such as 89Sr (strontium 89) or 153Sm-EDTMP (samavium 153-ethylene dlamine tetramethylene phosphonate); and 131I and 90Y microspheres for the treatment of hepatic malignancies. These therapeutic radiopharmaceuticals often require imaging (often SPECT) before treatment is performed and treatment may include imageguided quantitative dosimetry to personalize radiation dose to normal tissues. Recent reviews further describe the role of targeted radionuclide therapies and the associated role of imaging in their guidance.3-7

Overview of PET and of SPECT PET is an imaging method in which typically short-lived positron emitters, commonly produced in a medical cyclotron and usually attached to a targeting molecule, are used to qualitatively and quantitatively assess the biodistribution of the targeting molecule in vivo. Accurate and biologically relevant imaging depends on the delivery of the imaging agent to the relevant biological process. When the positron is emitted; it travels a short distance in tissue (about 1 mm for an 18F-associated positron) and then loses sufficient energy to interact with an electron, transiently producing positronium. This material gives off two 511-keV photons traveling at essentially 180° from one another as well as a neutrino. The 511-keV photons can be imaged using a specialized PET camera. Modern PET scanners often include a computed tomography (CT) scanner as a part of the instrument. Thus, patients can have a PET and a CT scan in a single imaging session. Advantages of PET are its high sensitivity because of the use of precise timing information to determine if photons interact with detector materials simultaneously (coincidently or nearly so, as is the case for positron annihilation) as well as the opportunity to accurately correct radiation measurements for body thickness (attenuation). The CT scan from the PET/CT produces a data source for an attenuation map. PET images quite accurately provide quantitative radioactivity measurements in vivo. A wide range of PET radiotracers have been developed to examine biologically relevant processes. The most common PET radioisotopes are 18F (109-minute half-life) and 11C (20minute half-life) with growing applications of 68Ga (1-hour half life). These short half-lives mean that most PET tracer studies have relatively favorable radiation absorbed dose profiles. These tracers are usually best suited for assessing rather

89 rapid biological processes. Longer-lived positron emitters like I-124 have more limited applications because of their unfavorable dosimetric properties. SPECT is a method of performing single-photon imaging (a class of imaging techniques in use for over 50 years) and typically involves rotating one or more gamma camera heads about the patient and acquiring, for example, 64 to 128 projection views from varying angles. This allows reconstruction of the 3D distribution of radioactivity in vivo for single-photon emitters. Common single-photon emitters (so named as they emit at least 1 gamma ray per decay which can travel in any direction) include 99mTc, 123I, 111In, and 131I. Determining the location of the emitted photons in single-photon imaging is typically achieved through the use of a lead collimator with 1 or many holes that provide spatial information. When integrated with CT systems, SPECT can measure radioactivity in vivo with moderate quantitative accuracy, but in general PET is more quantitatively accurate in determining the distribution of radioactivity in vivo. SPECT radiotracers typically have longer half-lives than PET tracers, but because they often release less energy, often nonparticulate, per decay their dosimetry can be satisfactory. They can be used to study slower biological processes (eg, localization of monoclonal antibodies to tumors which can take several days). Both PET/CT and SPECT/CT methods allow export of Digital Imaging and Communications in Medicine (DICOM) image datasets for CT and for the nuclear images, which can be imported into radiation therapy planning systems. A critical point for both imaging methods is that routine clinical positioning for optimal patient comfort in a diagnostic study is often not identical to the positioning used in delivery of radiation therapy. It is often highly desirable to perform the studies using a flat table, lasers, and similar imaging equipment. Detailed reviews8,9 describing these methods have been published. PET/CT simulators are an ideal way to minimize differences in patient setup, but these are only available at select centers. Alternatively, one can immobilize the patient in a cradle or mold and use the same device for the separate PET and CT scans. We have recently reported on many of the technical issues associated with using FDG PET/CT to guide IMRT.10

Key Biological Processes in Tumors and Normal Tissues Cancers differ substantially from normal tissues. Their different shapes, location, and densities allow CT imaging to detect and define many cancers. Despite this, many small tumors and/or subcentimeters or micrometastases are not detected on a CT scan. Nodal metastases are difficult to detect, whereas many enlarged nodes do not contain cancer. If surgical or radiation therapy have distorted the normal anatomy, tumor detection and delineation is quite difficult with a CT scan.

90 Cancers have a variety of characteristics that are associated with survival advantages. These characteristics are sometimes called hallmarks of cancer. Each of these hallmarks is a potential target for tumor imaging. Several of the molecular imaging agents available for cancer imaging as related to radiation oncology have been reviewed fairly recently.11

Glucose Use From an imaging standpoint, the increased glucose use seen in cancers is often profound. Increased glucose use is a typical alteration of most cancers and may in part be related to the growth of cancers at the extremes of their blood supply that results in hypoxia. Hypoxia upregulates the GLUT 1 glucose transporter and anaerobic glycolysis, which can provide energy to cells under conditions of profound hypoxia. Cancers also have accelerated glucose use under conditions of more normal oxygenation, and this aerobic glycolysis is recognized as the Warburg effect. The Pasteur effect is when glycolysis increases even more under conditions of hypoxia. The precise mechanisms of this process remain under study but are in part related to hypoxia-inducible factor (HIF)-1␣, in part to transforming oncogenes, as well as to a variety of mutations in the glucose utilization pathway. Most studies of tumors by autoradiography have shown FDG to be distributed throughout most of the viable tumor elements. Although most of the FDG signal is from viable cancer cells, there is a component of uptake in viable infiltrating immune cells, such as white cells of various types, which may cause a false-positive PET signal in the absence of tumor. Although FDG is probably the tracer best suited to detecting tumor extent in the vast majority of cancers, not all cancers are FDG avid, including many prostate cancers (especially the non– castrate-resistant form), hepatomas, and renal adenocarcinomas. Mixed results have been reported regarding FDG avidity for pancreatic cancers and mucinous histology colon cancers. Some tumors are not well seen because of surrounding normal tissues that accumulate FDG (eg, brain tumors). The excreted FDG in the urine and normal accumulation in the stomach, heart, and gut can also confound planning. Combined PET/CT with FDG has shown superior performance to CT scanning alone in common cancers, such as colorectal cancer, lymphoma, and lung. The reader is referred to several textbooks on the subject of the diagnostic accuracy of FDG PET/CT (eg, Principles and Practice of PET) and other reviews focused on radiation therapy.9,12 If the goal of radiation therapy is cure, then knowing the extent of tumor is critically important. Many FDG-avid tumors are staged by FDG PET/CT (a procedure routinely reimbursed by Medicare in the United States). PET/CT is commonly used for staging lung cancer. With PET/CT, about 15% to 20% of patients with cancers being considered for therapy with local radiation therapy were found to have tumors outside of the expected radiation field, either distant metastases or second primary lesions.13,14 Treatment planning of many types of cancer has been explored with FDG PET. Several recent reviews are available on

R.L. Wahl, J.M. Herman, and E. Ford specific cancers and the use of PET/CT in planning treatment.10,11,15,16 However, in the key areas of tumor imaging as related to radiation oncology, there is little doubt that FDG is the dominant molecular imaging tracer. There is evidence that PET-based as opposed to CT-based plans expose normal lung to less radiation and higher probabilities of tumor control.17,18 This dose savings to normal tissues is most apparent when mediastinal lymph nodes are excluded from the field of radiation if they are “negative” on PET.17 Patients planned with PET and treated with radiation appear to have superior outcomes to those who were planned without PET (probably in part by eliminating the attempt to cure patients whose disease extends out of therapeutic field at presentation).19 Head and neck cancer has been extensively studied using FDG PET/CT. Early studies showed that FDG PET was more accurate than either CT scans or MRI in detecting recurrent tumors after chemotherapy and irradiation, where anatomic alterations may be confounding. A variety of studies have examined changes in field shapes and sizes using FDG and other tracers in head and neck cancer.20 As an example, in a study of 76 patients with head and neck cancer, PET/CT scanning led to a TNM classification alteration in 34% (12/ 35), a change in the radiotherapy planning technique and/or dose in 29% (10/35), and altered assessment of treatment response in 43% (13/30). A complete metabolic response on PET was predictive of overall survival (P ⫽ .037).21 PET with FDG after treatment in head and neck cancer carries prognostic significance. Patients with higher FDG uptake after radiation treatment have inferior outcomes compared with patients with negative scans.22 The general area of PET and radiation therapy in head and neck cancer has been reviewed recently.23 PET and PET/CT have been applied in cervical cancer as well. Grigsby24 has shown that FDG PET is more sensitive than CT in detecting lymph node metastases of cervical cancer. Furthermore, positive PET scans in lymph nodes in the para-aortic area of cervical cancer patients are associated with worse outcomes than patients with negative para-aortic area lymph nodes. Similarly, positive uptake in the primary tumor after therapy has been associated with a less favorable outcome.24 Such data suggest 2 possibilities: (1) the PET findings are clear markers of a poor prognosis and this cannot be altered with current therapies because the underlying biology is so aggressive (ie, it is simply prognostic) or (2) the PET findings indicate where additional treatment should be delivered and can predict if it will be successful. Clearly, this cannot be known until studies are performed to more intensively treat those patients with high-risk phenotypes of cervical cancer, identifying them before or after therapy. For example, positive para-aortic nodes outside of the field of planned therapy should logically be included in the field of therapy (treatment field expanded). Residual FDG uptake in a tumor presumably will need additional therapy as well. PET/CT scanning can be used to guide the delivery of brachytherapy to primary cervical cancers and para-aortic nodes.25,26 The timing of response in cervical cancer to radi-

Promise and pitfalls of PET and SPECT MIGRT

91

Figure 1 CT-based (left image and black contour) gross tumor volume delineation and FDG PET/CT biological tumor volume (right and white contour). Overlaid isodose distributions are from the CT-based target definition. Potential for booing the most FDG avid tumor region exists.

ation therapy may also be predictive of outcome.27 The role of FDG PET in cervical cancer has recently been reviewed by Grigsby. It is important to note that in the posttreatment period, PET can also be useful for surveillance and PET scans performed 3 months after the completion of treatment have clear prognostic value.24 PET/CT scanning has been applied to planning a variety of other FDG-avid cancers. Reports in pancreatic, anal, thymic cancers, and myeloma are but a few of the examples that generally show changes in management by identifying additional lesions and by altering the size of the gross tumor volume (GTV) because of the biological information provided by PET.28-31 Recently, dose reduction in anatomically enlarged but FDG PET–negative lymph nodes of anal cancer has been reported to be a promising approach.32 It must be realized, however, that neither PET nor SPECT can detect microscopic tumor involvement. Clearly, FDG PET is the main MIGRT approach at the present time, but other approaches should be considered. Although not all the following have been applied yet, it is important to realize the following PET and SPECT agents have variable potential to guide external beam therapies to the relevant areas. An example of the biological tumor volume (BTV) from FDG PET and the GTV from CT scanning is shown in Figure 1 for a patient with pancreatic cancer. These volumes are not identical, and, in this case, the PET volume of tumor is smaller than that of CT scanning.

Flow, Amino Acid Transport, and Membrane Transport The SPECT tracers 201Tl and 67Ga-citrate have been used to detect tumors of a variety of types.33 Both agents have rather unfavorable imaging and dosimetry characteristics and are being used less often now that FDG PET is available. None-

theless, these single-photon emitters have been able to identify viable lymphoma; brain tumors; and, in some instances, sarcomas. Their mechanisms of uptake differ from FDG. 201Tl uptake is flow dependent and also depends on intact membrane energetics. In brain tumors, 201Tl uptake is correlated with bloodbrain barrier disruption and may underestimate tumor size versus amino acid tracers, which can also be used for 34 67 SPECT. Ga uptake in tumors is, in part, related to its behaving as a structural analog to transfer and as an entry to tumor cells via the transferrin receptor. Amino acid transport and protein synthesis are often increased in cancers. Several amino acid– based PET tracers have been used in PET imaging. The greatest advantages for this type of agent can be seen in non–FDG-avid tumors or in situations like brain tumors in which the normal FDG background activity is too extensive to allow clear distinction of the tumor from normal brain. Although 11C-L-methionine has been used the most, 18F-labeled amino acids or nonnatural amino acids, such as 18F-anti FACBC have shown promise, although the latter displays both wash in and wash out from tumor foci, such as in prostate cancer. Amino acid tracers, such as 11C-methionine, 18F-fluroethyltyrosine, and the SPECT agent 123I-alpha methyl tyrosine, have been applied to plan treatment of patients with brain tumors. Radiation treatment plans based on amino acid accumulation in brain tumors can show more extensive tumor than the simple region of blood brain-barrier enhancement and certainly disparate patterns and discrepancies between GTV and BTV.35,36

Membrane Synthesis and Aerobic Energy Production Growing tumors require components for membrane synthesis, and tumors that are not hypoxic may have more aerobic

92 energy sources than glycolytic ones. Although our understanding of the exact mechanisms of uptake remains in evolution, it is clear that radiotracers, such as 11C-acetate, 11Ccholine, and 18F-fluorocholine, can localize to non–FDG-avid cancers, like prostate cancer, and to a variety of other types of cancer, such as hepatocellular carcinoma (for acetate), that may not be FDG avid. Choline is transported into cancer cells via the choline transporters and appears to be phosphorylated by choline kinase. With time, choline is increasingly incorporated into the cell membrane and cell membrane precursors. Choline, both 11C- and 18F-labeled analogs (such as fluorcholine and fluoro ethyl choline), have been used in prostate cancer imaging as well as in imaging other cancers. These methods appear to generally be more sensitive for prostate cancer, especially the better differentiated prostate cancers, than FDG PET. Dosimetry estimates have been calculated using choline PET imaging, which have modeled the influence of a localized boost of radiation dose to choline-avid subregions within the prostate gland. Although this specific application may not have yet shown clear clinical benefit, it shows the potential for MIGRT approaches.37,38 Acetate can be used by cancers for oxidative energy production as well as a building block for membrane synthesis. 11C-acetate has been shown to be capable of imaging hepatomas, especially those that are non–FDG avid. Prostate cancer and some head and neck cancers can also be imaged with this tracer. Uptake of the radiotracer appears to be governed, in part, by levels of the enzyme fatty acid synthase. There has been relatively little work evaluating 11C-acetate for radiation therapy planning. In head and neck cancer, direct comparisons with FDG PET showed the standardized uptake value (SUV) of 11C-acetate avid tumors and nodes to be lower than their SUV for FDG. Tumor volumes estimated from PET were, however, larger for acetate than for FDG. The data on use of 11C-acetate for planning radiation therapy are very limited, but detecting more lesions with acetate than FDG suggests the potential for beneficial modification of treatment plans although much more study is needed.39 An example of a positive 11C-acetate scan in a patient with recurrent prostate cancer and a negative FDG PET scan are shown in Figure 2.

Proliferation Most, although not all, cancers have increased rates of proliferation. Cancers can grow as long as the rate of cell death is lower than the rate of cell proliferation. Thus, some cancers will have high proliferative rates, and others will have lower rates. In general, cancers with high proliferative rates, if left untreated, are the most lethal and have the worst prognosis. Some therapeutic agents, including radiation and chemotherapy, are more effective in rapidly proliferating than in more quiescent tumors. The measurement of proliferative rates is a key goal of cancer imaging and could have considerable potential for altering radiation therapy treatment plans. Rapidly and slowly proliferating tumors could be identified, responding tumors could be assessed, and tumor re-

R.L. Wahl, J.M. Herman, and E. Ford

Figure 2 Prostate cancer imaging with C11 acetate (top panel) and FDG (bottom panel) (JHU series). Note that the C11 acetate images show intense uptake in a lymph node, which appears normal by FDG PET. This node was clinically most consistent with recurrent prostate cancer.

growth could potentially be measured. Such information could change the approach to the therapy of a specific cancer. It is possible that radiation treatment–induced changes in cell proliferation may occur more rapidly in the presence of effective cancer therapy than changes in more general tumor viability markers, such as FDG, although this remains under study. Several attempts have been made to study cell proliferation with imaging. At least 2 forms of 11C-thymidine have been applied, but the 20-minute half-life and considerable metabolism are limitations to its use. There can be radioactive CO2 released, which can contaminate the tumor signal. Although 11C-thymidine showed proof of concept, other tracers, most notably 3=-deoxy-3=-fluorothymidine (18F-fluorothymidine [FLT]), which is a substrate for thymidine kinase, have proven more practicable for implementation into clinical imaging trials. In research applications, it is probable that FLT is second

Promise and pitfalls of PET and SPECT MIGRT only to FDG in use. However, FLT has some limitations. As a substrate for thymidine kinase, it is able to image only the thymidine “salvage” pathway. If there are high endogenous levels of thymidine present, it is possible that imaging this pathway may be influenced adversely by endogenous thymidine levels. In general, although not invariably, the uptake of FLT in tissue is in the form of FLT phosphate, and the FLT uptake is generally related to the proliferative index measured by other methods. Proliferation is not unique to tumors, and rapid proliferation can be seen in reactive lymph nodes (with activated lymphocytes in germinal centers) as well as in tumors. FLT uptake in lymph nodes (eg, head and neck cancer) not uncommonly is in reactive as opposed to malignoid lymph nodes.40 Absolute FLT uptake in tumors is often considerably lower than that of FDG so that maximum tumor SUV levels are often about 50% or less than those of FDG. There is often substantial FLT uptake in normal tissues, such as bone marrow, liver, and bowel. Smaller tumors may not be as detectable with FLT as they are with FDG. With a smaller starting SUV, the absolute decline in SUV with therapy is expected to be lower than with FDG. However, this agent is in a variety of clinical trials and has recently been assessed in defining the GTV in esophageal cancer. A much lower SUV peak cutoff is required to accurately detect tumor sizes with FLT than with FDG (1.4 vs 2.5). FLT will need40 more study and certainly may have its greatest value in assessing early treatment response although it is by no means specific for cancer.41 Autoradiographic studies by our group suggest the signal from FLT is less widely distributed within the tumor than that of FDG, which is consistent with imaging a subset of the BTV as defined by FDG. Another potential role of FLT would be to measure tumor repopulation rates.42 Planning based on FLT PET remains an area of opportunity. An example of a FLT PET study and a FDG PET study in a patient with lung cancer is shown in Figure 3. There is a much more intense visualization of the tumor on the FDG than on the FLT PET study.

93

Figure 3 FDG (lower panel) versus FLT (upper) images of lung cancer. Images in untreated lung cancer demonstrate intense FDG uptake in the primary lesion in the left lower lobe with definite but lower FLT uptake in the primary lesion (Courtesy of Dr M. Chaudhry, Johns Hopkins University, Baltimore, MD).

Hypoxia One of the most exciting areas in MIGRT is the use of agents for imaging hypoxia to define hypoxic tumors that may benefit from more aggressive therapy. The aggressive therapies could include hypofractionation, radiosensitizers, dose escalation, and an alternative form of radiation to overcome radioresistance. Several radiotracers have been developed that have characteristics suitable for imaging hypoxia and are considered hypoxia imaging agents. Interest in this area has been present for many years; although with greater availability of PET/CT scanners, some of the possibilities are now being tested and are testable in both single-center and multicenter trials.43,44 There is little doubt that hypoxia is associated with both resistance to chemotherapy and standard radiation therapy. The oxygen effect is well known to readers of this journal. However, an underlying challenge in hypoxia imaging is

whether the hypoxia is diffusely distributed through the tumor or more focal. Focal hypoxia may result in radiation resistance but may be difficult to resolve by PET imaging in which the resolutions are in the 5-mm to 1-cm range. A variety of radiotracers has been used in attempts to image hypoxia. 18F-fluromisonidazole (FMISO) has been the most extensively evaluated agent. This agent is structurally related to the nonradioactive hypoxia targeting agent, pimonidazole. Both FMISO and pimonidazole are nitroimidazoles that bind to very hypoxic regions. FMISO has a relatively slow blood clearance and develops only relatively low target/ background ratios in hypoxic regions. A number of challenges exist with this agent. The relationship between hypoxia and pimonidazole uptake is not always perfect in vivo, and the scale of hypoxia in vivo may be much smaller in some tumors than others. This suggests that hypoxia, at least in

94 small areas of tumors, will be difficult to image with FMISO agents45 and shows a general challenge with molecular imaging agents; the scale of the imaging (resolution) may not be optimally matched to the scale of the biological process. Even if the biological distribution could be imaged at submillimeter resolution, therapeutic photon radiation could not be delivered with this precision because of the fundamental limitations of the physics of radiation transport at high energies. Another concern with hypoxia imaging is that at least 1 report has suggested that hypoxia is inconsistent in tumors and that there is variability of the hypoxic signal using FMISO from day to day in test and retest studies. Some of this variability may be artifactual in that dichotomizing tumors as hypoxic or not based on a cutoff tumor/muscle level of 1.4 or 1.6 to 1 artificially places a cut line on an inherently continuous process. In test-retest studies in vivo, there are challenges with imaging and coregistering precisely the same area of tumor. This may contribute to apparent variability in tracer uptake over time, which may simply be caused by errors in registration. In all probability, tumor hypoxia is not fully stable.46 This means that challenges are likely to be present if intratumoral “dose painting” is applied over time. If 18F-MISO is used as the basis of the therapeutic plan, its distribution may change over time. This may be beneficial, but it adds complexity to the treatment.46 The temporal variability may be less of an issue if diseases can be treated with hypofractionated regimens in which high doses of radiation are delivered over fewer sessions. With the ability to image more than 1 aspect of cancer biology, the possibility of targeting the entire tumor (perhaps with FDG and anatomic imaging) and the hypoxic subregions (perhaps with FMISO) may be a useful approach to alter biological dose delivery within tumors. Clearly, the spatial resolution of PET may simply be insufficient to allow for high-resolution delivery of the tumor dose. There have been several reports showing that the FDG signal and the FMISO signal in head and neck cancers are not identical. There appears to be support for the concept that high FMISO activity in head and neck cancers is associated with a more aggressive phenotype and a less favorable outcome.47 The possibility to target such hypoxic areas within a tumor with an additional radiation dose is thus biologically rational.48 A logical approach to dealing with hypoxic regions identified by imaging with FMISO would be to increase the radiation dose to the area of greatest hypoxia. One group attempted to study the feasibility of boosting the radiation dose to areas of FMISO avidity within FDG-avid tumor volumes. Regions of elevated 18F-FMISO uptake within the FDG PET/CT GTV were targeted for an IMRT boost. Additional targets and/or normal structures were contoured or transferred to treatment planning to generate18F-FMISO PET/CTguided IMRT plans. The heterogeneous distribution of 18FFMISO within the GTV showed variable levels of hypoxia within the tumor. It was feasible in planning to dose escalate the GTV (hyopoxic region) to 84 Gy in all 10 patients and in

R.L. Wahl, J.M. Herman, and E. Ford 1 patient to 105 Gy without exceeding the normal tissue tolerance.49 A recent article evaluated 15 patients with locally advanced HNSCC treated with concomitant chemoradiotherapy, who were prospectively imaged with 18F-FDG PET (18Ffluoromisonidazole) and MRI. T1, T2, dynamic-enhanced, and diffusion-weighted sequences were performed before, during, and after radiotherapy. In 7 patients, disease recurred. Disease-free survival correlated negatively with the maximum tissue-to-blood 18F-fluoromisonidazole ratio (T/ B[max]) on the baseline 18F-fluoromisonidazole scan (P ⫽ .04), with the size of the initial hypoxic volume (P ⫽ .04), and with T/B(max) on the 18F-fluoromisonidazole scan during treatment (P ⫽ .02). All locoregional recurrences were within the 18F-FDG–avid regions on the baseline 18F-FDG PET; 3 recurrences mapped outside the hypoxic volume on the baseline 18F-fluoromisonidazole PET. Lesions that recurred had lower apparent diffusion coefficients and faster contrast enhancement than nonrecurrent lesions.50 There are other hypoxia-imaging agents under study. Fluoroazomycin arabinoside has been evaluated to some extent and appears to have higher target/background ratios than seen with FMISO. Although the agent is promising, it has only had limited evaluation.51 Similarly, hypoxia agents like 18F-EF5 (Fig. 4) are under study and may, if localization is sufficiently rapid, prove useful for mapping hypoxia.52 The ease of synthesis of such agents varies, and compounds that are difficult to synthesize may be impractical to use widely in clinical trials. Another promising agent that was developed to image hypoxia is copper (diacetyl-bis (N4-methylthiosemi carbazone)) [Cu ATSM]. Copper radioisotopes offer several possible advantages, including the availability of several radioactive copper isotopes with varying half-lives, including 64Cu (12.7-hour half life), which may make it suitable for test/ retest or multitracer studies.53 60Cu and 62Cu have been used in imaging. Chao et al53 showed the feasibility of planning head and neck cancer radiation therapy using Cu ATSM several years ago and showed different radiation distributions than anatomy based plans.54 The potential to identify hypoxic tumors and modify changes in tumor oxygenation with hyperthermia have also been reported in preclinical models using Cu ATSM (“flow and hypoxia”) and copper per-pyruvaldehyde-bis (N4methyl thio semicarbazone) [Cu PTSM] (“flow”) normalizations.55 Because the Cu agents may have some flow dependence, additional clinical verification studies are needed. However, the images obtained with Cu ATSM agents have been of very high quality, and this agent appears to allow for easier separation of tumor from background activity than has been seen with F-18 fluoromisonidazole. Which will be more predictive of outcomes is uncertain.

Receptors and Tumor Antigens SPECT agents specific for cancers are limited. One such agent is 111In-Octreoscan (Indium-III-Pentetreotide, Mallinckrudt Medical, St. Louis, MO) (for neuroendocrine tumors). An-

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Figure 4 FDG (left panel) versus EF5 (right panel) images of cancer in the neck. Note that the hypoxic volume on the EF5 scan is considerably smaller than the glycolytic volume identified on FDG PET. (Courtesy of Professor Heikki Minn, Turku U PET Center, Turku, Finland.)

other is 111In capromab pendetide (ProstaScint, EUSA Pharma, Oxford, UK). The latter has been used in some settings to attempt to boost the dose to prostate cancers.56 This agent suffers from relatively poor accuracy for the detection of prostate cancer. A 68Ga-labeled PET agent (68Ga-DOTA-toc) for imaging somatostatin receptor–positive neuroendocrine tumors has been developed and appears to be superior to 111In-pentetreotide. This agent has been applied for planning the radiation dose to meningiomas, which can be somatostatin receptor positive. Integration of the DOTA-Tyr3-octreotide (DOTATOC) data led to additional information concerning tumor extension in 17 of 26 patients (65%). Overall, the GTV MRI/CT scanning was larger than the GTV PET in 10 patients (38%), smaller in 13 patients (50%), and almost the same in 3 patients (12%). Most of the adaptations were performed in close vicinity to bony skull base structures or after complex surgery. The median GTV based on MRI was 18.1 mL; the median GTV based on PET was 25.3 mL.57

Normal-Tissue Assessments The knowledge of the location of normal tissues is typically provided by anatomic imaging, but the functional status of normal tissues can be best depicted by functional imaging. As an example, the path of therapeutic beams through the lungs to treat intrathoracic tumors should ideally spare normal lung tissue. Total sparing of normal lung from radiation may be impossible. However, the least functional areas may be selected to optimize beam entry/exits paths for external beam treatment planning. Irradiation of already damaged lung

would not be expected to cause deteriorations in function to as great an extent as seen when normal lung tissue is irradiated. This determination can be made for example using SPECT imaging with 99mTc- (99mTc-MAA [macro aggregated albumin]) perfusion imaging. Such approaches are increasingly possible using more widely available SPECT/CT technology.58 Similarly, agents like 99mTc-HIDA (hepatic iminodiacetic acid) and 99mTc-sulfur colloid depict function of the liver cells or the hepatic reticulo endothelial cells (immobilized macrophages). Other agents with 99mT-DMSA and 99mTc-MAG3 show renal function. Such agents have the potential to monitor normal tissue function in vivo but have not been extensively explored for RT planning. Myocardial perfusion and function can be measured using both single-photon and PET tracers if the heart is to be irradiated.

Practical Issues For MIGRT to be successful, identification and localization of the relevant tumor characteristics is essential. For FDG PET, the goal would typically be identification of the entire tumor with a good match between the size of the BTV on FDG PET with the true tumor size at histology. For other processes, an accurate match between the extent of the biological process, such as the true hypoxic fraction and the hypoxic volume defined by imaging would be highly desirable. A large literature has been developed addressing the issue of determining lesion volume from molecular imaging. In early studies, CT scan volumes were compared with FDG PET volumes in untreated lung cancers;59 the tumor size on

R.L. Wahl, J.M. Herman, and E. Ford

96 PET best matched CT tumor volumes when a region of interest was drawn that was 3␴ above the mean of the normal lung FDG SUV. This approach worked well in untreated tumors (r ⫽ .98) except when there was collapse of the lung distal to the tumor, enlarging the CT volume or in treated tumors. In such instances, the CT-defined volume was, expectedly, higher than the PET-defined volume. In other studies of lung cancer, it was clearly shown that a single SUV cutoff for FDG PET did not result in a reliable estimation of tumor volume relative to the tumor volume from CT scanning. Both SUV cutoffs of 20% and 40% of the maximum SUV value resulted in underestimation of the tumor size versus CT scanning.60 Considerable efforts have been undertaken to precisely match the SUV cutoff for FDG PET (as a percentage of the max single pixel SUV) with tumor size determined at pathology. “Intelligent contouring” in which anatomic and functional cutoff data are used has been shown to be quite reproducible by 1 group.61 A simple numeric SUV cutoff is unlikely to be adequate to define tumor edges because the normal SUV for FDG PET can vary based on patient body habitus. The normal tissue SUV is higher in obese patients than nonobese ones and in specific tumor types. Daisne and Beriwal62 examined PET versus pathology GTV estimates and found a very good correlation at specific threshold settings. It is improbable that PET will fully replace CT scanning or MRI in defining GTV. Rather, an approach in which the anatomic and functional data are combined, with an overlap of the varying volumes (which are substantially correlated), with possible intensification of dose in the BTV of relevance, will be an evolving approach to delineation.62 Varying characteristics must apply to each radiotracer to optimize thresholding, and it is possible that techniques like kinetic modeling may be useful with some tracers or even FDG.63 Also worth noting is that different tracers will likely require different approaches to accurately extract tumor boundaries. FDG and FLT, for example, require different cutoff values to estimate tumor size properly. This will clearly differ based on tumor size, tumor SUV, and normal tissue SUV (tumor/background uptake ratios) with a lower threshold for FLT- than FDG-based volume estimates.41 A variety of technical issues are present in treatment planning of lung cancer with any tracer. Registration between PET and CT scanning may not be perfect because of respiratory motion. The quantitation of exact SUV in tumors may be altered, and inconsistencies because of motion can affect attenuation maps.64 The size of lesions on PET may be larger than on CT scans because of respiratory effects of blurring during the relatively long duration PET acquisition, which entails multiple respiratory cycles, in contrast to a breathhold CT scan. Respiratory gating has been applied to help deal with such inconsistencies. If respiratory gating is used with PET, additional radiation dose may occur from additional CT scans required for attenuation correction and additional time for acquisition of images. How to best determine tumor volume from PET remains in evaluation, but real mismatches in lesion size can occur because of the movement of tumors and organs during respiration, which is most important for lesions in the upper abdomen and thorax. It is

notable that misregistrations between CT locations of lesions in the thorax on CT scans versus PET are greatest in the lower lung fields but can be 6 mm to 1 cm or more depending on the location.

Pitfalls With FDG and Other Tracers Although our radiotracers have exquisite specificity for a biological process, the biological process imaged may not always be cancer. Neither FDG nor FLT are tumor specific. Nonspecific uptake has been seen with virtually all PET tracers.40 This may be a particular problem in the post–radiation therapy setting where one observes the infiltration of FDGavid macrophages.65 Alternative tracers may represent an advantage in this regard. It is also well known that active infections, reactive lymph nodes, tuberculosis, sarcoidosis, and active inflammation can accumulate FDG. Reactive, nontumor involved, lymph nodes can also have intense accumulation of FLT.40 In addition, there can be transient flares in the extent of tracer accumulation after radiation with increases and decreases in FDG because of the recovery process.

Inflammation We have taken advantage of the increased FDG uptake in inflammation after surgery to test plans of breast cancer radiation therapy boosts based on the inflammation remaining at the resection site post treatment. Such approaches may be helpful but draw from the non–tumor-specific uptake of the tracer.66 However, it is critically important that the normal uptake pattern and the nonmalignant uptake patterns of radiotracers be known before a specific region of tracer avidity is treated as a malignant process.

Practical Issues in the Translational Process From Bench to Bedside The table of PET and SPECT imaging agents (Table 1) shows many possibilities for integrating PET and SPECT imaging into radiation therapy planning or monitoring, but many of the agents listed are not Food and Drug Administration approved. Some are under investigation under National Cancer Institute-sponsored Investigational New Drug or individual institution approvals. Others are in use in Europe or Asia/ Oceania but not in the United States. Radiopharmaceuticals have limited risks, but the regulatory structure is complex and treats the agents very similarly to therapeutic agents although such agents are typically given in “microdoses.” The cost of securing regulatory approval of the agents for clinical practice can be substantial although it is typically lower than that for therapeutic drugs. Center for Medicare Services payments in the United States for diagnostic imaging agents are much less than for therapeutics. A major problem lies in integrating advanced imaging into

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Table 1 Molecular Imaging Tools With Potential in MIGRT (Selected Radiopharmaceuticals) Agent

Target

Class

Comments

PET SPECT SPECT PET

PET

Viable tumor volume, inflammation Lower energy, long half-life, poor dosimetry Long half-life, poor dosimetry 20-min half-life of C11 limits availability images many tumor types Applied in brain tumor imaging, especially in Europe Investigational. Promise in prostate cancer

SPECT

Applied in brain tumor imaging

PET PET PET PET PET PET PET PET SPECT PET SPECT PET PET SPECT PET SPECT SPECT PET SPECT

Prostate cancer, hepatomas, etc Prostate cancer, many cancer types Lung and rapid proliferative tumor

F-18 Fluoromisonidazole Cu-62, Cu-64 Co-60 ATSM F-18 EF5 ([18F]fluoroazomycin-arabinoside [FAZA]) I-123 AZA F-18 ML-10 Tc99m annexin V 18-F RGD binding peptides Ga-68 RGD binding peptides Tc99m RGD binding peptides O-15 water Tc99m MIBI* In-111 pentetreotide (Octreoscan-tm)* Ga-68 Dota TOC/TATE/NOC I-123, I-131 MIBG*

Glucose metabolism, viable tumor Perfusion, viability General tumor marker Lymphomas including HD General tumor marker Amino acid transport General tumor marker Amino acid transport General tumor marker Amino acid transport General tumor marker Amino acid transport Membrane synthesis, non–FDG-avid tumors Membrane synthesis, choline metabolism Proliferation tumors Proliferation tumors Hypoxia Hypoxia Hypoxia Hypoxia Hypoxia Apoptosis Apoptosis Neovasculature Neovasculature Neovasculature Blood flow Flow and mitochondrial status Somatostatin receptors Somatostatin receptors NET-2 transport & vesicular storage

I-124 MIBG, C-11 HED 18F FDOPA I-131,* I-123* I-124 In-111 capromab Pendetide (Prostascint)* I-123 PSMA binding ligands F-18 and Ga 68 PSMA binders Tc99m MDP,* HEDP* 18F NaF

NET-2 transport & vesicular storage APUD Iodine transport & retention Iodine transport & retention Intracellular PSMA Extracellular PSMA Extracellular PSMA Bone remodeling Bone remodeling

PET PET SPECT PET SPECT SPECT PET SPECT PET

Tc99m MAA* Tc99m sulfur colloid* Tc99m MAG3,* DMSA* Tc-99m mebrofeninin,* disofenin*

Lung perfusion, IA liver perfusion Liver macrophage function Kidney function and morphology Liver function and excretion

SPECT SPECT SPECT SPECT

18F-FDG* Thallium 201* Gallium 67 citrate* C-11 L-methionine F-18 fluoroethyl tyrosine (FET) F-18 F-anti–ACBC I-123 alpha methyl tyrosine C-11 acetate C-11 choline and F-18 choline analogs C-11 thymidine, fluorothymidine (FLT)

PET

Hypoxic tumors Hypoxic tumors Hypoxic tumors Hypoxic tumors Hypoxic tumors Apoptosis in tumors Apoptosis in tumors ␣ v ␤ 3 integrins ␣ v ␤ 3 integrins ␣ v ␤ 3 integrins Requires cyclotron, 2 minute half life Sarcomas, breast cancer, drug resistance? Neuroendocrine tumors, SSTR2ⴙ Neuroendocrine tumors, SSTR2 Pheo, paragangliomas, neuroblastomas Pheo, paragangliomas, neuroblastoma Brain tumors and neuroendocrine tumors Thyroid cancer, reporter gene Thyroid cancer Prostate ca staging, recurrence Prostate cancer Prostate cancer Bone metastases Bone metastases Delineation of normal and abnormal lung or other tissues if IA delivery Functional/anatomic mapping Functional/anatomic mapping Functional mapping of hepatocytes/ducts

*A Food and Drug Administration–approved radiopharmaceutical in the United States. It is clear that a minority of potentially useful

radiopharmaceuticals are approved by the US Food and Drug Administration. Such agents are in use in other countries and in research settings in the United States. Some PET tracers are United States Pharmacopeia (USP) approved, such as C-11 methionine, NaF, C-11 acetate, and O-15 water, and may be available at some centers. Note that agents labeled “SPECT” can often be imaged with planar gamma camera imaging in addition to SPECT.

radiation therapy and practice. Some of the agents are not patent protected. Several promising agents have very short half-lives (C-11, 20 minutes). The more disease specific the targeting, the smaller the market is. Clearly, many scientifically interesting agents may have a compelling but limited diagnostic utility and may be difficult to translate into practice because the marketplace is small and payments may be low, making commercial risks high. Indeed, except for FDG, which is very widely used, no other PET drug for oncology is in widespread clinical use. NaF, a bone-imaging agent, has the greatest potential because it is generally more accurate than 99mTc-MDP (methylene diphosphonate) and has a projected broad market but as a generic has no clear sponsor. These challenges, plus US Food and Drug Administration regulations on “manufacturing” PET agents, which will go

into effect in late 2011, may make the translation process for new agents to practice a difficult and arduous one. For many of the agents listed in Table 1, no clinical trials of PET in treatment planning have yet been reported. In addition, fee codes in the United States for radiation treatment planning PET studies are lacking. Despite these considerable barriers, the dramatic impact of FDG PET on oncologic imaging and now on radiation therapy planning suggest other MIGRT approaches will emerge and be successful.

Emerging Opportunities The use of PET/CT scans to generate initial therapeutic plans has been evaluated in a variety of tumor types, with lung, head and neck, and brain tumors highlighted in this discus-

R.L. Wahl, J.M. Herman, and E. Ford

98 sion although clearly extending broadly across many types of tumor, including pancreas.67 The use of PET/CT of molecular imaging characteristics to modify the treatment course, either by changing the fields or altering the dose, has been considered by several groups, including a recent review on head and neck carcinoma.68 Similarly, the data in cervical cancer have been promising for identifying high-risk groups who can benefit from response adaptation.24 Recent proposed response assessment criteria, like positron emission response criteria in solid tumor, for PET may add greater order to the complexities of estimating tumor response and be useful for adaptation of response as well.69 The concepts that have been outlined by several groups for adaptive planning have been implemented in a clinical trial. In a group of 21 head and neck cancer patients, IMRT and “dose painting by numbers (DPBNs)” was implemented in patients with head and neck cancer. Each patient’s treatment used 3 separate treatment plans: fractions 1-10 used the baseline DPBN18 FDG-PET]) voxel intensity-based IMRT plan based on a pretreatment 18F-FDG-PET/CT scan; fractions 11-20 used a plan based on a 18F-FDG-PET/CT scan acquired after the eighth fraction; and fractions 21-32 used a conventional (uniform dose) IMRT plan. In a phase I trial, 2 dose prescription levels were tested: a median dose of 80.9 Gy to the high-dose clinical target volume (CTV [high-dose] (dose level I) and a median dose of 85.9 Gy to the GTV (dose level II. Treatment adaptation (ie, plans based on the second 18FFDG-PET/CT scan) reduced the volumes for the GTV (41%, P ⫽ .01), CTV (high dose) (18%, P ⫽ .01), high-dose planning target volume (14%, P ⫽ .02), and parotids (9-12%, P ⬍ .05). Because the GTV was much smaller than the CTV (high dose) and target adaptation, further dose escalation at dose level II resulted in less severe toxicity than that observed at dose level I.70 This study, although small, is an example of how PET/CT MIGRT can be implemented. Clearly, larger studies are needed to determine the clinical impact of the approach, especially as it relates to tumor control.

Dose Verification for High-Energy Particle Therapies Particle beam therapies are seeing increased clinical and research applications. Although such therapy systems can be very expensive, they can offer unique advantages over photon-based IMRT techniques by virtue of the Bragg peak energy deposition profile, which can allow the dose to be delivered in very close proximity to critical structures (eg, the spinal cord or optic nerve). By virtue of the high-energy protons or carbon particles interacting with normal tissues, in vivo activation of tissue can be achieved resulting in the irradiated tissue becoming transiently a positron emitter. This approach has the potential for dose verification in vivo in a manner not previously available. Short-lived radiotracers, such as 15O (half-life 109 seconds) and 11C, may disappear promptly because of their very short half-life and flow dependence. Studies in normal mus-

cle have shown that about two thirds of radioactivity generated by heavy ion beams washes out with a 4-minute half-life; however, there is a longer-lived component useful for dose deposition verification.71,72 Other longer-lived tracers may be generated and in tissues with low blood flow, such as bone, a more durable signal can be achieved. A variety of recent reports have shown the feasibility of using the in vivo activation of positrons as an imaging tool for verification of the dose distribution.73

Conclusions FDG PET/CT has revolutionized the diagnosis and follow-up of patients with cancer and is increasingly the test of choice for cancer imaging. The technology also is being more widely applied in radiation oncology to avoid geographic misses, intensify dose to high-risk areas, decrease dose to low-risk areas, and hopefully improve therapeutic outcomes. A range of additional PET and some SPECT tracers are emerging, which may also have a considerable impact in radiation oncology by portraying, at relatively low resolution, tumor proliferation, hypoxia, non–FDG-avid tumors as well as in displaying normal tissue function. Hybrid functional imaging may help guide dose intensification, dose painting, and adaptive trial designs in which additional intensification/ deintensification is performed based on key imaging findings and treatment response to chemotherapeutic and/or targeted agents. Major challenges remain in this evolving field, including how to precisely define tumor margins, especially for tracers with lower tumor uptake, ensuring registration of CT and molecular imaging data remain precise, issues of variability over the course of many days of radiation treatment, and exactly how to use these powerful data to guide therapy. Because MIGRT is a rather new area for investigation, it is important that functional imaging and MIGRT are integrated in a systematic manner into clinical trials of radiation oncology, not only for treatment planning but also for diagnosis and response assessment. In an era of constrained resources, studies confirming improved outcomes in patients treated with MIGRT versus standard methods are important and should be performed as the technologies mature.

References 1. Gregoire V, Haustermans K, Geets X, et al: PET-based treatment planning in radiotherapy: A new standard? J Nucl Med 48:68S-77S, 2007 (suppl 1) 2. Ling CC, Humm J, Larson S, et al: Towards multidimensional radiotherapy (MD-CRT): Biological imaging and biological conformality. Int J Radiat Oncol Biol Phys 47:551-560, 2000 3. Ott RJ, Tait D, Flower MA, et al: Treatment planning for 131I-mIBG radiotherapy of neural crest tumours using 124I-mIBG positron emission tomography. Br J Radiol 65:787-791, 1992 4. Ahmadzadehfar H, Sabet A, Biermann K, et al: The significance of 99mTc-MAA SPECT/CT liver perfusion imaging in treatment planning for 90Y-microsphere selective internal radiation treatment. J Nucl Med 51:1206-1212, 2010 5. Dewaraja YK, Schipper MJ, Roberson PL, et al: 131I-tositumomab radioimmunotherapy: Initial tumor dose–response results using 3-di-

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6.

7.

8.

9.

10.

11.

12. 13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

24. 25.

26.

mensional dosimetry including radiobiologic modeling. J Nucl Med 51:1155-1162, 2010 Forster GJ, Engelbach MJ, Brockmann JJ, et al: Preliminary data on biodistribution and dosimetry for therapy planning of somatostatin receptor positive tumours: Comparison of (86)Y-DOTATOC and (111)In-DTPA-octreotide. Eur J Nucl Med 28:1743-1750, 2001 Hobbs RF, Wahl RL, Lodge MA, et al: 124I PET-based 3D–RD dosimetry for a pediatric thyroid cancer patient: Real-time treatment planning and methodologic comparison. J Nucl Med 50:1844-1847, 2009 Nestle U, Weber W, Hentschel M, et al: Biological imaging in radiation therapy: Role of positron emission tomography. Phys Med Biol 54:R125, 2009 Delbeke D, Schoder H, Martin WH, et al: Hybrid imaging (SPECT/CT and PET/CT): Improving therapeutic decisions. Semin Nucl Med 39: 308-340, 2009 Ford EC, Herman J, Yorke E, et al: 18F-FDG PET/CT for image-guided and intensity-modulated radiotherapy. J Nucl Med 50:1655-1665, 2009 Schoder H, Ong SC: Fundamentals of molecular imaging: Rationale and applications with relevance for radiation oncology. Semin Nucl Med 38:119-128, 2008 Delbeke D, Martin WH: Metabolic imaging with FDG: A primer. Cancer J 10:201-213, 2004 Igdem S, Alco G, Ercan T, et al: The application of positron emission tomography/computed tomography in radiation treatment planning: Effect on gross target volume definition and treatment management. Clin Oncol (R Coll Radiol) 22:173-178, 2010 Mac Manus MP, Hicks RJ, Ball DL, et al: F-18 fluorodeoxyglucose positron emission tomography staging in radical radiotherapy candidates with nonsmall cell lung carcinoma: Powerful correlation with survival and high impact on treatment. Cancer 92:886-895, 2001 Aristei C, Falcinelli L, Palumbo B, et al: PET and PET-CT in radiation treatment planning for lung cancer. Expert Rev Anticancer Ther 10: 571-584, 2010 Grosu AL, Nestle U, Weber WA: How to use functional imaging information for radiotherapy planning. Eur J Cancer 45:461-463, 2009 (suppl 1) van Loon J, Offermann C, Bosmans G, et al: 18FDG-PET based radiation planning of mediastinal lymph nodes in limited disease small cell lung cancer changes radiotherapy fields: A planning study. Radiother Oncol 87:49-54, 2008 De Ruysscher D, Wanders S, Minken A, et al: Effects of radiotherapy planning with a dedicated combined PET-CT-simulator of patients with non-small cell lung cancer on dose limiting normal tissues and radiation dose-escalation: A planning study. Radiother Oncol 77:5-10, 2005 Kalff V, Hicks RJ, MacManus MP, et al: Clinical impact of (18)F fluorodeoxyglucose positron emission tomography in patients with nonsmall-cell lung cancer: A prospective study. J Clin Oncol 19:111-118, 2001 Macmanus M, D’Costa I, Everitt S, et al: Comparison of CT and positron emission tomography/CT coregistered images in planning radical radiotherapy in patients with non-small-cell lung cancer. Australas Radiol 51:386-393, 2007 Connell CA, Corry J, Milner AD, et al: Clinical impact of, and prognostic stratification by, F-18 FDG PET/CT in head and neck mucosal squamous cell carcinoma. Head Neck 29:986-995, 2007 Aerts HJ, van Baardwijk AA, Petit SF, et al: Identification of residual metabolic-active areas within individual NSCLC tumours using a preradiotherapy, 18fluorodeoxyglucose-PET-CT scan. Radiother Oncol 91:386-392, 2009 Corry J, Rischin D, Hicks RJ, et al: The role of PET-CT in the management of patients with advanced cancer of the head and neck. Curr Oncol Rep 10:149-155, 2008 Grigsby PW: PET/CT imaging to guide cervical cancer therapy. Futures Oncol 5:953-958, 2009 Lin LL, Mutic S, Low DA, et al: Adaptive brachytherapy treatment planning for cervical cancer using FDG-PET. Int J Radiat Oncol Biol Phys 67:91-96, 2007 Esthappan J, Chaudhari S, Santanam L, et al: Prospective clinical trial of

99

27.

28.

29.

30.

31.

32.

33. 34. 35.

36.

37.

38.

39.

40.

41.

42. 43.

44.

45.

46.

positron emission tomography/computed tomography image-guided intensity-modulated radiation therapy for cervical carcinoma with positive para-aortic lymph nodes. Int J Radiat Oncol Biol Phys 72: 1134-1139, 2008 Schwarz JK, Lin LL, Siegel BA, et al: 18-F-fluorodeoxyglucose-positron emission tomography evaluation of early metabolic response during radiation therapy for cervical cancer. Int J Radiat Oncol Biol Phys 72: 1502-1507, 2008 Winton E, Heriot AG, Ng M, et al: The impact of 18-fluorodeoxyglucose positron emission tomography on the staging, management and outcome of anal cancer. Br J Cancer 100:693-700, 2009 Kim PJ, Hicks RJ, Wirth A, et al: Impact of 18F-fluorodeoxyglucose positron emission tomography before and after definitive radiation therapy in patients with apparently solitary plasmacytoma. Int J Radiat Oncol Biol Phys 74:740-746, 2009 Lee JW, Mac Manus M, Hogg A, et al: Clinical influence of 18F-fluorodeoxyglucose positron emission tomography on the management of primary tumours of the thymus. J Med Imaging Radiat Oncol 52:25461, 2008 Leong T, Everitt C, Yuen K, et al: A prospective study to evaluate the impact of FDG-PET on CT-based radiotherapy treatment planning for oesophageal cancer. Radiother Oncol 78:254-261, 2006 Mai SK, Welzel G, Hermann B, et al: Can the radiation dose to CTenlarged but FDG-PET-negative inguinal lymph nodes in anal cancer be reduced? Strahlenther Onkol 185:254-259, 2009 Jerusalem G, Hustinx R, Beguin Y, et al: Positron emission tomography imaging for lymphoma. Curr Opin Oncol 17:441-445, 2005 Weckesser M, Matheja P, Rickert C, et al: Evaluation of the extension of cerebral gliomas by scintigraphy. Strahlenther Onkol 176:180-185, 2000 Weber DC, Casanova N, Zilli T, et al: Recurrence pattern after [(18)F]fluoroethyltyrosine-positron emission tomography-guided radiotherapy for high-grade glioma: A prospective study. Radiother Oncol 93:586-592, 2009 Grosu AL, Weber WA, Riedel E, et al: L-(methyl-11C) methionine positron emission tomography for target delineation in resected high-grade gliomas before radiotherapy. Int J Radiat Oncol Biol Phys 63:64-74, 2005 Pinkawa M, Attieh C, Piroth MD, et al: Dose-escalation using intensitymodulated radiotherapy for prostate cancer—Evaluation of the dose distribution with and without 18F-choline PET-CT detected simultaneous integrated boost. Radiother Oncol 93:213-219, 2009 Niyazi M, Bartenstein P, Belka C, et al: Choline PET based dose-painting in prostate cancer—Modelling of dose effects. Radiol Oncol 5:23, 2010 Sun A, Sorensen J, Karlsson M, et al: 1-[11C]-acetate PET imaging in head and neck cancer—A comparison with 18F-FDG-PET: Implications for staging and radiotherapy planning. Eur J Nucl Med Mol Imaging 34:651-657, 2007 Troost EG, Vogel WV, Merkx MA, et al: 18F-FLT PET does not discriminate between reactive and metastatic lymph nodes in primary head and neck cancer patients. J Nucl Med 48:726-735, 2007 Han D, Yu J, Yu Y, et al: Comparison of (18)F-fluorothymidine and (18)F-fluorodeoxyglucose PET/CT in delineating gross tumor volume by optimal threshold in patients with squamous cell carcinoma of thoracic esophagus. Int J Radiat Oncol Biol 76:1235-1241, 2010 Wang JZ, Li XA: Impact of tumor repopulation on radiotherapy planning. Int J Radiat Oncol Biol Phys 61:220-227, 2005 Chapman JD, Engelhardt EL, Stobbe CC, et al: Measuring hypoxia and predicting tumor radioresistance with nuclear medicine assays. Radiother Oncol 46:229-237, 1998 Rajendran JG, Hendrickson KR, Spence AM, et al: Hypoxia imagingdirected radiation treatment planning. Eur J Nucl Med Mol Imaging 33:44-53, 2006 (suppl 1) Troost EG, Laverman P, Philippens ME, et al: Correlation of [18F]FMISO autoradiography and pimonidazole [corrected] immunohistochemistry in human head and neck carcinoma xenografts. Eur J Nucl Med Mol Imaging 35:1803-1811, 2008 Lin Z, Mechalakos J, Nehmeh S, et al: The influence of changes in tumor hypoxia on dose-painting treatment plans based on 18F-FMISO positron emission tomography. Int J Radiat Oncol Biol Phys 70:1219-1228, 2008

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100 47. Thorwarth D, Eschmann SM, Holzner F, et al: Combined uptake of [18F]FDG and [18F]FMISO correlates with radiation therapy outcome in head-and-neck cancer patients. Radiother Oncol 80:151-156, 2006 48. Thorwarth D, Eschmann SM, Paulsen F, et al: Hypoxia dose painting by numbers: A planning study. Int J Radiat Oncol Biol Phys 68:291-300, 2007 49. Lee NY, Mechalakos JG, Nehmeh S, et al: Fluorine-18-labeled fluoromisonidazole positron emission and computed tomography-guided intensity-modulated radiotherapy for head and neck cancer: A feasibility study. Int J Radiol Oncol J Biol Phys 70:2-13, 2008 50. Dirix P, Vandecaveye V, De Keyzer F, et al: Dose painting in radiotherapy for head and neck squamous cell carcinoma: Value of repeated functional imaging with (18)F-FDG PET, (18)F-fluoromisonidazole PET, diffusion-weighted MRI, and dynamic contrast-enhanced MRI. J Nucl Med 50:1020-7, 2009 51. Grosu AL, Souvatzoglou M, Roper B, et al: Hypoxia imaging with FAZA-PET and theoretical considerations with regard to dose painting for individualization of radiotherapy in patients with head and neck cancer. Int J Radiat Oncol Biol Phys 69:541-551, 2007 52. Basu S, Alavi A: Molecular imaging (PET) of brain tumors. Neuroimaging Clin N Am 19:625-646, 2009 53. Wong TZ, Lacy JL, Petry NA, et al: PET of hypoxia and perfusion with 62Cu-ATSM and 62Cu-PTSM using a 62Zn/62Cu generator. AJR Am J Roentgenol 190:427-432, 2008 54. Chao KS, Bosch WR, Mutic S, et al: A novel approach to overcome hypoxic tumor resistance: Cu-ATSM-guided intensity-modulated radiation therapy. Int J Radiat Oncol Biol Phys 49:1171-1182, 2001 55. Myerson RJ, Singh AK, Bigott HM, et al: Monitoring the effect of mild hyperthermia on tumour hypoxia by Cu-ATSM PET scanning. Int J Hyperthermia 22:93-115, 2006 56. Ellis RJ, Kaminsky DA: Fused radioimmunoscintigraphy for treatment planning. Rev Urol 8:S11-S19, 2006 (suppl 1) 57. Gehler B, Paulsen F, Oksuz MO, et al: [68Ga]-DOTATOC-PET/CT for meningioma IMRT treatment planning. Radiol Oncol 4:56, 2009 58. McGuire SM, Zhou S, Marks LB, et al: A methodology for using SPECT to reduce intensity-modulated radiation therapy (IMRT) dose to functioning lung. Int J Radiat Oncol Biol Phys 66:1543-1552, 2006 59. Zasadny KR, Kison PV, Francis IR, et al: FDG PET determination of metabolically active tumor volume and comparison with CT. Clin Positron Imaging 1:123-129, 1998 60. Biehl KJ, Kong FM, Dehdashti F, et al: 18F-FDG PET definition of gross tumor volume for radiotherapy of non-small cell lung cancer: Is a single

61.

62. 63.

64.

65.

66.

67.

68.

69.

70.

71.

72.

73.

standardized uptake value threshold approach appropriate? J Nucl Med 47:1808-1812, 2006 Bayne M, Hicks RJ, Everitt S, et al: Reproducibility of “intelligent” contouring of gross tumor volume in non-small-cell lung cancer on PET/CT images using a standardized visual method. Int J Radiat Oncol Biol Phys 77:1151-1157, 2010 Gupta T, Beriwal S: PET/CT-guided radiation therapy planning: From present to the future. Indian J Cancer 47:126-133, 2010 Haggstrom I, Johansson L, Larsson A, et al: Semi-automatic tumour segmentation by selective navigation in a three-parameter volume, obtained by voxel-wise kinetic modelling of 11C-acetate. Radiat Protect Dosim 139:214-218, 2010 Nagel CC, Bosmans G, Dekker AL, et al: Phased attenuation correction in respiration correlated computed tomography/positron emitted tomography. Med Phys 33:1840-1847, 2006 Poettgen C, Theegarten D, Eberhardt W, et al: Correlation of PET/CT findings and histopathology after neoadjuvant therapy in non-small cell lung cancer. Oncology 73:316-323, 2007 Ford EC, Lavely WC, Frassica DA, et al: Comparison of FDG-PET/CT and CT for delineation of lumpectomy cavity for partial breast irradiation. Int J Radiat Oncol Biol Phys 71:595-602, 2008 Schellenberg D, Quon A, Minn AY, et al: 18Fluorodeoxyglucose PET is prognostic of progression-free and overall survival in locally advanced pancreas cancer treated with stereotactic radiotherapy. Int J Radiol Oncol J Biol Phys 77:1420-1425, 2010 Wahl RL, Jacene H, Kasamon Y, et al: From RECIST to PERCIST: Evolving considerations for PET response criteria in solid tumors. J Nucl Med 50:122S-150S, 2009 (suppl 1) Bussink J, van Herpen CM, Kaanders JH, et al: PET-CT for response assessment and treatment adaptation in head and neck cancer. Lancet Oncol 2010 11:661-669, 2010 Duprez F, De Neve W, De Gersem W, et al: Adaptive dose painting by numbers for head-and-neck cancer. Int J Radiat Oncol Biol Phys July 17, 2010 [Epub ahead of print] Tomitani T, Pawelke J, Kanazawa M, et al: Washout studies of 11C in rabbit thigh muscle implanted by secondary beams of HIMAC. Phys Med Biol 48:875-889, 2003 Mizuno H, Tomitani T, Kanazawa M, et al: Washout measurement of radioisotope implanted by radioactive beams in the rabbit. Phys Med Biol 48:2269-2281, 2003 Fiedler F, Shakirin G, Skowron J, et al: On the effectiveness of ion range determination from in-beam PET data. Phys Med Biol 55:1989-1998, 2010

The Promise and Pitfalls of Positron Emission Tomography and Single-Photon Emission Computed Tomography Molecular Imaging–Guided Radiation Therapy Richard L. Wahl, MD,*,† Joseph M. Herman, MD, MSc,‡ and Eric Ford, PhD‡ External beam radiation therapy procedures have, until recently, been planned almost exclusively using anatomic imaging methods. Molecular imaging using hybrid positron emission tomography (PET)/computed tomography scanning or single-photon emission computed tomography (SPECT) imaging has provided new insights into the precise location of tumors (staging) and the extent and character of the biologically active tumor volume (BTV) and has provided differential response information during and after therapy. In addition to the commonly used radiotracer 18F-fluoro- 2-deoxyD-glucose (FDG), additional radiopharmaceuticals are being explored to image major physiological processes as well as tumor biological properties, such as hypoxia, proliferation, amino acid accumulation, apoptosis, and receptor expression, providing the potential to target or boost the radiation dose to a biologically relevant region within a tumor, such as the most hypoxic or most proliferative area. Imaging using SPECT agents has furthered the possibility of limiting dose to functional normal tissues. PET can also portray the distribution of particle therapy by displaying activated species in situ. With both PET and SPECT imaging, fundamental physical issues of limited spatial resolution relative to the biological process, partial volume effects for quantification of small volumes, image misregistration, motion, and edge delineation must be carefully considered and can differ by agent or the method applied. Molecular imaging– guided radiation therapy (MIGRT) is a rapidly evolving and promising area of investigation and clinical translation. As MIGRT evolves, evidence must continue to be gathered to support improved clinical outcomes using MIGRT versus purely anatomic approaches. Semin Radiat Oncol 21:88-100 © 2011 Published by Elsevier Inc.

T

he rapid evolution of precision radiation therapy now permits the delivery of high-dose rates to tumors located near normal structures with exquisitely sculpted dose sparing of the normal tissues. Currently, anatomic imaging is essential and the primary means applied to guide precision radiation therapy planning and serves as an important tool for providing attenuation maps to facilitate accurate dose

*Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD. †Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD. ‡Department of Molecular Radiation Oncology, Johns Hopkins University School of Medicine, Baltimore, MD. Address reprint requests to Richard L. Wahl, MD, Division of Nuclear Medicine, 601 North Caroline Street, Room 3223, JHOC, Baltimore, MD 21287-0817. E-mail: [email protected]

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calculation.1 Anatomic imaging is, however, limited in sensitivity for defining tumor extent, capacity to probe the selective biology of tumors and normal tissues, and potential for monitoring response of tumors and normal tissues to treatment. The concept of a “biological tumor volume”(BTV) was introduced by Ling et al2 over a decade ago to describe radiation treatment planning, which included imaging data specific to tumor biology beyond that provided by anatomy alone. In this review, the term “molecular imaging– guided radiation therapy” (MIGRT) is used to describe imaging processes from which the signal is expected to map molecular distributions and their surrogates and can be used to guide external beam therapy plans. Although the discussion focuses on molecular imaging, it is understood that these images operate in concert with anatomic imaging to guide precision radiation therapy.

Promise and pitfalls of PET and SPECT MIGRT Although 18F-fluoro- 2-deoxyD-glucose (FDG) positron emission tomography (PET) has had a reasonably long history of use as an adjunct to anatomic imaging for radiation treatment planning, a large number of interesting (and some targeted) agents for both PET and single-photon emission computed tomography (SPECT) have been introduced or are under investigation to probe other aspects of tumor biology and normal tissue physiology. Internally administered radiopharmaceuticals that target cancer after systemic administration include, among others, radiolabeled monoclonal antibodies, such as anti-CD20 reagents; radiopeptides binding to the somatostatin receptors; 131I-meta-iodo-benzylguanidine (MIBG); bone-targeting tracers such as 89Sr (strontium 89) or 153Sm-EDTMP (samavium 153-ethylene dlamine tetramethylene phosphonate); and 131I and 90Y microspheres for the treatment of hepatic malignancies. These therapeutic radiopharmaceuticals often require imaging (often SPECT) before treatment is performed and treatment may include imageguided quantitative dosimetry to personalize radiation dose to normal tissues. Recent reviews further describe the role of targeted radionuclide therapies and the associated role of imaging in their guidance.3-7

Overview of PET and of SPECT PET is an imaging method in which typically short-lived positron emitters, commonly produced in a medical cyclotron and usually attached to a targeting molecule, are used to qualitatively and quantitatively assess the biodistribution of the targeting molecule in vivo. Accurate and biologically relevant imaging depends on the delivery of the imaging agent to the relevant biological process. When the positron is emitted; it travels a short distance in tissue (about 1 mm for an 18F-associated positron) and then loses sufficient energy to interact with an electron, transiently producing positronium. This material gives off two 511-keV photons traveling at essentially 180° from one another as well as a neutrino. The 511-keV photons can be imaged using a specialized PET camera. Modern PET scanners often include a computed tomography (CT) scanner as a part of the instrument. Thus, patients can have a PET and a CT scan in a single imaging session. Advantages of PET are its high sensitivity because of the use of precise timing information to determine if photons interact with detector materials simultaneously (coincidently or nearly so, as is the case for positron annihilation) as well as the opportunity to accurately correct radiation measurements for body thickness (attenuation). The CT scan from the PET/CT produces a data source for an attenuation map. PET images quite accurately provide quantitative radioactivity measurements in vivo. A wide range of PET radiotracers have been developed to examine biologically relevant processes. The most common PET radioisotopes are 18F (109-minute half-life) and 11C (20minute half-life) with growing applications of 68Ga (1-hour half life). These short half-lives mean that most PET tracer studies have relatively favorable radiation absorbed dose profiles. These tracers are usually best suited for assessing rather

89 rapid biological processes. Longer-lived positron emitters like I-124 have more limited applications because of their unfavorable dosimetric properties. SPECT is a method of performing single-photon imaging (a class of imaging techniques in use for over 50 years) and typically involves rotating one or more gamma camera heads about the patient and acquiring, for example, 64 to 128 projection views from varying angles. This allows reconstruction of the 3D distribution of radioactivity in vivo for single-photon emitters. Common single-photon emitters (so named as they emit at least 1 gamma ray per decay which can travel in any direction) include 99mTc, 123I, 111In, and 131I. Determining the location of the emitted photons in single-photon imaging is typically achieved through the use of a lead collimator with 1 or many holes that provide spatial information. When integrated with CT systems, SPECT can measure radioactivity in vivo with moderate quantitative accuracy, but in general PET is more quantitatively accurate in determining the distribution of radioactivity in vivo. SPECT radiotracers typically have longer half-lives than PET tracers, but because they often release less energy, often nonparticulate, per decay their dosimetry can be satisfactory. They can be used to study slower biological processes (eg, localization of monoclonal antibodies to tumors which can take several days). Both PET/CT and SPECT/CT methods allow export of Digital Imaging and Communications in Medicine (DICOM) image datasets for CT and for the nuclear images, which can be imported into radiation therapy planning systems. A critical point for both imaging methods is that routine clinical positioning for optimal patient comfort in a diagnostic study is often not identical to the positioning used in delivery of radiation therapy. It is often highly desirable to perform the studies using a flat table, lasers, and similar imaging equipment. Detailed reviews8,9 describing these methods have been published. PET/CT simulators are an ideal way to minimize differences in patient setup, but these are only available at select centers. Alternatively, one can immobilize the patient in a cradle or mold and use the same device for the separate PET and CT scans. We have recently reported on many of the technical issues associated with using FDG PET/CT to guide IMRT.10

Key Biological Processes in Tumors and Normal Tissues Cancers differ substantially from normal tissues. Their different shapes, location, and densities allow CT imaging to detect and define many cancers. Despite this, many small tumors and/or subcentimeters or micrometastases are not detected on a CT scan. Nodal metastases are difficult to detect, whereas many enlarged nodes do not contain cancer. If surgical or radiation therapy have distorted the normal anatomy, tumor detection and delineation is quite difficult with a CT scan.

90 Cancers have a variety of characteristics that are associated with survival advantages. These characteristics are sometimes called hallmarks of cancer. Each of these hallmarks is a potential target for tumor imaging. Several of the molecular imaging agents available for cancer imaging as related to radiation oncology have been reviewed fairly recently.11

Glucose Use From an imaging standpoint, the increased glucose use seen in cancers is often profound. Increased glucose use is a typical alteration of most cancers and may in part be related to the growth of cancers at the extremes of their blood supply that results in hypoxia. Hypoxia upregulates the GLUT 1 glucose transporter and anaerobic glycolysis, which can provide energy to cells under conditions of profound hypoxia. Cancers also have accelerated glucose use under conditions of more normal oxygenation, and this aerobic glycolysis is recognized as the Warburg effect. The Pasteur effect is when glycolysis increases even more under conditions of hypoxia. The precise mechanisms of this process remain under study but are in part related to hypoxia-inducible factor (HIF)-1␣, in part to transforming oncogenes, as well as to a variety of mutations in the glucose utilization pathway. Most studies of tumors by autoradiography have shown FDG to be distributed throughout most of the viable tumor elements. Although most of the FDG signal is from viable cancer cells, there is a component of uptake in viable infiltrating immune cells, such as white cells of various types, which may cause a false-positive PET signal in the absence of tumor. Although FDG is probably the tracer best suited to detecting tumor extent in the vast majority of cancers, not all cancers are FDG avid, including many prostate cancers (especially the non– castrate-resistant form), hepatomas, and renal adenocarcinomas. Mixed results have been reported regarding FDG avidity for pancreatic cancers and mucinous histology colon cancers. Some tumors are not well seen because of surrounding normal tissues that accumulate FDG (eg, brain tumors). The excreted FDG in the urine and normal accumulation in the stomach, heart, and gut can also confound planning. Combined PET/CT with FDG has shown superior performance to CT scanning alone in common cancers, such as colorectal cancer, lymphoma, and lung. The reader is referred to several textbooks on the subject of the diagnostic accuracy of FDG PET/CT (eg, Principles and Practice of PET) and other reviews focused on radiation therapy.9,12 If the goal of radiation therapy is cure, then knowing the extent of tumor is critically important. Many FDG-avid tumors are staged by FDG PET/CT (a procedure routinely reimbursed by Medicare in the United States). PET/CT is commonly used for staging lung cancer. With PET/CT, about 15% to 20% of patients with cancers being considered for therapy with local radiation therapy were found to have tumors outside of the expected radiation field, either distant metastases or second primary lesions.13,14 Treatment planning of many types of cancer has been explored with FDG PET. Several recent reviews are available on

R.L. Wahl, J.M. Herman, and E. Ford specific cancers and the use of PET/CT in planning treatment.10,11,15,16 However, in the key areas of tumor imaging as related to radiation oncology, there is little doubt that FDG is the dominant molecular imaging tracer. There is evidence that PET-based as opposed to CT-based plans expose normal lung to less radiation and higher probabilities of tumor control.17,18 This dose savings to normal tissues is most apparent when mediastinal lymph nodes are excluded from the field of radiation if they are “negative” on PET.17 Patients planned with PET and treated with radiation appear to have superior outcomes to those who were planned without PET (probably in part by eliminating the attempt to cure patients whose disease extends out of therapeutic field at presentation).19 Head and neck cancer has been extensively studied using FDG PET/CT. Early studies showed that FDG PET was more accurate than either CT scans or MRI in detecting recurrent tumors after chemotherapy and irradiation, where anatomic alterations may be confounding. A variety of studies have examined changes in field shapes and sizes using FDG and other tracers in head and neck cancer.20 As an example, in a study of 76 patients with head and neck cancer, PET/CT scanning led to a TNM classification alteration in 34% (12/ 35), a change in the radiotherapy planning technique and/or dose in 29% (10/35), and altered assessment of treatment response in 43% (13/30). A complete metabolic response on PET was predictive of overall survival (P ⫽ .037).21 PET with FDG after treatment in head and neck cancer carries prognostic significance. Patients with higher FDG uptake after radiation treatment have inferior outcomes compared with patients with negative scans.22 The general area of PET and radiation therapy in head and neck cancer has been reviewed recently.23 PET and PET/CT have been applied in cervical cancer as well. Grigsby24 has shown that FDG PET is more sensitive than CT in detecting lymph node metastases of cervical cancer. Furthermore, positive PET scans in lymph nodes in the para-aortic area of cervical cancer patients are associated with worse outcomes than patients with negative para-aortic area lymph nodes. Similarly, positive uptake in the primary tumor after therapy has been associated with a less favorable outcome.24 Such data suggest 2 possibilities: (1) the PET findings are clear markers of a poor prognosis and this cannot be altered with current therapies because the underlying biology is so aggressive (ie, it is simply prognostic) or (2) the PET findings indicate where additional treatment should be delivered and can predict if it will be successful. Clearly, this cannot be known until studies are performed to more intensively treat those patients with high-risk phenotypes of cervical cancer, identifying them before or after therapy. For example, positive para-aortic nodes outside of the field of planned therapy should logically be included in the field of therapy (treatment field expanded). Residual FDG uptake in a tumor presumably will need additional therapy as well. PET/CT scanning can be used to guide the delivery of brachytherapy to primary cervical cancers and para-aortic nodes.25,26 The timing of response in cervical cancer to radi-

Promise and pitfalls of PET and SPECT MIGRT

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Figure 1 CT-based (left image and black contour) gross tumor volume delineation and FDG PET/CT biological tumor volume (right and white contour). Overlaid isodose distributions are from the CT-based target definition. Potential for booing the most FDG avid tumor region exists.

ation therapy may also be predictive of outcome.27 The role of FDG PET in cervical cancer has recently been reviewed by Grigsby. It is important to note that in the posttreatment period, PET can also be useful for surveillance and PET scans performed 3 months after the completion of treatment have clear prognostic value.24 PET/CT scanning has been applied to planning a variety of other FDG-avid cancers. Reports in pancreatic, anal, thymic cancers, and myeloma are but a few of the examples that generally show changes in management by identifying additional lesions and by altering the size of the gross tumor volume (GTV) because of the biological information provided by PET.28-31 Recently, dose reduction in anatomically enlarged but FDG PET–negative lymph nodes of anal cancer has been reported to be a promising approach.32 It must be realized, however, that neither PET nor SPECT can detect microscopic tumor involvement. Clearly, FDG PET is the main MIGRT approach at the present time, but other approaches should be considered. Although not all the following have been applied yet, it is important to realize the following PET and SPECT agents have variable potential to guide external beam therapies to the relevant areas. An example of the biological tumor volume (BTV) from FDG PET and the GTV from CT scanning is shown in Figure 1 for a patient with pancreatic cancer. These volumes are not identical, and, in this case, the PET volume of tumor is smaller than that of CT scanning.

Flow, Amino Acid Transport, and Membrane Transport The SPECT tracers 201Tl and 67Ga-citrate have been used to detect tumors of a variety of types.33 Both agents have rather unfavorable imaging and dosimetry characteristics and are being used less often now that FDG PET is available. None-

theless, these single-photon emitters have been able to identify viable lymphoma; brain tumors; and, in some instances, sarcomas. Their mechanisms of uptake differ from FDG. 201Tl uptake is flow dependent and also depends on intact membrane energetics. In brain tumors, 201Tl uptake is correlated with bloodbrain barrier disruption and may underestimate tumor size versus amino acid tracers, which can also be used for 34 67 SPECT. Ga uptake in tumors is, in part, related to its behaving as a structural analog to transfer and as an entry to tumor cells via the transferrin receptor. Amino acid transport and protein synthesis are often increased in cancers. Several amino acid– based PET tracers have been used in PET imaging. The greatest advantages for this type of agent can be seen in non–FDG-avid tumors or in situations like brain tumors in which the normal FDG background activity is too extensive to allow clear distinction of the tumor from normal brain. Although 11C-L-methionine has been used the most, 18F-labeled amino acids or nonnatural amino acids, such as 18F-anti FACBC have shown promise, although the latter displays both wash in and wash out from tumor foci, such as in prostate cancer. Amino acid tracers, such as 11C-methionine, 18F-fluroethyltyrosine, and the SPECT agent 123I-alpha methyl tyrosine, have been applied to plan treatment of patients with brain tumors. Radiation treatment plans based on amino acid accumulation in brain tumors can show more extensive tumor than the simple region of blood brain-barrier enhancement and certainly disparate patterns and discrepancies between GTV and BTV.35,36

Membrane Synthesis and Aerobic Energy Production Growing tumors require components for membrane synthesis, and tumors that are not hypoxic may have more aerobic

92 energy sources than glycolytic ones. Although our understanding of the exact mechanisms of uptake remains in evolution, it is clear that radiotracers, such as 11C-acetate, 11Ccholine, and 18F-fluorocholine, can localize to non–FDG-avid cancers, like prostate cancer, and to a variety of other types of cancer, such as hepatocellular carcinoma (for acetate), that may not be FDG avid. Choline is transported into cancer cells via the choline transporters and appears to be phosphorylated by choline kinase. With time, choline is increasingly incorporated into the cell membrane and cell membrane precursors. Choline, both 11C- and 18F-labeled analogs (such as fluorcholine and fluoro ethyl choline), have been used in prostate cancer imaging as well as in imaging other cancers. These methods appear to generally be more sensitive for prostate cancer, especially the better differentiated prostate cancers, than FDG PET. Dosimetry estimates have been calculated using choline PET imaging, which have modeled the influence of a localized boost of radiation dose to choline-avid subregions within the prostate gland. Although this specific application may not have yet shown clear clinical benefit, it shows the potential for MIGRT approaches.37,38 Acetate can be used by cancers for oxidative energy production as well as a building block for membrane synthesis. 11C-acetate has been shown to be capable of imaging hepatomas, especially those that are non–FDG avid. Prostate cancer and some head and neck cancers can also be imaged with this tracer. Uptake of the radiotracer appears to be governed, in part, by levels of the enzyme fatty acid synthase. There has been relatively little work evaluating 11C-acetate for radiation therapy planning. In head and neck cancer, direct comparisons with FDG PET showed the standardized uptake value (SUV) of 11C-acetate avid tumors and nodes to be lower than their SUV for FDG. Tumor volumes estimated from PET were, however, larger for acetate than for FDG. The data on use of 11C-acetate for planning radiation therapy are very limited, but detecting more lesions with acetate than FDG suggests the potential for beneficial modification of treatment plans although much more study is needed.39 An example of a positive 11C-acetate scan in a patient with recurrent prostate cancer and a negative FDG PET scan are shown in Figure 2.

Proliferation Most, although not all, cancers have increased rates of proliferation. Cancers can grow as long as the rate of cell death is lower than the rate of cell proliferation. Thus, some cancers will have high proliferative rates, and others will have lower rates. In general, cancers with high proliferative rates, if left untreated, are the most lethal and have the worst prognosis. Some therapeutic agents, including radiation and chemotherapy, are more effective in rapidly proliferating than in more quiescent tumors. The measurement of proliferative rates is a key goal of cancer imaging and could have considerable potential for altering radiation therapy treatment plans. Rapidly and slowly proliferating tumors could be identified, responding tumors could be assessed, and tumor re-

R.L. Wahl, J.M. Herman, and E. Ford

Figure 2 Prostate cancer imaging with C11 acetate (top panel) and FDG (bottom panel) (JHU series). Note that the C11 acetate images show intense uptake in a lymph node, which appears normal by FDG PET. This node was clinically most consistent with recurrent prostate cancer.

growth could potentially be measured. Such information could change the approach to the therapy of a specific cancer. It is possible that radiation treatment–induced changes in cell proliferation may occur more rapidly in the presence of effective cancer therapy than changes in more general tumor viability markers, such as FDG, although this remains under study. Several attempts have been made to study cell proliferation with imaging. At least 2 forms of 11C-thymidine have been applied, but the 20-minute half-life and considerable metabolism are limitations to its use. There can be radioactive CO2 released, which can contaminate the tumor signal. Although 11C-thymidine showed proof of concept, other tracers, most notably 3=-deoxy-3=-fluorothymidine (18F-fluorothymidine [FLT]), which is a substrate for thymidine kinase, have proven more practicable for implementation into clinical imaging trials. In research applications, it is probable that FLT is second

Promise and pitfalls of PET and SPECT MIGRT only to FDG in use. However, FLT has some limitations. As a substrate for thymidine kinase, it is able to image only the thymidine “salvage” pathway. If there are high endogenous levels of thymidine present, it is possible that imaging this pathway may be influenced adversely by endogenous thymidine levels. In general, although not invariably, the uptake of FLT in tissue is in the form of FLT phosphate, and the FLT uptake is generally related to the proliferative index measured by other methods. Proliferation is not unique to tumors, and rapid proliferation can be seen in reactive lymph nodes (with activated lymphocytes in germinal centers) as well as in tumors. FLT uptake in lymph nodes (eg, head and neck cancer) not uncommonly is in reactive as opposed to malignoid lymph nodes.40 Absolute FLT uptake in tumors is often considerably lower than that of FDG so that maximum tumor SUV levels are often about 50% or less than those of FDG. There is often substantial FLT uptake in normal tissues, such as bone marrow, liver, and bowel. Smaller tumors may not be as detectable with FLT as they are with FDG. With a smaller starting SUV, the absolute decline in SUV with therapy is expected to be lower than with FDG. However, this agent is in a variety of clinical trials and has recently been assessed in defining the GTV in esophageal cancer. A much lower SUV peak cutoff is required to accurately detect tumor sizes with FLT than with FDG (1.4 vs 2.5). FLT will need40 more study and certainly may have its greatest value in assessing early treatment response although it is by no means specific for cancer.41 Autoradiographic studies by our group suggest the signal from FLT is less widely distributed within the tumor than that of FDG, which is consistent with imaging a subset of the BTV as defined by FDG. Another potential role of FLT would be to measure tumor repopulation rates.42 Planning based on FLT PET remains an area of opportunity. An example of a FLT PET study and a FDG PET study in a patient with lung cancer is shown in Figure 3. There is a much more intense visualization of the tumor on the FDG than on the FLT PET study.

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Figure 3 FDG (lower panel) versus FLT (upper) images of lung cancer. Images in untreated lung cancer demonstrate intense FDG uptake in the primary lesion in the left lower lobe with definite but lower FLT uptake in the primary lesion (Courtesy of Dr M. Chaudhry, Johns Hopkins University, Baltimore, MD).

Hypoxia One of the most exciting areas in MIGRT is the use of agents for imaging hypoxia to define hypoxic tumors that may benefit from more aggressive therapy. The aggressive therapies could include hypofractionation, radiosensitizers, dose escalation, and an alternative form of radiation to overcome radioresistance. Several radiotracers have been developed that have characteristics suitable for imaging hypoxia and are considered hypoxia imaging agents. Interest in this area has been present for many years; although with greater availability of PET/CT scanners, some of the possibilities are now being tested and are testable in both single-center and multicenter trials.43,44 There is little doubt that hypoxia is associated with both resistance to chemotherapy and standard radiation therapy. The oxygen effect is well known to readers of this journal. However, an underlying challenge in hypoxia imaging is

whether the hypoxia is diffusely distributed through the tumor or more focal. Focal hypoxia may result in radiation resistance but may be difficult to resolve by PET imaging in which the resolutions are in the 5-mm to 1-cm range. A variety of radiotracers has been used in attempts to image hypoxia. 18F-fluromisonidazole (FMISO) has been the most extensively evaluated agent. This agent is structurally related to the nonradioactive hypoxia targeting agent, pimonidazole. Both FMISO and pimonidazole are nitroimidazoles that bind to very hypoxic regions. FMISO has a relatively slow blood clearance and develops only relatively low target/ background ratios in hypoxic regions. A number of challenges exist with this agent. The relationship between hypoxia and pimonidazole uptake is not always perfect in vivo, and the scale of hypoxia in vivo may be much smaller in some tumors than others. This suggests that hypoxia, at least in

94 small areas of tumors, will be difficult to image with FMISO agents45 and shows a general challenge with molecular imaging agents; the scale of the imaging (resolution) may not be optimally matched to the scale of the biological process. Even if the biological distribution could be imaged at submillimeter resolution, therapeutic photon radiation could not be delivered with this precision because of the fundamental limitations of the physics of radiation transport at high energies. Another concern with hypoxia imaging is that at least 1 report has suggested that hypoxia is inconsistent in tumors and that there is variability of the hypoxic signal using FMISO from day to day in test and retest studies. Some of this variability may be artifactual in that dichotomizing tumors as hypoxic or not based on a cutoff tumor/muscle level of 1.4 or 1.6 to 1 artificially places a cut line on an inherently continuous process. In test-retest studies in vivo, there are challenges with imaging and coregistering precisely the same area of tumor. This may contribute to apparent variability in tracer uptake over time, which may simply be caused by errors in registration. In all probability, tumor hypoxia is not fully stable.46 This means that challenges are likely to be present if intratumoral “dose painting” is applied over time. If 18F-MISO is used as the basis of the therapeutic plan, its distribution may change over time. This may be beneficial, but it adds complexity to the treatment.46 The temporal variability may be less of an issue if diseases can be treated with hypofractionated regimens in which high doses of radiation are delivered over fewer sessions. With the ability to image more than 1 aspect of cancer biology, the possibility of targeting the entire tumor (perhaps with FDG and anatomic imaging) and the hypoxic subregions (perhaps with FMISO) may be a useful approach to alter biological dose delivery within tumors. Clearly, the spatial resolution of PET may simply be insufficient to allow for high-resolution delivery of the tumor dose. There have been several reports showing that the FDG signal and the FMISO signal in head and neck cancers are not identical. There appears to be support for the concept that high FMISO activity in head and neck cancers is associated with a more aggressive phenotype and a less favorable outcome.47 The possibility to target such hypoxic areas within a tumor with an additional radiation dose is thus biologically rational.48 A logical approach to dealing with hypoxic regions identified by imaging with FMISO would be to increase the radiation dose to the area of greatest hypoxia. One group attempted to study the feasibility of boosting the radiation dose to areas of FMISO avidity within FDG-avid tumor volumes. Regions of elevated 18F-FMISO uptake within the FDG PET/CT GTV were targeted for an IMRT boost. Additional targets and/or normal structures were contoured or transferred to treatment planning to generate18F-FMISO PET/CTguided IMRT plans. The heterogeneous distribution of 18FFMISO within the GTV showed variable levels of hypoxia within the tumor. It was feasible in planning to dose escalate the GTV (hyopoxic region) to 84 Gy in all 10 patients and in

R.L. Wahl, J.M. Herman, and E. Ford 1 patient to 105 Gy without exceeding the normal tissue tolerance.49 A recent article evaluated 15 patients with locally advanced HNSCC treated with concomitant chemoradiotherapy, who were prospectively imaged with 18F-FDG PET (18Ffluoromisonidazole) and MRI. T1, T2, dynamic-enhanced, and diffusion-weighted sequences were performed before, during, and after radiotherapy. In 7 patients, disease recurred. Disease-free survival correlated negatively with the maximum tissue-to-blood 18F-fluoromisonidazole ratio (T/ B[max]) on the baseline 18F-fluoromisonidazole scan (P ⫽ .04), with the size of the initial hypoxic volume (P ⫽ .04), and with T/B(max) on the 18F-fluoromisonidazole scan during treatment (P ⫽ .02). All locoregional recurrences were within the 18F-FDG–avid regions on the baseline 18F-FDG PET; 3 recurrences mapped outside the hypoxic volume on the baseline 18F-fluoromisonidazole PET. Lesions that recurred had lower apparent diffusion coefficients and faster contrast enhancement than nonrecurrent lesions.50 There are other hypoxia-imaging agents under study. Fluoroazomycin arabinoside has been evaluated to some extent and appears to have higher target/background ratios than seen with FMISO. Although the agent is promising, it has only had limited evaluation.51 Similarly, hypoxia agents like 18F-EF5 (Fig. 4) are under study and may, if localization is sufficiently rapid, prove useful for mapping hypoxia.52 The ease of synthesis of such agents varies, and compounds that are difficult to synthesize may be impractical to use widely in clinical trials. Another promising agent that was developed to image hypoxia is copper (diacetyl-bis (N4-methylthiosemi carbazone)) [Cu ATSM]. Copper radioisotopes offer several possible advantages, including the availability of several radioactive copper isotopes with varying half-lives, including 64Cu (12.7-hour half life), which may make it suitable for test/ retest or multitracer studies.53 60Cu and 62Cu have been used in imaging. Chao et al53 showed the feasibility of planning head and neck cancer radiation therapy using Cu ATSM several years ago and showed different radiation distributions than anatomy based plans.54 The potential to identify hypoxic tumors and modify changes in tumor oxygenation with hyperthermia have also been reported in preclinical models using Cu ATSM (“flow and hypoxia”) and copper per-pyruvaldehyde-bis (N4methyl thio semicarbazone) [Cu PTSM] (“flow”) normalizations.55 Because the Cu agents may have some flow dependence, additional clinical verification studies are needed. However, the images obtained with Cu ATSM agents have been of very high quality, and this agent appears to allow for easier separation of tumor from background activity than has been seen with F-18 fluoromisonidazole. Which will be more predictive of outcomes is uncertain.

Receptors and Tumor Antigens SPECT agents specific for cancers are limited. One such agent is 111In-Octreoscan (Indium-III-Pentetreotide, Mallinckrudt Medical, St. Louis, MO) (for neuroendocrine tumors). An-

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Figure 4 FDG (left panel) versus EF5 (right panel) images of cancer in the neck. Note that the hypoxic volume on the EF5 scan is considerably smaller than the glycolytic volume identified on FDG PET. (Courtesy of Professor Heikki Minn, Turku U PET Center, Turku, Finland.)

other is 111In capromab pendetide (ProstaScint, EUSA Pharma, Oxford, UK). The latter has been used in some settings to attempt to boost the dose to prostate cancers.56 This agent suffers from relatively poor accuracy for the detection of prostate cancer. A 68Ga-labeled PET agent (68Ga-DOTA-toc) for imaging somatostatin receptor–positive neuroendocrine tumors has been developed and appears to be superior to 111In-pentetreotide. This agent has been applied for planning the radiation dose to meningiomas, which can be somatostatin receptor positive. Integration of the DOTA-Tyr3-octreotide (DOTATOC) data led to additional information concerning tumor extension in 17 of 26 patients (65%). Overall, the GTV MRI/CT scanning was larger than the GTV PET in 10 patients (38%), smaller in 13 patients (50%), and almost the same in 3 patients (12%). Most of the adaptations were performed in close vicinity to bony skull base structures or after complex surgery. The median GTV based on MRI was 18.1 mL; the median GTV based on PET was 25.3 mL.57

Normal-Tissue Assessments The knowledge of the location of normal tissues is typically provided by anatomic imaging, but the functional status of normal tissues can be best depicted by functional imaging. As an example, the path of therapeutic beams through the lungs to treat intrathoracic tumors should ideally spare normal lung tissue. Total sparing of normal lung from radiation may be impossible. However, the least functional areas may be selected to optimize beam entry/exits paths for external beam treatment planning. Irradiation of already damaged lung

would not be expected to cause deteriorations in function to as great an extent as seen when normal lung tissue is irradiated. This determination can be made for example using SPECT imaging with 99mTc- (99mTc-MAA [macro aggregated albumin]) perfusion imaging. Such approaches are increasingly possible using more widely available SPECT/CT technology.58 Similarly, agents like 99mTc-HIDA (hepatic iminodiacetic acid) and 99mTc-sulfur colloid depict function of the liver cells or the hepatic reticulo endothelial cells (immobilized macrophages). Other agents with 99mT-DMSA and 99mTc-MAG3 show renal function. Such agents have the potential to monitor normal tissue function in vivo but have not been extensively explored for RT planning. Myocardial perfusion and function can be measured using both single-photon and PET tracers if the heart is to be irradiated.

Practical Issues For MIGRT to be successful, identification and localization of the relevant tumor characteristics is essential. For FDG PET, the goal would typically be identification of the entire tumor with a good match between the size of the BTV on FDG PET with the true tumor size at histology. For other processes, an accurate match between the extent of the biological process, such as the true hypoxic fraction and the hypoxic volume defined by imaging would be highly desirable. A large literature has been developed addressing the issue of determining lesion volume from molecular imaging. In early studies, CT scan volumes were compared with FDG PET volumes in untreated lung cancers;59 the tumor size on

R.L. Wahl, J.M. Herman, and E. Ford

96 PET best matched CT tumor volumes when a region of interest was drawn that was 3␴ above the mean of the normal lung FDG SUV. This approach worked well in untreated tumors (r ⫽ .98) except when there was collapse of the lung distal to the tumor, enlarging the CT volume or in treated tumors. In such instances, the CT-defined volume was, expectedly, higher than the PET-defined volume. In other studies of lung cancer, it was clearly shown that a single SUV cutoff for FDG PET did not result in a reliable estimation of tumor volume relative to the tumor volume from CT scanning. Both SUV cutoffs of 20% and 40% of the maximum SUV value resulted in underestimation of the tumor size versus CT scanning.60 Considerable efforts have been undertaken to precisely match the SUV cutoff for FDG PET (as a percentage of the max single pixel SUV) with tumor size determined at pathology. “Intelligent contouring” in which anatomic and functional cutoff data are used has been shown to be quite reproducible by 1 group.61 A simple numeric SUV cutoff is unlikely to be adequate to define tumor edges because the normal SUV for FDG PET can vary based on patient body habitus. The normal tissue SUV is higher in obese patients than nonobese ones and in specific tumor types. Daisne and Beriwal62 examined PET versus pathology GTV estimates and found a very good correlation at specific threshold settings. It is improbable that PET will fully replace CT scanning or MRI in defining GTV. Rather, an approach in which the anatomic and functional data are combined, with an overlap of the varying volumes (which are substantially correlated), with possible intensification of dose in the BTV of relevance, will be an evolving approach to delineation.62 Varying characteristics must apply to each radiotracer to optimize thresholding, and it is possible that techniques like kinetic modeling may be useful with some tracers or even FDG.63 Also worth noting is that different tracers will likely require different approaches to accurately extract tumor boundaries. FDG and FLT, for example, require different cutoff values to estimate tumor size properly. This will clearly differ based on tumor size, tumor SUV, and normal tissue SUV (tumor/background uptake ratios) with a lower threshold for FLT- than FDG-based volume estimates.41 A variety of technical issues are present in treatment planning of lung cancer with any tracer. Registration between PET and CT scanning may not be perfect because of respiratory motion. The quantitation of exact SUV in tumors may be altered, and inconsistencies because of motion can affect attenuation maps.64 The size of lesions on PET may be larger than on CT scans because of respiratory effects of blurring during the relatively long duration PET acquisition, which entails multiple respiratory cycles, in contrast to a breathhold CT scan. Respiratory gating has been applied to help deal with such inconsistencies. If respiratory gating is used with PET, additional radiation dose may occur from additional CT scans required for attenuation correction and additional time for acquisition of images. How to best determine tumor volume from PET remains in evaluation, but real mismatches in lesion size can occur because of the movement of tumors and organs during respiration, which is most important for lesions in the upper abdomen and thorax. It is

notable that misregistrations between CT locations of lesions in the thorax on CT scans versus PET are greatest in the lower lung fields but can be 6 mm to 1 cm or more depending on the location.

Pitfalls With FDG and Other Tracers Although our radiotracers have exquisite specificity for a biological process, the biological process imaged may not always be cancer. Neither FDG nor FLT are tumor specific. Nonspecific uptake has been seen with virtually all PET tracers.40 This may be a particular problem in the post–radiation therapy setting where one observes the infiltration of FDGavid macrophages.65 Alternative tracers may represent an advantage in this regard. It is also well known that active infections, reactive lymph nodes, tuberculosis, sarcoidosis, and active inflammation can accumulate FDG. Reactive, nontumor involved, lymph nodes can also have intense accumulation of FLT.40 In addition, there can be transient flares in the extent of tracer accumulation after radiation with increases and decreases in FDG because of the recovery process.

Inflammation We have taken advantage of the increased FDG uptake in inflammation after surgery to test plans of breast cancer radiation therapy boosts based on the inflammation remaining at the resection site post treatment. Such approaches may be helpful but draw from the non–tumor-specific uptake of the tracer.66 However, it is critically important that the normal uptake pattern and the nonmalignant uptake patterns of radiotracers be known before a specific region of tracer avidity is treated as a malignant process.

Practical Issues in the Translational Process From Bench to Bedside The table of PET and SPECT imaging agents (Table 1) shows many possibilities for integrating PET and SPECT imaging into radiation therapy planning or monitoring, but many of the agents listed are not Food and Drug Administration approved. Some are under investigation under National Cancer Institute-sponsored Investigational New Drug or individual institution approvals. Others are in use in Europe or Asia/ Oceania but not in the United States. Radiopharmaceuticals have limited risks, but the regulatory structure is complex and treats the agents very similarly to therapeutic agents although such agents are typically given in “microdoses.” The cost of securing regulatory approval of the agents for clinical practice can be substantial although it is typically lower than that for therapeutic drugs. Center for Medicare Services payments in the United States for diagnostic imaging agents are much less than for therapeutics. A major problem lies in integrating advanced imaging into

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Table 1 Molecular Imaging Tools With Potential in MIGRT (Selected Radiopharmaceuticals) Agent

Target

Class

Comments

PET SPECT SPECT PET

PET

Viable tumor volume, inflammation Lower energy, long half-life, poor dosimetry Long half-life, poor dosimetry 20-min half-life of C11 limits availability images many tumor types Applied in brain tumor imaging, especially in Europe Investigational. Promise in prostate cancer

SPECT

Applied in brain tumor imaging

PET PET PET PET PET PET PET PET SPECT PET SPECT PET PET SPECT PET SPECT SPECT PET SPECT

Prostate cancer, hepatomas, etc Prostate cancer, many cancer types Lung and rapid proliferative tumor

F-18 Fluoromisonidazole Cu-62, Cu-64 Co-60 ATSM F-18 EF5 ([18F]fluoroazomycin-arabinoside [FAZA]) I-123 AZA F-18 ML-10 Tc99m annexin V 18-F RGD binding peptides Ga-68 RGD binding peptides Tc99m RGD binding peptides O-15 water Tc99m MIBI* In-111 pentetreotide (Octreoscan-tm)* Ga-68 Dota TOC/TATE/NOC I-123, I-131 MIBG*

Glucose metabolism, viable tumor Perfusion, viability General tumor marker Lymphomas including HD General tumor marker Amino acid transport General tumor marker Amino acid transport General tumor marker Amino acid transport General tumor marker Amino acid transport Membrane synthesis, non–FDG-avid tumors Membrane synthesis, choline metabolism Proliferation tumors Proliferation tumors Hypoxia Hypoxia Hypoxia Hypoxia Hypoxia Apoptosis Apoptosis Neovasculature Neovasculature Neovasculature Blood flow Flow and mitochondrial status Somatostatin receptors Somatostatin receptors NET-2 transport & vesicular storage

I-124 MIBG, C-11 HED 18F FDOPA I-131,* I-123* I-124 In-111 capromab Pendetide (Prostascint)* I-123 PSMA binding ligands F-18 and Ga 68 PSMA binders Tc99m MDP,* HEDP* 18F NaF

NET-2 transport & vesicular storage APUD Iodine transport & retention Iodine transport & retention Intracellular PSMA Extracellular PSMA Extracellular PSMA Bone remodeling Bone remodeling

PET PET SPECT PET SPECT SPECT PET SPECT PET

Tc99m MAA* Tc99m sulfur colloid* Tc99m MAG3,* DMSA* Tc-99m mebrofeninin,* disofenin*

Lung perfusion, IA liver perfusion Liver macrophage function Kidney function and morphology Liver function and excretion

SPECT SPECT SPECT SPECT

18F-FDG* Thallium 201* Gallium 67 citrate* C-11 L-methionine F-18 fluoroethyl tyrosine (FET) F-18 F-anti–ACBC I-123 alpha methyl tyrosine C-11 acetate C-11 choline and F-18 choline analogs C-11 thymidine, fluorothymidine (FLT)

PET

Hypoxic tumors Hypoxic tumors Hypoxic tumors Hypoxic tumors Hypoxic tumors Apoptosis in tumors Apoptosis in tumors ␣ v ␤ 3 integrins ␣ v ␤ 3 integrins ␣ v ␤ 3 integrins Requires cyclotron, 2 minute half life Sarcomas, breast cancer, drug resistance? Neuroendocrine tumors, SSTR2ⴙ Neuroendocrine tumors, SSTR2 Pheo, paragangliomas, neuroblastomas Pheo, paragangliomas, neuroblastoma Brain tumors and neuroendocrine tumors Thyroid cancer, reporter gene Thyroid cancer Prostate ca staging, recurrence Prostate cancer Prostate cancer Bone metastases Bone metastases Delineation of normal and abnormal lung or other tissues if IA delivery Functional/anatomic mapping Functional/anatomic mapping Functional mapping of hepatocytes/ducts

*A Food and Drug Administration–approved radiopharmaceutical in the United States. It is clear that a minority of potentially useful

radiopharmaceuticals are approved by the US Food and Drug Administration. Such agents are in use in other countries and in research settings in the United States. Some PET tracers are United States Pharmacopeia (USP) approved, such as C-11 methionine, NaF, C-11 acetate, and O-15 water, and may be available at some centers. Note that agents labeled “SPECT” can often be imaged with planar gamma camera imaging in addition to SPECT.

radiation therapy and practice. Some of the agents are not patent protected. Several promising agents have very short half-lives (C-11, 20 minutes). The more disease specific the targeting, the smaller the market is. Clearly, many scientifically interesting agents may have a compelling but limited diagnostic utility and may be difficult to translate into practice because the marketplace is small and payments may be low, making commercial risks high. Indeed, except for FDG, which is very widely used, no other PET drug for oncology is in widespread clinical use. NaF, a bone-imaging agent, has the greatest potential because it is generally more accurate than 99mTc-MDP (methylene diphosphonate) and has a projected broad market but as a generic has no clear sponsor. These challenges, plus US Food and Drug Administration regulations on “manufacturing” PET agents, which will go

into effect in late 2011, may make the translation process for new agents to practice a difficult and arduous one. For many of the agents listed in Table 1, no clinical trials of PET in treatment planning have yet been reported. In addition, fee codes in the United States for radiation treatment planning PET studies are lacking. Despite these considerable barriers, the dramatic impact of FDG PET on oncologic imaging and now on radiation therapy planning suggest other MIGRT approaches will emerge and be successful.

Emerging Opportunities The use of PET/CT scans to generate initial therapeutic plans has been evaluated in a variety of tumor types, with lung, head and neck, and brain tumors highlighted in this discus-

R.L. Wahl, J.M. Herman, and E. Ford

98 sion although clearly extending broadly across many types of tumor, including pancreas.67 The use of PET/CT of molecular imaging characteristics to modify the treatment course, either by changing the fields or altering the dose, has been considered by several groups, including a recent review on head and neck carcinoma.68 Similarly, the data in cervical cancer have been promising for identifying high-risk groups who can benefit from response adaptation.24 Recent proposed response assessment criteria, like positron emission response criteria in solid tumor, for PET may add greater order to the complexities of estimating tumor response and be useful for adaptation of response as well.69 The concepts that have been outlined by several groups for adaptive planning have been implemented in a clinical trial. In a group of 21 head and neck cancer patients, IMRT and “dose painting by numbers (DPBNs)” was implemented in patients with head and neck cancer. Each patient’s treatment used 3 separate treatment plans: fractions 1-10 used the baseline DPBN18 FDG-PET]) voxel intensity-based IMRT plan based on a pretreatment 18F-FDG-PET/CT scan; fractions 11-20 used a plan based on a 18F-FDG-PET/CT scan acquired after the eighth fraction; and fractions 21-32 used a conventional (uniform dose) IMRT plan. In a phase I trial, 2 dose prescription levels were tested: a median dose of 80.9 Gy to the high-dose clinical target volume (CTV [high-dose] (dose level I) and a median dose of 85.9 Gy to the GTV (dose level II. Treatment adaptation (ie, plans based on the second 18FFDG-PET/CT scan) reduced the volumes for the GTV (41%, P ⫽ .01), CTV (high dose) (18%, P ⫽ .01), high-dose planning target volume (14%, P ⫽ .02), and parotids (9-12%, P ⬍ .05). Because the GTV was much smaller than the CTV (high dose) and target adaptation, further dose escalation at dose level II resulted in less severe toxicity than that observed at dose level I.70 This study, although small, is an example of how PET/CT MIGRT can be implemented. Clearly, larger studies are needed to determine the clinical impact of the approach, especially as it relates to tumor control.

Dose Verification for High-Energy Particle Therapies Particle beam therapies are seeing increased clinical and research applications. Although such therapy systems can be very expensive, they can offer unique advantages over photon-based IMRT techniques by virtue of the Bragg peak energy deposition profile, which can allow the dose to be delivered in very close proximity to critical structures (eg, the spinal cord or optic nerve). By virtue of the high-energy protons or carbon particles interacting with normal tissues, in vivo activation of tissue can be achieved resulting in the irradiated tissue becoming transiently a positron emitter. This approach has the potential for dose verification in vivo in a manner not previously available. Short-lived radiotracers, such as 15O (half-life 109 seconds) and 11C, may disappear promptly because of their very short half-life and flow dependence. Studies in normal mus-

cle have shown that about two thirds of radioactivity generated by heavy ion beams washes out with a 4-minute half-life; however, there is a longer-lived component useful for dose deposition verification.71,72 Other longer-lived tracers may be generated and in tissues with low blood flow, such as bone, a more durable signal can be achieved. A variety of recent reports have shown the feasibility of using the in vivo activation of positrons as an imaging tool for verification of the dose distribution.73

Conclusions FDG PET/CT has revolutionized the diagnosis and follow-up of patients with cancer and is increasingly the test of choice for cancer imaging. The technology also is being more widely applied in radiation oncology to avoid geographic misses, intensify dose to high-risk areas, decrease dose to low-risk areas, and hopefully improve therapeutic outcomes. A range of additional PET and some SPECT tracers are emerging, which may also have a considerable impact in radiation oncology by portraying, at relatively low resolution, tumor proliferation, hypoxia, non–FDG-avid tumors as well as in displaying normal tissue function. Hybrid functional imaging may help guide dose intensification, dose painting, and adaptive trial designs in which additional intensification/ deintensification is performed based on key imaging findings and treatment response to chemotherapeutic and/or targeted agents. Major challenges remain in this evolving field, including how to precisely define tumor margins, especially for tracers with lower tumor uptake, ensuring registration of CT and molecular imaging data remain precise, issues of variability over the course of many days of radiation treatment, and exactly how to use these powerful data to guide therapy. Because MIGRT is a rather new area for investigation, it is important that functional imaging and MIGRT are integrated in a systematic manner into clinical trials of radiation oncology, not only for treatment planning but also for diagnosis and response assessment. In an era of constrained resources, studies confirming improved outcomes in patients treated with MIGRT versus standard methods are important and should be performed as the technologies mature.

References 1. Gregoire V, Haustermans K, Geets X, et al: PET-based treatment planning in radiotherapy: A new standard? J Nucl Med 48:68S-77S, 2007 (suppl 1) 2. Ling CC, Humm J, Larson S, et al: Towards multidimensional radiotherapy (MD-CRT): Biological imaging and biological conformality. Int J Radiat Oncol Biol Phys 47:551-560, 2000 3. Ott RJ, Tait D, Flower MA, et al: Treatment planning for 131I-mIBG radiotherapy of neural crest tumours using 124I-mIBG positron emission tomography. Br J Radiol 65:787-791, 1992 4. Ahmadzadehfar H, Sabet A, Biermann K, et al: The significance of 99mTc-MAA SPECT/CT liver perfusion imaging in treatment planning for 90Y-microsphere selective internal radiation treatment. J Nucl Med 51:1206-1212, 2010 5. Dewaraja YK, Schipper MJ, Roberson PL, et al: 131I-tositumomab radioimmunotherapy: Initial tumor dose–response results using 3-di-

Promise and pitfalls of PET and SPECT MIGRT

6.

7.

8.

9.

10.

11.

12. 13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

24. 25.

26.

mensional dosimetry including radiobiologic modeling. J Nucl Med 51:1155-1162, 2010 Forster GJ, Engelbach MJ, Brockmann JJ, et al: Preliminary data on biodistribution and dosimetry for therapy planning of somatostatin receptor positive tumours: Comparison of (86)Y-DOTATOC and (111)In-DTPA-octreotide. Eur J Nucl Med 28:1743-1750, 2001 Hobbs RF, Wahl RL, Lodge MA, et al: 124I PET-based 3D–RD dosimetry for a pediatric thyroid cancer patient: Real-time treatment planning and methodologic comparison. J Nucl Med 50:1844-1847, 2009 Nestle U, Weber W, Hentschel M, et al: Biological imaging in radiation therapy: Role of positron emission tomography. Phys Med Biol 54:R125, 2009 Delbeke D, Schoder H, Martin WH, et al: Hybrid imaging (SPECT/CT and PET/CT): Improving therapeutic decisions. Semin Nucl Med 39: 308-340, 2009 Ford EC, Herman J, Yorke E, et al: 18F-FDG PET/CT for image-guided and intensity-modulated radiotherapy. J Nucl Med 50:1655-1665, 2009 Schoder H, Ong SC: Fundamentals of molecular imaging: Rationale and applications with relevance for radiation oncology. Semin Nucl Med 38:119-128, 2008 Delbeke D, Martin WH: Metabolic imaging with FDG: A primer. Cancer J 10:201-213, 2004 Igdem S, Alco G, Ercan T, et al: The application of positron emission tomography/computed tomography in radiation treatment planning: Effect on gross target volume definition and treatment management. Clin Oncol (R Coll Radiol) 22:173-178, 2010 Mac Manus MP, Hicks RJ, Ball DL, et al: F-18 fluorodeoxyglucose positron emission tomography staging in radical radiotherapy candidates with nonsmall cell lung carcinoma: Powerful correlation with survival and high impact on treatment. Cancer 92:886-895, 2001 Aristei C, Falcinelli L, Palumbo B, et al: PET and PET-CT in radiation treatment planning for lung cancer. Expert Rev Anticancer Ther 10: 571-584, 2010 Grosu AL, Nestle U, Weber WA: How to use functional imaging information for radiotherapy planning. Eur J Cancer 45:461-463, 2009 (suppl 1) van Loon J, Offermann C, Bosmans G, et al: 18FDG-PET based radiation planning of mediastinal lymph nodes in limited disease small cell lung cancer changes radiotherapy fields: A planning study. Radiother Oncol 87:49-54, 2008 De Ruysscher D, Wanders S, Minken A, et al: Effects of radiotherapy planning with a dedicated combined PET-CT-simulator of patients with non-small cell lung cancer on dose limiting normal tissues and radiation dose-escalation: A planning study. Radiother Oncol 77:5-10, 2005 Kalff V, Hicks RJ, MacManus MP, et al: Clinical impact of (18)F fluorodeoxyglucose positron emission tomography in patients with nonsmall-cell lung cancer: A prospective study. J Clin Oncol 19:111-118, 2001 Macmanus M, D’Costa I, Everitt S, et al: Comparison of CT and positron emission tomography/CT coregistered images in planning radical radiotherapy in patients with non-small-cell lung cancer. Australas Radiol 51:386-393, 2007 Connell CA, Corry J, Milner AD, et al: Clinical impact of, and prognostic stratification by, F-18 FDG PET/CT in head and neck mucosal squamous cell carcinoma. Head Neck 29:986-995, 2007 Aerts HJ, van Baardwijk AA, Petit SF, et al: Identification of residual metabolic-active areas within individual NSCLC tumours using a preradiotherapy, 18fluorodeoxyglucose-PET-CT scan. Radiother Oncol 91:386-392, 2009 Corry J, Rischin D, Hicks RJ, et al: The role of PET-CT in the management of patients with advanced cancer of the head and neck. Curr Oncol Rep 10:149-155, 2008 Grigsby PW: PET/CT imaging to guide cervical cancer therapy. Futures Oncol 5:953-958, 2009 Lin LL, Mutic S, Low DA, et al: Adaptive brachytherapy treatment planning for cervical cancer using FDG-PET. Int J Radiat Oncol Biol Phys 67:91-96, 2007 Esthappan J, Chaudhari S, Santanam L, et al: Prospective clinical trial of

99

27.

28.

29.

30.

31.

32.

33. 34. 35.

36.

37.

38.

39.

40.

41.

42. 43.

44.

45.

46.

positron emission tomography/computed tomography image-guided intensity-modulated radiation therapy for cervical carcinoma with positive para-aortic lymph nodes. Int J Radiat Oncol Biol Phys 72: 1134-1139, 2008 Schwarz JK, Lin LL, Siegel BA, et al: 18-F-fluorodeoxyglucose-positron emission tomography evaluation of early metabolic response during radiation therapy for cervical cancer. Int J Radiat Oncol Biol Phys 72: 1502-1507, 2008 Winton E, Heriot AG, Ng M, et al: The impact of 18-fluorodeoxyglucose positron emission tomography on the staging, management and outcome of anal cancer. Br J Cancer 100:693-700, 2009 Kim PJ, Hicks RJ, Wirth A, et al: Impact of 18F-fluorodeoxyglucose positron emission tomography before and after definitive radiation therapy in patients with apparently solitary plasmacytoma. Int J Radiat Oncol Biol Phys 74:740-746, 2009 Lee JW, Mac Manus M, Hogg A, et al: Clinical influence of 18F-fluorodeoxyglucose positron emission tomography on the management of primary tumours of the thymus. J Med Imaging Radiat Oncol 52:25461, 2008 Leong T, Everitt C, Yuen K, et al: A prospective study to evaluate the impact of FDG-PET on CT-based radiotherapy treatment planning for oesophageal cancer. Radiother Oncol 78:254-261, 2006 Mai SK, Welzel G, Hermann B, et al: Can the radiation dose to CTenlarged but FDG-PET-negative inguinal lymph nodes in anal cancer be reduced? Strahlenther Onkol 185:254-259, 2009 Jerusalem G, Hustinx R, Beguin Y, et al: Positron emission tomography imaging for lymphoma. Curr Opin Oncol 17:441-445, 2005 Weckesser M, Matheja P, Rickert C, et al: Evaluation of the extension of cerebral gliomas by scintigraphy. Strahlenther Onkol 176:180-185, 2000 Weber DC, Casanova N, Zilli T, et al: Recurrence pattern after [(18)F]fluoroethyltyrosine-positron emission tomography-guided radiotherapy for high-grade glioma: A prospective study. Radiother Oncol 93:586-592, 2009 Grosu AL, Weber WA, Riedel E, et al: L-(methyl-11C) methionine positron emission tomography for target delineation in resected high-grade gliomas before radiotherapy. Int J Radiat Oncol Biol Phys 63:64-74, 2005 Pinkawa M, Attieh C, Piroth MD, et al: Dose-escalation using intensitymodulated radiotherapy for prostate cancer—Evaluation of the dose distribution with and without 18F-choline PET-CT detected simultaneous integrated boost. Radiother Oncol 93:213-219, 2009 Niyazi M, Bartenstein P, Belka C, et al: Choline PET based dose-painting in prostate cancer—Modelling of dose effects. Radiol Oncol 5:23, 2010 Sun A, Sorensen J, Karlsson M, et al: 1-[11C]-acetate PET imaging in head and neck cancer—A comparison with 18F-FDG-PET: Implications for staging and radiotherapy planning. Eur J Nucl Med Mol Imaging 34:651-657, 2007 Troost EG, Vogel WV, Merkx MA, et al: 18F-FLT PET does not discriminate between reactive and metastatic lymph nodes in primary head and neck cancer patients. J Nucl Med 48:726-735, 2007 Han D, Yu J, Yu Y, et al: Comparison of (18)F-fluorothymidine and (18)F-fluorodeoxyglucose PET/CT in delineating gross tumor volume by optimal threshold in patients with squamous cell carcinoma of thoracic esophagus. Int J Radiat Oncol Biol 76:1235-1241, 2010 Wang JZ, Li XA: Impact of tumor repopulation on radiotherapy planning. Int J Radiat Oncol Biol Phys 61:220-227, 2005 Chapman JD, Engelhardt EL, Stobbe CC, et al: Measuring hypoxia and predicting tumor radioresistance with nuclear medicine assays. Radiother Oncol 46:229-237, 1998 Rajendran JG, Hendrickson KR, Spence AM, et al: Hypoxia imagingdirected radiation treatment planning. Eur J Nucl Med Mol Imaging 33:44-53, 2006 (suppl 1) Troost EG, Laverman P, Philippens ME, et al: Correlation of [18F]FMISO autoradiography and pimonidazole [corrected] immunohistochemistry in human head and neck carcinoma xenografts. Eur J Nucl Med Mol Imaging 35:1803-1811, 2008 Lin Z, Mechalakos J, Nehmeh S, et al: The influence of changes in tumor hypoxia on dose-painting treatment plans based on 18F-FMISO positron emission tomography. Int J Radiat Oncol Biol Phys 70:1219-1228, 2008

R.L. Wahl, J.M. Herman, and E. Ford

100 47. Thorwarth D, Eschmann SM, Holzner F, et al: Combined uptake of [18F]FDG and [18F]FMISO correlates with radiation therapy outcome in head-and-neck cancer patients. Radiother Oncol 80:151-156, 2006 48. Thorwarth D, Eschmann SM, Paulsen F, et al: Hypoxia dose painting by numbers: A planning study. Int J Radiat Oncol Biol Phys 68:291-300, 2007 49. Lee NY, Mechalakos JG, Nehmeh S, et al: Fluorine-18-labeled fluoromisonidazole positron emission and computed tomography-guided intensity-modulated radiotherapy for head and neck cancer: A feasibility study. Int J Radiol Oncol J Biol Phys 70:2-13, 2008 50. Dirix P, Vandecaveye V, De Keyzer F, et al: Dose painting in radiotherapy for head and neck squamous cell carcinoma: Value of repeated functional imaging with (18)F-FDG PET, (18)F-fluoromisonidazole PET, diffusion-weighted MRI, and dynamic contrast-enhanced MRI. J Nucl Med 50:1020-7, 2009 51. Grosu AL, Souvatzoglou M, Roper B, et al: Hypoxia imaging with FAZA-PET and theoretical considerations with regard to dose painting for individualization of radiotherapy in patients with head and neck cancer. Int J Radiat Oncol Biol Phys 69:541-551, 2007 52. Basu S, Alavi A: Molecular imaging (PET) of brain tumors. Neuroimaging Clin N Am 19:625-646, 2009 53. Wong TZ, Lacy JL, Petry NA, et al: PET of hypoxia and perfusion with 62Cu-ATSM and 62Cu-PTSM using a 62Zn/62Cu generator. AJR Am J Roentgenol 190:427-432, 2008 54. Chao KS, Bosch WR, Mutic S, et al: A novel approach to overcome hypoxic tumor resistance: Cu-ATSM-guided intensity-modulated radiation therapy. Int J Radiat Oncol Biol Phys 49:1171-1182, 2001 55. Myerson RJ, Singh AK, Bigott HM, et al: Monitoring the effect of mild hyperthermia on tumour hypoxia by Cu-ATSM PET scanning. Int J Hyperthermia 22:93-115, 2006 56. Ellis RJ, Kaminsky DA: Fused radioimmunoscintigraphy for treatment planning. Rev Urol 8:S11-S19, 2006 (suppl 1) 57. Gehler B, Paulsen F, Oksuz MO, et al: [68Ga]-DOTATOC-PET/CT for meningioma IMRT treatment planning. Radiol Oncol 4:56, 2009 58. McGuire SM, Zhou S, Marks LB, et al: A methodology for using SPECT to reduce intensity-modulated radiation therapy (IMRT) dose to functioning lung. Int J Radiat Oncol Biol Phys 66:1543-1552, 2006 59. Zasadny KR, Kison PV, Francis IR, et al: FDG PET determination of metabolically active tumor volume and comparison with CT. Clin Positron Imaging 1:123-129, 1998 60. Biehl KJ, Kong FM, Dehdashti F, et al: 18F-FDG PET definition of gross tumor volume for radiotherapy of non-small cell lung cancer: Is a single

61.

62. 63.

64.

65.

66.

67.

68.

69.

70.

71.

72.

73.

standardized uptake value threshold approach appropriate? J Nucl Med 47:1808-1812, 2006 Bayne M, Hicks RJ, Everitt S, et al: Reproducibility of “intelligent” contouring of gross tumor volume in non-small-cell lung cancer on PET/CT images using a standardized visual method. Int J Radiat Oncol Biol Phys 77:1151-1157, 2010 Gupta T, Beriwal S: PET/CT-guided radiation therapy planning: From present to the future. Indian J Cancer 47:126-133, 2010 Haggstrom I, Johansson L, Larsson A, et al: Semi-automatic tumour segmentation by selective navigation in a three-parameter volume, obtained by voxel-wise kinetic modelling of 11C-acetate. Radiat Protect Dosim 139:214-218, 2010 Nagel CC, Bosmans G, Dekker AL, et al: Phased attenuation correction in respiration correlated computed tomography/positron emitted tomography. Med Phys 33:1840-1847, 2006 Poettgen C, Theegarten D, Eberhardt W, et al: Correlation of PET/CT findings and histopathology after neoadjuvant therapy in non-small cell lung cancer. Oncology 73:316-323, 2007 Ford EC, Lavely WC, Frassica DA, et al: Comparison of FDG-PET/CT and CT for delineation of lumpectomy cavity for partial breast irradiation. Int J Radiat Oncol Biol Phys 71:595-602, 2008 Schellenberg D, Quon A, Minn AY, et al: 18Fluorodeoxyglucose PET is prognostic of progression-free and overall survival in locally advanced pancreas cancer treated with stereotactic radiotherapy. Int J Radiol Oncol J Biol Phys 77:1420-1425, 2010 Wahl RL, Jacene H, Kasamon Y, et al: From RECIST to PERCIST: Evolving considerations for PET response criteria in solid tumors. J Nucl Med 50:122S-150S, 2009 (suppl 1) Bussink J, van Herpen CM, Kaanders JH, et al: PET-CT for response assessment and treatment adaptation in head and neck cancer. Lancet Oncol 2010 11:661-669, 2010 Duprez F, De Neve W, De Gersem W, et al: Adaptive dose painting by numbers for head-and-neck cancer. Int J Radiat Oncol Biol Phys July 17, 2010 [Epub ahead of print] Tomitani T, Pawelke J, Kanazawa M, et al: Washout studies of 11C in rabbit thigh muscle implanted by secondary beams of HIMAC. Phys Med Biol 48:875-889, 2003 Mizuno H, Tomitani T, Kanazawa M, et al: Washout measurement of radioisotope implanted by radioactive beams in the rabbit. Phys Med Biol 48:2269-2281, 2003 Fiedler F, Shakirin G, Skowron J, et al: On the effectiveness of ion range determination from in-beam PET data. Phys Med Biol 55:1989-1998, 2010

Molecular Imaging–Based Dose Painting: A Novel Paradigm for Radiation Therapy Prescription Søren M. Bentzen, PhD, DSc,*,† and Vincent Gregoire, MD, PhD, FRCR*,† Dose painting is the prescription of a nonuniform radiation dose distribution to the target volume based on functional or molecular images shown to indicate the local risk of relapse. Two prototypical strategies for implementing this novel paradigm in radiation oncology are reviewed: subvolume boosting and dose painting by numbers. Subvolume boosting involves the selection of a “target within the target,” defined by image segmentation on the basis of the quantitative information in the image or morphologically, and this is related to image-based target volume selection and delineation. Dose painting by numbers is a voxel-level prescription of dose based on a mathematical transformation of the image intensity of individual pixels. The quantitative use of images to decide both where and how to delivery radiation therapy in an individual case is also called theragnostic imaging. Dose painting targets are imaging surrogates for cellular or microenvironmental phenotypes associated with poor radioresponsiveness. In this review, the focus is on the following positron emission tomography tracers: FDG and choline as surrogates for tumor burden, fluorothymidine as a surrogate for proliferation (or cellular growth fraction) and hypoxiasensitive tracers, including [18F] fluoromisonidazole, EF3, EF5, and 64Cu-labeled copper(II) diacetyl-di(N4-methylthiosemicarbazone) as surrogates of cellular hypoxia. Research advances supporting the clinicobiological rationale for dose painting are reviewed as are studies of the technical feasibility of optimizing and delivering realistic dose painted radiation therapy plans. Challenges and research priorities in this exciting research field are defined and a possible design for a randomized clinical trial of dose painting is presented. Semin Radiat Oncol 21:101-110 © 2011 Elsevier Inc. All rights reserved.

I

maging-based dose painting, the prescription and delivery of a nonuniform dose to the clinical target volume, is a novel paradigm for prescribing radiation therapy.1-3 The basic idea is to replace, completely or in part, the morphologically or anatomically defined target volume with a map of the spatial distribution of a specific tumor phenotype that is hypothesized or has been shown to be related to local tumor control after radiotherapy. A dose prescription function is then used to transform this map into a map of prescribed doses that can be used as input to an inverse planning optimizer. Two prototypical strategies have been considered in

*Departments of Human Oncology, Medical Physics, Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI. †Department of Radiation Oncology, Center for Molecular Imaging and Experimental Radiotherapy, Université Catholique de Louvain, St-Luc University Hospital, Brussels, Belgium. Address reprint requests to Søren M. Bentzen, PhD, DSc, Departments of Human Oncology, Medical Physics, Biostatistics and Medical Informatics, University of Wisconsin, K4/316 Clinical Science Center, 600 Highland Avenue, Madison, WI 53792. E-mail: [email protected]

1053-4296/11/$-see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.semradonc.2010.10.001

the literature: subvolume boosting1 in which an imagingdefined discrete volume is given an additional “boost” radiation dose (in which case the prescription function takes only 2 discrete dose values), or dose painting by numbers2 in which a dose is prescribed at the voxel level and it is left for the dose plan optimizer to arrive at a physically deliverable dose distribution that approximates the desired dose distribution. In the latter case, the prescription function maps a range of image intensities onto a range of doses. Hybrids between the 2 strategies use a series of nested volumes, often about 5 or so, with a prescribed dose assigned to each of them. Theragnostic imaging2 is the application of the quantitative information in biomedical images to produce a prescribed dose map (ie, not just a map of where to treat but ideally also of the local dose fractionation that will optimize tumor control under specified normal-tissue constraints). Dose painting by numbers relies directly on theragnostic imaging, whereas subvolume boosting could include morphologic information as well as image intensities in the definition of the boost volume. However, in practice, there is a contin101

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uum of dose prescription strategies ranging between these 2 ideal cases. In this review, the emphasis is on theragnostic imaging, but large parts of the discussion apply to the case of subvolume boosting as well.

The Case for Nonuniform Dose Distributions in Radiation Therapy Throughout the first half of the history of cancer radiation therapy, the dosimetric challenge was to deliver a sufficiently high dose to the distal aspect of the tumor seen from the direction of the beam. With the advent of megavoltage radiation therapy technology soon after World War II, parallel opposing fields or noncollinear wedged fields allowed the delivery of a near-uniform dose to a fairly large target volume. Target volume selection was generally based on structures identifiable on normal x-rays or during fluoroscopy, often bony landmarks or even on the patient’s external anatomy. These target regions were typically rationalized as “having a high risk of microscopic spread of the disease,” and in general no attempt was made to directly visualize the tumor mass. What this meant in effect was that margins around the macroscopic tumor volume were liberal; setup accuracy and motion management were less of a concern, but this relative robustness came at a price. It was difficult to push the prescribed dose much above 60 Gy in 2-Gy fractions without excessive toxicity because of the large volume of normal tissue irradiated. The development of computed tomography (CT) provided an unprecedented capability to visualize the gross tumor volume (GTV), and this gave the opportunity to test the socalled 3-dimensional hypothesis, namely that by increasing the conformality between the high dose and the target volume, it would be possible to increase local tumor control without increasing (late) toxicity.4 This led to a flurry of phase I, II, and III radiation dose-escalation trials. The broad conclusion of these have been that the dose can be pushed higher to some extent, but that at least for some late endpoints there has been an associated increase in toxicity as well. This raises the question whether more advanced doseescalation strategies are more likely to provide a therapeutic gain? One compelling clinicobiological hypothesis is the dose painting hypothesis, which includes the following: (1) local recurrences arise from cellular or microenvironmental niches that are (relatively) resistant at the radiation dose level that can safely be routinely delivered using a uniform dose distribution; (2) molecular and functional imaging will allow spatiotemporal mapping of these regions of relative radioresistance; and (3) advances in radiation therapy planning and delivery technologies facilitate the delivery of a graded boost to such regions, which, in turn, should lead to improved local tumor control with acceptable side effects. Support for the dose painting hypothesis comes in part from mathematical modeling studies. It has been shown that for a fixed integral dose to a tumor with a uniform spatial

Figure 1 The established clinical causes of local treatment failure after fractionated radiation therapy and selected PET tracers of interest as surrogates for these phenotypes.

radiosensitivity distribution, delivering a uniform dose of radiation will maximize the tumor control probability.5-7 However, for a nonuniform radiosensitivity distribution, a uniform dose distribution is inferior to a distribution that delivers a relatively higher proportion of the integral tumor dose to the more resistant regions of the tumor8 (ie, by dose painting). Numerous modeling studies evaluating dose painting strategies under various radiobiological assumptions have consistently shown large gains in tumor control probability, in the order of 50% or more.3,8-13 It should be emphasized that none of these modeling exercises are assumption free. Proving a clinical gain from dose painting will ultimately require evidence from clinical trials. However, there is a large body of experimental and clinical data providing indirect support for the dose painting hypothesis and thereby informing the design and encouraging the conduct of dose painting trials. Also, advances in preclinical and theoretical research into dose painting provide an increasingly firm base for the rational design of clinical trials that are likely to be informative once they are completed.

Dose Painting Targets The validation of a dose painting target does not necessarily require a mechanistic understanding of the relationship between dose response and expression of the target. It is a sufficient, and from a bioethical standpoint probably also a necessary, condition that an empiric relationship has been shown between target expression and worse local outcome of radiation therapy. The current interest focuses on 3 evidencebased causes of radiation therapy failure in the clinic: tumor burden, tumor cell proliferation, and hypoxia (Fig. 1).

Tumor Burden, Target Volume Definition, and Subvolume Boosting Clinical outcome data show the importance of volume of cancer as a driver of local outcome after radiation therapy. It was hypothesized already in the 1970s that microscopic disease could be sterilized with a lower dose; the rule of thumb

Molecular imaging– based dose painting proposed by Fletcher14 was that roughly two thirds of the dose needed to control gross disease would suffice. At the other end of the scale, bulky disease might need a radiation boost dose of 10 to 15 Gy to produce a reasonable tumor control probability. The volume of cancer hypothesis gave rise to boost field techniques, now an integral part of radiation therapy practice, for instance in postoperative radiotherapy for breast cancer or elective irradiation of regions with suspected subclinical disease to a lower dose level (eg, irradiation of the clinically negative neck in patients with squamous cell carcinoma of the head and neck). Until recently, CT and magnetic resonance imaging (MRI) were the only imaging modalities used to delineate the tumor burden or GTV for radiotherapy planning purposes. With the rising availability of positron emission tomography (PET) scanners, more and more studies have been conducted to assess the added value of functional imaging in radiotherapy planning. Among the available tracers for clinical use, 2-Deoxy-2-(18F)fluoro-D-glucose (FDG) is by far the most widely used. Its availability, its high signal-to-background ratio, and the wide clinical experience accumulated over the years make FDG an attractive tracer for clinical research compared with the other available PET tracers. FDG uptake is commonly considered a good surrogate for tumor cell burden although various parameters influence FDG uptake and retention, such as the rate of glycolysis, tumor perfusion, proliferation, inflammation, and hypoxia.15,16 Encouraging data support the use of FDG-PET in target definition and dose painting. In head and neck squamous cell carcinoma (HNSCC), methodological studies have shown that the use of preradiotherapy FDG-PET led to a better estimate of the true tumor volume, as defined by the pathologic specimens, compared with CT and MRI.17,18 Interestingly, when validated segmentation tools were used, the mean FDG-PET– based GTV was consistently smaller than the GTV defined from morphologic imaging in all investigated tumor locations and at all time points during radiotherapy (Fig. 2).19-21 This finding probably reflects the limited soft-tissue discrimination of morphologic imaging. The reduction of target volumes defined from FDG-PET translated into significant normal tissue sparing with either 3-dimensional CRT or helical intensity-modulated irradiation therapy (IMRT) in “proof-of-concept” HNSCC studies.18,20 This effect was even more impressive when FDG-PET was performed weekly during treatment to allow for dose adaptation as shown in Figure 3. FDG-PET has been used as a dose painting target for subvolume boosting in a phase I study in locally advanced HNSCC.22 This approach was proven to be feasible and safe. De Neve’s group has also shown that FDG avidity could be used as a target for dose painting by numbers, with a clear dosimetric advantage relative to subvolume boosting because of a better capability to create peak-dose regions inside the planning target volume.23 Similar encouraging data have also been reported for non– small-cell lung cancer (NSCLC) where studies have found that FDG-PET volumes were smaller in general than CT-

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Figure 2 A patient with a right-sided T4-N1-M0 hypopharyngeal squamous cell carcinoma receiving concomitant chemoradiotherapy and imaged with intravenous contrast CT, MRI (T2-weighted sequence), and FDG-PET before treatment and at the end of weeks 3 (30 Gy) and 5 (50 Gy). Primary tumor shrinkage is observed with all imaging modalities but is more pronounced with FDG-PET.

defined GTVs, thus reducing the incidental radiation exposure of the healthy lungs and the esophagus sufficiently to allow a radiation dose escalation.24,25 FDG-PET scans also help in tumor delineation in the presence of atelectasis or intratumor heterogeneity and have shown a remarkably good correlation with surgical pathology and patient data.26-28 Limited data are available on GTV delineation and dose adaptation with metabolic tracers other than FDG. In the brain, amino acids, such as methionine and fluoroethyl-Ltyrosine, have been used for GTV delineation in gliomas and for the differentiation between treatment-related changes (pseudoprogression or pseudoregression) and residual/recurrent tumor.29 Some data support the use of methionine and radio-labelled DOTA-D Phe1-Tyr3-Octreotide as PET tracers for GTV delineation in meningiomas and glomus tumors.29 Yet, no data are available on dose adaptation in these tumors. Limited data are available on the use of 11C-choline PET for volume selection and delineation in adenocarcinoma of the prostate. Because of its low sensitivity, choline PET has a limited role in routine clinical practice for defining target volumes in patients with intraprostatic lesions or with metastatic/recurrent disease.30 Yet, there are promising preliminary data on the use of choline-PET for target selection and as a means of guiding high-dose radiation therapy for recurring nodal disease.31 A modeling study found that using choline PET to boost the radiation dose within the prostate resulted in only a modest increase in local control.32 This modest gain stems from the lack of knowledge of the exact PET sensitivity as well as the fractionation sensitivity, quantified by the ␣/␤ ratio, of prostate cancer.33

Proliferation Tumor cell proliferation is a well-established cause of local failure after radiation therapy for many tumor types; it is most convincingly supported by the evidence from randomized

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Figure 3 A patient with a T3-N0-M0 posterior pharyngeal wall squamous cell carcinoma treated with simultaneous integrated boost IMRT. A total dose of 69 Gy (30 fractions of 2.3 Gy in 6 weeks) was prescribed to the primary tumor PTV and 55.5 Gy (30 fractions of 1.85 Gy in 6 weeks) to the prophylactic nodal PTV. Intravenous-contrast CT and FDG-PET were performed before the start of radiotherapy and weekly during treatment. Target volumes and organs at risk were delineated on each CT study. On FDG-PET scans, the GTVs were automatically segmented using a gradientbased algorithm. FDG-PET images were registered on the CT images with a rigid registration algorithm. Dose optimizations were performed at each time point on CT-based and PET-based images. Doses were then added to get the composite dose distribution. Compared with nonadaptive CT planning (classic CT-based), nonadaptive FDG-PET planning (classic PET-based) allows a significant reduction of the high dose volumes (V90-V100). Although an adaptive CT-based plan allowed a greater reduction in V90 to V100 isodose volumes, the largest effect was observed with the adaptive PET-based plan. In all scenarios, the lower isodose volumes were not reduced because of the nonadapted prophylactic irradiation of the nodal PTV.

controlled trials of a benefit from accelerated radiation therapy in HNSCC34 and NSCLC.35 It is likely that patient-topatient variability in accelerated proliferation seen after cytotoxic insults explains at least some of the variability in treatment outcome seen in the clinic.36,37 Intensive research and development has gone into developing 11C-labeled thymidine, which was initially meant for use as an in vivo parallel to the classical tritiated thymidine assay, and several radiolabeled halogenated pyrimidine deoxynucleosides as PET imaging tracers of DNA synthesis. However, rapid biodegradation of these compounds leads to radiolabeled metabolites in the blood, which complicates image analysis and interpretation.38,39 Most current interest centers on another radiofluorinated thymidine analog: 3=-deoxy-3=-[18F] fluorothymidine (FLT). FLT is a terminator of the growing DNA chain and is therefore only incorporated into DNA during synthesis to a very limited extent. However, it is retained in cells after phosphorylation by the thymidine kinase 1 (TK1) enzyme.40 TK1 is differentially expressed in the late G1 and S phases but is practically absent in G0 cells.41 Thus, although FLT is often referred to as a “proliferation tracer,” it actually provides a map of the local growth fraction of tumor cells. Consistent with this, the standardized uptake value of FLT has been shown in several clinical studies to correlate with the cell-cycle–specific Ki-67 index as assessed by immuno-

histochemistry in biopsies.42-48 One negative study failed to detect such a correlation in 8 patients with esophageal cancer.49 However, in addition to the issue of limited statistical power, the range of Ki-67 indexes in the tumors included in this particular study seems atypically narrow and concentrated in the upper end of the range seen in larger series of patients with this disease.50 In addition to the data showing that FLT is an imaging surrogate for the tumor cell growth fraction, there are data from animal studies showing the sensitivity of FLT PET to detect the growth inhibitory response induced by molecular-targeted agents, cytotoxic drugs, or ionizing radiation in mouse tumor models (for a recent review see Barwick et al51) and of androgen ablation in a hormone-sensitive murine prostate tumor model.52 FLT-PET scans at baseline and 2 weeks into fractionated radiotherapy have been used to define targets for subvolume boosting in a recent radiation therapy planning study.53 In the absence of direct clinical evidence for an association between these regions and a subsequent local treatment failure, the biological rationale for this boost strategy is still not completely clear. Part of the concern arises from our current understanding of the biology of accelerated proliferation. There is emerging evidence from experimental and clinical studies that accelerated tumor cell repopulation is an actively controlled biological response to a trauma rather than a sim-

Molecular imaging– based dose painting ple consequence of high proliferative activity at the onset of therapy. A study by Begg et al54 of 476 patients with HNSCC showed that pretreatment cell kinetics was not predictive of the outcome of fractionated radiation therapy. Furthermore, a separate study of the pretreatment tumor cell growth fraction, assessed by the Ki-67 labeling index from immunohistochemistry on biopsies from 402 patients with HNSCC, found this to be inversely associated with a benefit from randomized allocation to strongly accelerated radiation therapy.55 These observations discourage the use of proliferation at baseline as a theragnostic target for dose painting. Redefining the boost volume after 2 weeks of radiation therapy may help overcome this issue, but it is not clear if this is a valid strategy either. In contrast to simple cell kinetics at baseline, a strong expression of the epidermal growth factor receptor (EGFR) was statistically significantly associated with a favorable outcome after randomization to strongly accelerated radiation therapy in 304 patients with HNSCC.56 This finding is consistent with data from the Danish Head and Neck Cancer Study Group57,58 although these studies did not rely exclusively on randomized treatment allocation. Further evidence for the central role of the epidermal growth factor signaling pathway in the response of HNSCC to fractionated radiation therapy comes from the randomized controlled trial showing a clinical benefit from combining radiation therapy with cetuximab, a monoclonal antibody against the EGFR.59 Synthesizing all this evidence, 2 promising avenues for proliferation-targeting dose painting are being pursued. One is to assess the proliferative response to therapy rather than proliferation at baseline as a dose painting target. However, serial FLT scans during a course of therapy should be interpreted with some care. This is in part because of the competition between cell killing and (compensatory) proliferation and in part because FLT uptake requires progression in the cell cycle. Studies have shown that the inhibition of cell cycle progression prevents FLT uptake40 and that FLT uptake in vivo is reduced 24 and 48 hours after single doses of radiation of 8 to 20 Gy,60,61 which is consistent with the well-established mitotic delay after irradiation. Although a shorter delay is to be expected after clinically relevant fraction sizes, the mitotic delay could cause a biased assessment of the tumor cell growth fraction if measured shortly after a dose of radiation. The other avenue is direct PET imaging of a major proliferative response pathway, the obvious prime candidate being the EGFR pathway, at least in cases of HNSCC in which a substantial body of evidence shows that this is a master switch for accelerated repopulation. EGFR expression can be PET imaged using radiolabeled tyrosine kinase inhibitors or antibodies against the extracellular domain of the receptor.62 Also of potential interest in a radiation oncology context is the emerging possibility63 to image constitutively active mutant EGFR using 124I-labeled chimeric antibodies that bind to an EGFR epitope exposed only on mutant, overexpressed, or activated forms of the receptor.

Hypoxia Hypoxia plays a double role as a consequence and as a driver of malignant progression.64 As early as 1936, Mottram at

105 Mount Vernon Hospital in Northwood, England, showed that tumor hypoxia is associated with a poor response to radiation therapy.65 Large clinical studies have confirmed that hypoxia, assessed by polarographic microelectrode measurements of partial oxygen pressure or assessed by immunohistochemistry of hypoxia-related endogenous biomarkers in diagnostic biopsies, is associated with malignant progression and a poor outcome after therapy. The strongest evidence exists for squamous cell carcinoma of the head and neck,66,67 but there is also evidence for several other tumor sites68 in particular prostate69-72 and uterine cervix.73,74 A number of PET tracers have been developed as hypoxia surrogates for imaging, and several new ones are in various stages of preclinical or clinical development.75,76 The 2 tracers with most extensive clinical experience are [18F] fluoromisonidazole (FMISO) and 60Cu-, 61Cu-, or 64Cu-labeled copper(II) diacetyl-di(N4-methylthiosemicarbazone). The overall conclusion from about a dozen correlative clinical studies is that there is a statistically significant association between PET-assessed hypoxia at baseline (ie, before treatment start) and tumor outcome after radiation therapy.13 The development of new hypoxia-sensitive tracers is partly driven by mechanistic considerations of uptake and retention mechanisms. Like FMISO, several of these tracers are [18F]-labeled nitroimidazoles, such as ([18F] fluoroerythronitroimidazole, EF5 [2-(2-nitro-1H-imidazol-1yl)-N-(2,2,3,3,3-[18F] pentafluoropropyl) acetamide], EF3 [2-(2-nitroimidazol-1H-yl)-N-(3,3,3-trifluoropropyl) acetamide], and [18F] fluoroazomycinarabinofuranoside. There are potentially interesting differences between these tracers,77 as an example ([18F] fluoroerythronitroimidazole) and FMISO are more hydrophilic than EF3 or EF5; and there is more non– hypoxia-related bioreduction of FMISO compared with EF3. Some studies in rodent tumor models also show differences from one tumor type to another as well as an influence of the interval from the administration of the tracer to performing the PET scan. It is not clear to what extent these tracers will have clinically relevant differences in performance when used for PET imaging of human tumors in a population of patients. It is likely that there is no consistently superior hypoxia tracer, and it is even possible that some of these tracers could provide complementary information. However, in our view, the potential utility of various tracers for defining dose painting targets can only be established from clinical correlative studies, and it is time to conduct such studies to bring the field forward. As discussed previously, FDG may be a valid dose painting target in itself, but it is not a good imaging surrogate of hypoxia. Clinical studies have found no or very limited correlations between FDG uptake and various PET hypoxia tracers,78 and this is consistent with several small animal tumor studies showing a lack of colocalization between FDG on one hand and 64Cu-labeled copper(II) diacetyl-di(N4-methylthiosemicarbazone),79 [18F] fluoroazomycinarabinofuranoside,80 or EF381 on the other (Fig. 4). One concern in the development of hypoxia-directed dose painting is the spatiotemporal stability of the PET hypoxia map. Already the pioneering FMISO PET study by Koh et

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Figure 4 A comparison between FDG and EF3 for the detection of hypoxia in C3Hf/Kam mouse fibrosarcoma (FSA) and squamous cell carcinoma (SCCVII) under air breathing or 10% oxygen breathing. 14C-EF3 (9.7 MBq) was injected in the tail vein followed 1 hour later by 18F-FDG (17.6 MBq). One hour after the second injection, the mice were killed and processed for autoradiography. The 18FFDG images were obtained after a 45-minute exposure. After a 48-hour rest time allowing for 18F decay, the sections were exposed for 56 hours to obtain the 14C-EF3 distribution.

al82,83 showed PET-detected reoxygenation in some but not all tumors investigated. It remains a research question whether the initially hypoxic region should be boosted or whether the plan should be adapted to change in hypoxic regions. Spatial variability has also been shown in a subset of HNSCC tumors in scan-rescan studies conducted before the onset of radiation therapy.84 However, Lee et al85 found that boosting the dose to the hypoxic subvolume on 1 scan would still lead to a substantial additional dose to the subvolume that was found to be hypoxic on the repeat scan. The magnitude of the required dose to control disease in PET hypoxic regions is not clear. Simplistic back-of-an-envelope estimates based on in vitro oxygen-enhancement ratios are likely to be gross overestimates of the dose required in human tumors. Interestingly in this context, Lee et al86 found excellent locoregional tumor control in a series of 20 patients after standard chemoradiation therapy despite the presence of PETdetected hypoxia in the primary tumor or in positive nodes at baseline or during fractionated therapy in 18 of these cases.

Adaptive Dose Painting Adaptive dose painting aims to use imaging as a biomarker of (local) response, typically derived from repeat imaging during radiotherapy compared with a scan at baseline (ie, before treatment commences). This response map is then used as the input map to adapt the delineated target volume or, in case of dose painting by numbers, the dose distribution so that relatively more dose is applied in regions showing poor response. A few studies have been conducted so far on FDG-PET– based adaptive treatment in NSCLC. A pilot study of 14 cases reported that a FDG-PET scan performed at midtreatment showed a 44% reduction in GTV in 6 patients compared with a reduction of 26% on CT.87 A FDG-PET– based boost dose of 30 to 102 Gy (mean, 58 Gy) or, alternatively, a reduction in

normal tissue complication probability (NTCP) of 0.4% to 3% (mean, 2%), was deemed clinically feasible in 5 of these 6 patients. However, in a similarly designed study, Gillham et al88 concluded that despite tumor shrinkage determined by FDG-PET/CT during treatment, the adaptive-targeting strategy tested in their study would only allow a modest dose escalation. Response-adaptive dose painting immediately raises the following question: when to image? With FDG in HNSCC, the signal is weak after 30 to 40 Gy of chemoradiation and is not visible after 50 Gy. Similar observations have been made for other tracers. It is likely that useful differentiation according to patient-to-patient variability in response to therapy will have to be assessed after 1 to 3 weeks of treatment. Even after 1 week of therapy, the PET signal would conceivably mainly reflect doomed cells. No imaging modality can currently distinguish between viable cells that are doomed or not. However, as mentioned previously, the validation of a dose painting target will come from empiric, clinical evidence rather than from mechanistic considerations also in the case of adaptive dose painting strategies.

Dose Prescription for Dose Painting by Numbers The simplest, reasonable, voxel-based prescription function is a linear interpolation between a minimum dose, Dmin, and a maximum dose Dmax when the voxel image intensity, I, varies between its lower and upper bound, Imin and Imax within the target volume9,89: D(I) ⫽ Dmin ⫹

I ⫺ Imin Imax ⫺ Imin

· (Dmax ⫺ Dmin)

A slightly different formulation was used by Flynn et al90 who proposed a strategy in which a voxel-based boost dose was applied to an anatomically defined target subvolume. The boost was added on top of a uniform dose to the clinical target volume, DCTV, such that the mean boost dose was kept equal to a preset uniform boost dose, Dboost. Using the same notation as above: D(I) ⫽ DCTV ⫹

I Imean

· Dboost

This principle for dose prescription is known as dose redistribution.10 Flynn’s formulation still assumes proportionality between image intensity and (boost) dose, but the advantage is that keeping Dboost constant produces a “natural” control dose plan for a hypothetical trial, delivering a uniform boost of the same size but without image-guided dose painting. Also, Alber et al91 considered a linear transformation of the image intensity into a prescribed dose but with slightly different constraints on the prescribed dose. The optimal mathematical form of the prescription function is unknown92 and will depend on the specific tracer. Ideally, it should be estimated empirically from radiation

Molecular imaging– based dose painting dose-response data as a function of tracer uptake level. Several authors have tried to incorporate mechanistic elements in the prescription function including Thorwarth et al93 who modified the radiosensitivity parameter in a linear quadratic dose-survival curve according to the retention of FMISO and Sovik et al10 who also used an underlying linear quadratic model of cell survival with direct scaling of the ␣ and ␤ parameters using an analytic, approximate, description of the oxygen-enhancement ratio. Although these semimechanistic prescription functions may represent useful starting points for empiric estimation of the optimal prescription function, they are obviously oversimplifications of the more complex multifactorial biology underlying local radioresponsiveness at the voxel level.

Delivery of DosePainted Radiation Therapy Numerous planning studies have shown that subvolume boosting or multilevel dose painting plans can be optimized using commercial software65 and that dose painting by numbers plans can be optimized using research software.90,91,94 The technical feasibility of optimizing linac-based IMRT plans,9,65,93,95,96 helical90,97 and arc photon98 IMRT plans, and intensity-modulated proton therapy plans90,97 has been documented. Several of these studies have compared the relative merits of various delivery platforms, and, as expected, delivery technologies with a high number of degrees of freedom and with intrinsically high spatial resolution do better. Overall, it seems fair to say that success in meeting a given set of dose painting plan objectives is less technology dependent than one would have suspected. Intensity-modulated proton therapy plans may show an advantage for certain configurations of target volumes and critical normal structures, again as one would intuitively expect. Relatively few feasibility studies have taken the crucial step from planning to delivery of realistic dose painting. However, phantom studies by Kissick et al99 have produced encouraging results. Kissick used film dosimetry on a 4-dimensional motion anthropomorphic lung phantom to test the feasibility of helical tomotherapy delivery of a clinically realistic dose painting plan. The authors concluded that delivery was feasible with a relatively modest degradation of the gamma map compared with delivery of the same plan to a static phantom.

Research Priorities: Towards Clinical Implementation Molecular imaging– guided dose painting is a compelling concept. Based on current research, modulation of the radiation dose distribution according to local phenotypic or microenvironmental variations in an individual tumor is likely to be technically feasible at a level of spatial resolution comparable to the voxel size in clinical PET images. In view of the complexity of dose painting strategies, clinical correlative studies and early clinical trials should be initiated and conducted in parallel with continued preclinical research.

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Figure 5 A schematic representation of a proposed phase III trial of dose painting. See text for discussion.

What would clinical trials of dose painting look like? They probably would not be very different from other comparative effectiveness trials. Phase I/II studies of dose painting by FDG-defined subvolume boosting have already been published for patients with HNSCC,22 and further early trials are in progress in this disease and in NSCLC. Preoperative radiation therapy for rectal adenocarcinoma is another interesting indication in which the pathologic response could be used as an early surrogate endpoint for tumor effect. Also, recurrent rectal carcinomas, among many other tumor sites and histologies, have been considered in this context.100 The obvious attraction of FDG-based dose painting is the wide availability and the extensive clinical experience with this tracer. The boost dose escalation trial from De Neve’s group22 enrolled 41 patients with HNSCC between 2003 and 2005, 23 at dose level I (a total physical dose of 72.5 Gy in 32 fractions to the FDG-PET–positive volume) and 18 at dose level II (77.5 Gy in 32 fractions to the PET-positive volume). With a median follow-up for surviving patients of 14 months, 2 cases of dose-limiting grade 4 toxicity occurred at dose level I. A treatment-related death at dose level II led to termination of the trial. Actuarial 1-year local control was 85% and 87% at the 2 dose levels. However, 4 of the 9 recurrences seen at the time of analysis occurred in the FDG-defined boost region, raising the possibility that dose should be escalated further if possible without exceeding the acceptable toxicity. Once the feasibility and safety of the dose painting strategy is established, a phase III trial should be launched with time to locoregional progression and late toxicity as joint primary endpoints (Fig. 5). Recording the pattern of failure and its relation to the PET tracer uptake distribution will be important. Again, HNSCC would be an attractive tumor site because of the importance of local control for long-term disease control. To avoid undertreating patients in any of the trial arms, a conservative approach would be to do a dose-painted boost dose only on top of a standard curative dose to the clinical target volume. A PET/CT scan should be performed in all cases with the investigational PET tracer and dose painting by numbers or to a PET-defined subvolume should be planned. To compare like with like, in each patient, the PETdefined dose painting plan should be matched with a geometrically defined boost plan delivering the same integral dose to the target volume. The actual plan to be used will be decided by randomization. Although modeling has suggested large gains in tumor control from dose painting, the trial

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108 should have a sample size allowing detection of a 15% improvement in locoregional control with 90% power. The trial sample size would be about 450 patients, but an adaptive design could be considered. Clearly, this would need a multicenter or cooperative group format to finish accrual within a reasonable time. Strong data from the early clinical trials will be required to motivate a phase III trial with this sample size and complexity of the intervention. We anticipate that such a trial will be initiated within the next 5 years.

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References 1. Ling CC, Humm J, Larson S, et al: Towards multidimensional radiotherapy (MD-CRT): Biological imaging and biological conformality. Int J Radiat Oncol Biol Phys 47:551-560, 2000 2. Bentzen SM: Theragnostic imaging for radiation oncology: Dosepainting by numbers. Lancet Oncol 6:112-117, 2005 3. Brahme A: Biologically optimized 3-dimensional in vivo predictive assay-based radiation therapy using positron emission tomographycomputerized tomography imaging. Acta Oncol 42:123-136, 2003 4. Lichter AS: Three-dimensional conformal radiation therapy: A testable hypothesis. Int J Radiat Oncol Biol Phys 21:853-855, 1991 5. Webb S, Evans PM, Swindell W, et al: A proof that uniform dose gives the greatest TCP for fixed integral dose in the planning target volume. Phys Med Biol 39:2091-2098, 1994 6. Webb S, Nahum AE: A model for calculating tumour control probability in radiotherapy including the effects of inhomogeneous distributions of dose and clonogenic cell density. Phys Med Biol 38:653666, 1993 7. Levin-Plotnik D, Hamilton RJ: Optimization of tumour control probability for heterogeneous tumours in fractionated radiotherapy treatment protocols. Phys Med Biol 49:407-424, 2004 8. Brahme A, Agren AK: Optimal dose distribution for eradication of heterogeneous tumours. Acta Oncol 26:377-385, 1987 9. Vanderstraeten B, Duthoy W, Gersem WD, et al: [(18)F]fluoro-deoxyglucose positron emission tomography ([(18)F]FDG-PET) voxel intensity-based intensity-modulated radiation therapy (IMRT) for head and neck cancer. Radiother Oncol 79:249-258, 2006 10. Sovik A, Malinen E, Bruland OS, et al: Optimization of tumour control probability in hypoxic tumours by radiation dose redistribution: A modelling study. Phys Med Biol 52:499-513, 2007 11. Stavreva NA, Stavrev PV, Round WH: A mathematical approach to optimizing the radiation dose distribution in heterogeneous tumours. Acta Oncol 35:727-732, 1996 12. Popple RA, Ove R, Shen S: Tumor control probability for selective boosting of hypoxic subvolumes, including the effect of reoxygenation. Int J Radiat Oncol Biol Phys 54:921-927, 2002 13. Bentzen SM: Dose painting and theragnostic imaging: Towards the prescription, planning and delivery of biologically targeted dose distributions in external beam radiation oncology. Cancer Treat Res 139: 41-62:41-62, 2008 14. Fletcher GH: Subclinical disease. Cancer 53:1274-1284, 1984 15. Bos R, Der Hoeven JJ, van der We, et al: Biologic correlates of (18)fluorodeoxyglucose uptake in human breast cancer measured by positron emission tomography. J Clin Oncol 20:379-387, 2002 16. Zhao S, Kuge Y, Mochizuki T, et al: Biologic correlates of intratumoral heterogeneity in 18F-FDG distribution with regional expression of glucose transporters and hexokinase-II in experimental tumor. J Nucl Med 46:675-682, 2005 17. Daisne JF, Duprez T, Weynand B, et al: Tumor volume in pharyngolaryngeal squamous cell carcinoma: Comparison at CT, MR imaging, and FDG PET and validation with surgical specimen. Radiology 233: 93-100, 2004 18. Geets X, Lee JA, Bol A, et al: A gradient-based method for segmenting FDG-PET images: Methodology and validation. Eur J Nucl Med Mol Imaging 34:1427-1438, 2007 19. Geets X, Tomsej M, Lee JA, et al: Adaptive biological image-guided IMRT with anatomic and functional imaging in pharyngo-laryngeal

23.

24.

25.

26.

27.

28.

29. 30.

31.

32.

33. 34.

35.

36. 37. 38. 39.

tumors: Impact on target volume delineation and dose distribution using helical tomotherapy. Radiother Oncol 85:105-115, 2007 Geets X, Daisne JF, Tomsej M, et al: Impact of the type of imaging modality on target volumes delineation and dose distribution in pharyngo-laryngeal squamous cell carcinoma: Comparison between pre- and per-treatment studies. Radiother Oncol 78:291-297, 2006 Dirix P, Vandecaveye V, De KF, et al: Dose painting in radiotherapy for head and neck squamous cell carcinoma: Value of repeated functional imaging with (18)F-FDG PET 18F-fluoromisonidazole PET, diffusion-weighted MRI, and dynamic contrast-enhanced MRI. J Nucl Med 50:1020-1027, 2009 Madani I, Duthoy W, Derie C, et al: Positron emission tomographyguided, focal-dose escalation using intensity-modulated radiotherapy for head and neck cancer. Int J Radiat Oncol Biol Phys 68:126-135, 2007 Vanderstraeten B, Duthoy W, De GW, et al: [18F]fluoro-deoxy-glucose positron emission tomography ([18F]FDG-PET) voxel intensitybased intensity-modulated radiation therapy (IMRT) for head and neck cancer. Radiother Oncol 79:249-258, 2006 De Ruysscher D, Wanders S, Minken A, et al: Effects of radiotherapy planning with a dedicated combined PET-CT-simulator of patients with non-small cell lung cancer on dose limiting normal tissues and radiation dose-escalation: A planning study. Radiother Oncol 77:510, 2005 van der Wel A, Nijsten S, Hochstenbag M, et al: Increased therapeutic ratio by 18FDG-PET CT planning in patients with clinical CT stage N2–N3M0 non-small-cell lung cancer: A modeling study. Int J Radiat Oncol Biol Phys 61:649-655, 2005 Nestle, U, Kremp S, et al: Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-small cell lung cancer. J Nucl Med 46:1342-1348, 2005 Stroom J, Blaauwgeers H, van Baardwijk A, et al: Feasibility of pathology-correlated lung imaging for accurate target definition of lung tumors. Int J Radiat Oncol Biol Phys 69:267-275, 2007 van Baardwijk A, Bosmans G, Boersma L, et al: PET-CT-based autocontouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes. Int J Radiat Oncol Biol Phys 68:771-778, 2007 Grosu AL, Weber WA: PET for radiation treatment planning of brain tumours. Radiother Oncol 96:325-327, 2010 Picchio M, Giovannini E, Crivellaro C, et al: Clinical evidence on PET/CT for radiation therapy planning in prostate cancer. Radiother Oncol 96:347-350, 2010 Rinnab L, Simon J, Hautmann RE, et al: [(11)C]choline PET/CT in prostate cancer patients with biochemical recurrence after radical prostatectomy. World J Urol 27:619-625, 2009 Niyazi M, Bartenstein P, Belka C, et al: Choline PET based dosepainting in prostate cancer—Modelling of dose effects. Radiol Oncol 5:23:23, 2010 Bentzen SM, Ritter MA: The alpha/beta ratio for prostate cancer: What is it, really? Radiother Oncol 76:1-3, 2005 Bourhis J, Overgaard J, Audry H, et al: Hyperfractionated or accelerated radiotherapy in head and neck cancer: A meta-analysis. Lancet 368:843-854, 2006 Saunders M, Dische S, Barrett A, et al: Continuous hyperfractionated accelerated radiotherapy (CHART) versus conventional radiotherapy in non-small-cell lung cancer: A randomised multicentre trial, in CHART Steering Committee. Lancet 350:161-165, 1997 Kim JJ, Tannock IF: Repopulation of cancer cells during therapy: An important cause of treatment failure. Nat Rev Cancer 5:516-525, 2005 Bentzen SM: Repopulation in radiation oncology: Perspectives of clinical research. Int J Radiat Biol 79:581-585, 2003 Gardelle O, Roelcke U, Vontobel P, et al: [76Br]bromodeoxyuridine PET in tumor-bearing animals. Nucl Med Biol 28:51-57, 2001 Christman D, Crawford EJ, Friedkin M, et al: Detection of DNA synthesis in intact organisms with positron-emitting (methyl- 11 C)thymidine. Proc Natl Acad Sci U S A 69:988-992, 1972

Molecular imaging– based dose painting 40. Rasey JS, Grierson JR, Wiens LW, et al: Validation of FLT uptake as a measure of thymidine kinase-1 activity in A549 carcinoma cells. J Nucl Med 43:1210-1217, 2002 41. Munch-Petersen B, Cloos L, Jensen HK, et al: Human thymidine kinase. 1. Regulation in normal and malignant cells. Adv Enzyme Regul 35:69-89:69-89, 1995 42. Yap CS, Czernin J, Fishbein MC, et al: Evaluation of thoracic tumors with 18F-fluorothymidine and 18F-fluorodeoxyglucose-positron emission tomography. Chest 129:393-401, 2006 43. Buck AK, Halter G, Schirrmeister H, et al: Imaging proliferation in lung tumors with PET: 18F-FLT versus 18F-FDG. J Nucl Med 44: 1426-1431, 2003 44. Chen W, Cloughesy T, Kamdar N, et al: Imaging proliferation in brain tumors with 18F-FLT PET: comparison with 18F-FDG. J Nucl Med 46:945-952, 2005 45. Muzi M, Vesselle H, Grierson JR, et al: Kinetic analysis of 3=-deoxy3=-fluorothymidine PET studies: Validation studies in patients with lung cancer. J Nucl Med 46:274-282, 2005 46. Vesselle H, Grierson J, Muzi M, et al: In vivo validation of 3=deoxy3=-[(18)F]fluorothymidine ([(18)F] FLT) as a proliferation imaging tracer in humans: Correlation of [(18)F]FLT uptake by positron emission tomography with Ki-67 immunohistochemistry and flow cytometry in human lung tumors. Clin Cancer Res 8:3315-3323, 2002 47. Yamamoto Y, Nishiyama Y, Ishikawa S, et al: Correlation of 18F-FLT and 18F-FDG uptake on PET with Ki-67 immunohistochemistry in non-small cell lung cancer. Eur J Nucl Med Mol Imaging 34:16101616, 2007 48. Yue J, Chen L, Cabrera AR, et al: Measuring tumor cell proliferation with 18F-FLT PET during radiotherapy of esophageal squamous cell carcinoma: A pilot clinical study. J Nucl Med 51:528-534, 2010 49. van Westreenen HL, Cobben DC, Jager PL, et al: Comparison of 18FFLT PET and 18F-FDG PET in esophageal cancer. J Nucl Med 46:400404, 2005 50. Ikeda G, Isaji S, Chandra B, et al: Prognostic significance of biologic factors in squamous cell carcinoma of the esophagus. Cancer 86: 1396-1405, 1999 51. Barwick T, Bencherif B, Mountz JM, et al: Molecular PET and PET/CT imaging of tumour cell proliferation using F-18 fluoro-L-thymidine: A comprehensive evaluation. Nucl Med Commun 30:908-917, 2009 52. Oyama N, Ponde DE, Dence C, et al: Monitoring of therapy in androgen-dependent prostate tumor model by measuring tumor proliferation. J Nucl Med 45:519-525, 2004 53. Troost EG, Bussink J, Hoffmann AL, et al: 18F-FLT PET/CT for early response monitoring and dose escalation in oropharyngeal tumors. J Nucl Med 51:866-874, 2010 54. Begg AC, Haustermans K, Hart AA, et al: The value of pretreatment cell kinetic parameters as predictors for radiotherapy outcome in head and neck cancer: A multicenter analysis. Radiother Oncol 50:13-23, 1999 55. Wilson GD, Saunders MI, Dische S, et al: Pre-treatment proliferation and the outcome of conventional and accelerated radiotherapy. Eur J Cancer 42:363-371, 2006 56. Bentzen SM, Atasoy BM, Daley FM, et al: Epidermal growth factor receptor expression in pretreatment biopsies from head and neck squamous cell carcinoma as a predictive factor for a benefit from accelerated radiation therapy in a randomized controlled trial. J Clin Oncol 23:5560-5567, 2005 57. Eriksen JG, Steiniche T, Askaa J, et al: The prognostic value of epidermal growth factor receptor is related to tumor differentiation and the overall treatment time of radiotherapy in squamous cell carcinomas of the head and neck. Int J Radiat Oncol Biol Phys 58:561-566, 2004 58. Eriksen JG, Steiniche T, Overgaard J: The influence of epidermal growth factor receptor and tumor differentiation on the response to accelerated radiotherapy of squamous cell carcinoma of the head and neck in the randomized DAHANCA 6 and 7 study. Radiother Oncol 74:93-100, 2005 59. Bonner JA, Harari PM, Giralt J, et al: Radiotherapy plus cetuximab for squamous-cell carcinoma of the head and neck. N Engl J Med 354: 567-578, 2006

109 60. Yang YJ, Ryu JS, Kim SY, et al: Use of 3=-deoxy-3=-[18F]fluorothymidine PET to monitor early responses to radiation therapy in murine SCCVII tumors. Eur J Nucl Med Mol Imaging 33:412-419, 2006 61. Apisarnthanarax S, Alauddin MM, Mourtada F, et al: Early detection of chemoradioresponse in esophageal carcinoma by 3=-deoxy-3=-3Hfluorothymidine using preclinical tumor models. Clin Cancer Res 12:4590-4597, 2006 62. Mishani E, Abourbeh G, Eiblmaier M, et al: Imaging of EGFR and EGFR tyrosine kinase overexpression in tumors by nuclear medicine modalities. Curr Pharm Des 14:2983-2998, 2008 63. Lee FT, O’Keefe GJ, Gan HK, et al: Immuno-PET quantitation of de2-7 epidermal growth factor receptor expression in glioma using 124IIMP-R4-labeled antibody ch806. J Nucl Med 51:967-972, 2010 64. Harris AL: Hypoxia—A key regulatory factor in tumour growth. Nat Rev Cancer 2:38-47, 2002 65. Malinen E, Sovik A, Hristov D, et al: Adapting radiotherapy to hypoxic tumours. Phys Med Biol 51:4903-4921, 2006 66. Nordsmark M, Bentzen SM, Rudat V, et al: Prognostic value of tumor oxygenation in 397 head and neck tumors after primary radiation therapy. An international multi-center study. Radiother Oncol 77:1824, 2005 67. Koukourakis MI, Bentzen SM, Giatromanolaki A, et al: Endogenous Markers of two separate hypoxia response pathways (hypoxia inducible factor 2 alpha and carbonic anhydrase 9) are associated with radiotherapy failure in head and neck cancer patients recruited in the CHART Randomized Trial. J Clin Oncol 24:727-735, 2006 68. Tatum JL, Kelloff GJ, Gillies RJ, et al: Hypoxia: Importance in tumor biology, noninvasive measurement by imaging, and value of its measurement in the management of cancer therapy. Int J Radiat Biol 82:699-757, 2006 69. Movsas B, Chapman JD, Hanlon AL, et al: Hypoxic prostate/muscle pO2 ratio predicts for biochemical failure in patients with prostate cancer: Preliminary findings. Urology 60:634-639, 2002 70. Vergis R, Corbishley CM, Norman AR, et al: Intrinsic markers of tumour hypoxia and angiogenesis in localised prostate cancer and outcome of radical treatment: A retrospective analysis of two randomised radiotherapy trials and one surgical cohort study. Lancet Oncol 9:342-351, 2008 71. Stewart GD, Gray K, Pennington CJ, et al: Analysis of hypoxia-associated gene expression in prostate cancer: Lysyl oxidase and glucose transporter-1 expression correlate with Gleason score. Oncol Rep 20: 1561-1567, 2008 72. Carnell DM, Smith RE, Daley FM, et al: An immunohistochemical assessment of hypoxia in prostate carcinoma using pimonidazole: Implications for radioresistance. Int J Radiat Oncol Biol Phys 65:9199, 2006 73. Hockel M, Schlenger K, Aral B, et al: Association between tumor hypoxia and malignant progression in advanced cancer of the uterine cervix. Cancer Res 56:4509-4515, 1996 74. Fyles AW, Milosevic M, Wong R, et al: Oxygenation predicts radiation response and survival in patients with cervix cancer. Radiother Oncol 48:149-156, 1998 75. Mees G, Dierckx R, Vangestel C, et al: Molecular imaging of hypoxia with radiolabelled agents. Eur J Nucl Med Mol Imaging 36:16741686, 2009 76. Tatum JL, Kelloff GJ, Gillies RJ, et al: Hypoxia: Importance in tumor biology, noninvasive measurement by imaging, and value of its measurement in the management of cancer therapy. Int J Radiat Biol 82:699-757, 2006 77. Mahy P, De Bast M, de Groot T, et al: Comparative pharmacokinetics, biodistribution, metabolism and hypoxia-dependent uptake of [18F]EF3 and [18F]-MISO in rodent tumor models. Radiother Oncol 89: 353-360, 2008 78. Dehdashti F, Grigsby PW, Lewis JS, et al: Assessing tumor hypoxia in cervical cancer by PET with 60Cu-labeled diacetyl-bis(N4-methylthiosemicarbazone). J Nucl Med 49:201-205, 2008 79. Dence CS, Ponde DE, Welch MJ, et al: Autoradiographic and smallanimal PET comparisons between (18)F-FMISO 18F-FDG, (18)F-

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110

80.

81.

82.

83.

84.

85.

86.

87.

88.

89.

FLT and the hypoxic selective (64)Cu-ATSM in a rodent model of cancer. Nucl Med Biol 35:713-720, 2008 Busk M, Horsman MR, Jakobsen S, et al: Cellular uptake of PET tracers of glucose metabolism and hypoxia and their linkage. Eur J Nucl Med Mol Imaging 35:2294-2303, 2008 Christian N, Deheneffe S, Bol A, et al: Is 18F-FDG a surrogate tracer to measure tumor hypoxia? Comparison with the hypoxic tracer (14)CEF3 in animal tumor models. Radiother Oncol 97:183-188, 2010 Koh WJ, Rasey JS, Evans ML, et al: Imaging of hypoxia in human tumors with [F-18]fluoromisonidazole. Int J Radiat Oncol Biol Phys 22:199-212, 1992 Rasey JS, Koh WJ, Evans ML, et al: Quantifying regional hypoxia in human tumors with positron emission tomography of [18F]fluoromisonidazole: A pretherapy study of 37 patients. Int J Radiat Oncol Biol Phys 36:417-428, 1996 Nehmeh SA, Lee NY, Schroder H, et al: Reproducibility of intratumor distribution of (18)F-fluoromisonidazole in head and neck cancer. Int J Radiat Oncol Biol Phys 70:235-242, 2008 Lee NY, Mechalakos JG, Nehmeh S, et al: Fluorine-18-labeled fluoromisonidazole positron emission and computed tomography-guided intensity-modulated radiotherapy for head and neck cancer: A feasibility study. Int J Radiat Oncol Biol Phys 70:2-13, 2008 Lee N, Nehmeh S, Schoder H, et al: Prospective trial incorporating pre-/mid-treatment [18F]-misonidazole positron emission tomography for head-and-neck cancer patients undergoing concurrent chemoradiotherapy. Int J Radiat Oncol Biol Phys 75:101-108, 2009 Feng M, Kong FM, Gross M, et al: Using fluorodeoxyglucose positron emission tomography to assess tumor volume during radiotherapy for non-small-cell lung cancer and its potential impact on adaptive dose escalation and normal tissue sparing. Int J Radiat Oncol Biol Phys 73:1228-1234, 2009 Gillham C, Zips D, Ponisch F, et al: Additional PET/CT in week 5-6 of radiotherapy for patients with stage III non-small cell lung cancer as a means of dose escalation planning? Radiother Oncol 88:335-341, 2008 Das SK, Miften MM, Zhou S, et al: Feasibility of optimizing the dose distribution in lung tumors using fluorine-18-fluorodeoxyglucose

90.

91. 92.

93.

94.

95.

96.

97.

98.

99.

100.

positron emission tomography and single photon emission computed tomography guided dose prescriptions. Med Phys 31:1452-1461, 2004 Flynn R, Barbee D, Bowen S, et al: Dose painting with intensity modulated proton therapy and intensity modulated x-ray therapy: A comparison. Med Phys 34:2522-2523, 2007 Alber M, Paulsen F, Eschmann SM, et al: On biologically conformal boost dose optimization. Phys Med Biol 48:N31-N35, 2003 Bowen S, Flynn R, Bentzen SM, et al: Effect of a biologically based prescription function in IMRT dose optimization. Med Phys 34:2524, 2007 Thorwarth D, Eschmann SM, Paulsen F, et al: Hypoxia dose painting by numbers: A planning study. Int J Radiat Oncol Biol Phys 68:291300, 2007 Rickhey M, Bogner L: Application of the inverse Monte Carlo treatment planning system IKO for an inhomogeneous dose prescription in the sense of dose painting. Z Med Phys 16:307-312, 2006 Sovik A, Malinen E, Skogmo HK, et al: Radiotherapy adapted to spatial and temporal variability in tumor hypoxia. Int J Radiat Oncol Biol Phys 68:1496-1504, 2007 Yang Y, Xing L: Towards biologically conformal radiation therapy (BCRT): Selective IMRT dose escalation under the guidance of spatial biology distribution. Med Phys 32:1473-1484, 2005 Muzik J, Soukup M, Alber M: Comparison of fixed-beam IMRT, helical tomotherapy, and IMPT for selected cases. Med Phys 35:15801592, 2008 Korreman SS, Ulrich S, Bowen S, et al: Feasibility of dose painting using a volumetric modulated arc optimization technique. Acta Oncol 49:964-971, 2010 Kissick MW, Mo X, McCall KC, et al: A phantom model demonstration of tomotherapy dose painting delivery, including managed respiratory motion without motion management. Phys Med Biol 55:29832995, 2010 Jingu K, Ariga H, Kaneta T, et al: Focal dose escalation using FDGPET-guided intensity-modulated radiation therapy boost for postoperative local recurrent rectal cancer: A planning study with comparison of DVH and NTCP. BMC Cancer 10:127, 2010

Functional and Molecular Image Guidance in Radiotherapy Treatment Planning Optimization Shiva K. Das, PhD,* and Randall K. Ten Haken, PhD† Functional and molecular imaging techniques are increasingly being developed and used to quantitatively map the spatial distribution of parameters, such as metabolism, proliferation, hypoxia, perfusion, and ventilation, onto anatomically imaged normal organs and tumor. In radiotherapy optimization, these imaging modalities offer the promise of increased dose sparing to high-functioning subregions of normal organs or dose escalation to selected subregions of the tumor as well as the potential to adapt radiotherapy to functional changes that occur during the course of treatment. The practical use of functional/molecular imaging in radiotherapy optimization must take into cautious consideration several factors whose influences are still not clearly quantified or well understood including patient positioning differences between the planning computed tomography and functional/molecular imaging sessions, image reconstruction parameters and techniques, image registration, target/normal organ functional segmentation, the relationship governing the dose escalation/sparing warranted by the functional/molecular image intensity map, and radiotherapy-induced changes in the image intensity map over the course of treatment. The clinical benefit of functional/molecular image guidance in the form of improved local control or decreased normal organ toxicity has yet to be shown and awaits prospective clinical trials addressing this issue. Semin Radiat Oncol 21:111-118 © 2011 Elsevier Inc. All rights reserved.

T

he integration of anatomic imaging data directly within radiation therapy treatment planning systems is now widespread and in most cases dictates standard practice. Most common are 3-dimensional (3D) x-ray computed tomography (CT) datasets, which are augmented in many cases with magnetic resonance imaging (MRI). Both tumor and normal tissue anatomy delineated (segmented) on these imaging datasets form the backdrop for beam selection, plan optimization, and dose computation and display. These same imaging modalities are most often used to assess the results of treatment (eg, tumor shrinkage and some signs of normal tissue changes) either after or more commonly during the course of treatment. Recent issues of this journal have reviewed the use of anatomic images to guide1 and adapt2 radiation therapy treatments.

*Department of Radiation Oncology, Duke University Medical Center, Durham, NC. †Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, MI. Dr Ten Haken’s research is supported in part by NIH P01 CA59827. Address reprint requests to Randall K. Ten Haken, PhD, Department of Radiation Oncology, University of Michigan, 519 W. William St, Argus 1 Building, Ann Arbor, MI 48103-4943. E-mail: [email protected]

1053-4296/11/$-see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.semradonc.2010.10.002

More recently, advances in the availability and utility of functional and, to some extent, molecular imaging data have led to great interest within the radiation therapy community because of their use in treatment assessment and ultimately their integration into the treatment planning optimization and adaptive delivery processes. These uses generally fall into the areas of identifying regions of tumors for dose intensification (see for example the previous article in this issue) or, as is also now gaining interest, for use in assessing and monitoring the function of normal tissues.3,4 Several recently published articles reviewed the potential of imaging as a biomarker for various mechanisms and processes and for applications in clinical radiotherapy. Some of these review articles5-8 indicate tumor hallmarks, mechanisms, and expression parameters (eg, metabolism, proliferation, hypoxia, apoptosis, angiogenesis/vascularity, and receptor expression) that have potential for study using imaging biomarkers. Other articles9-16 review clinical sites (eg, head and neck, lung, brain, esophagus, cervix, rectum, breast, prostate) in which functional MRI, positron emission tomography (PET), and single photon emission computed tomography (SPECT) have been applied in radiation oncology practice. Recent investigations and reports17-24 address the use of this new type of information in optimization strategies for planning and adapting radiotherapy treatments. 111

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In this article, we review some methodologies used in the planning of functional image– guided therapy, indicate areas now using or soon to use functional imaging for adaptive treatment strategies, summarize some expectations related to dosimetric outcome of this planning, and point out some issues related to the correct use of these data in the planning process. Thus, much of this article makes broad assumptions about the applicability of certain functional and molecular imaging data in the selection of what to treat or what not to treat. Gregoire et al12 point out how the sensitivity and specificity of functional PET imaging might relate to its applicability for target selection in various tumors. Beyond selection lies the additional task of the delineation of targets and normal tissues (or perhaps more meaningful the delineation of subsections of targets and normal tissues) to treat or avoid, respectively. Again, beyond warnings and alerts for concerns, much of this article assumes that both the selection and delineation questions have (to some extent) been validated before use in the planning processes we describe. As such, caution should be applied for the use of these techniques in a nonprotocol setting within the clinic.

Planning Methodologies for Functional Image–Guided Radiotherapy The mapping of the spatial variation of function, as identified with functional imaging, can be used to either spare highfunctioning normal tissue or escalate the dose to hyperactive/ hypoxic regions of the tumor. These aims can be achieved to differing extents based on whether the plan used is 3D conformal or intensity-modulated radiotherapy (IMRT) and also on whether or not beam orientations are selected with this aim in mind. Compared with 3D radiotherapy, IMRT is generally more capable of providing the dose to the tumor via less functional intervening normal tissue or, conversely, providing a greater dose to the hyperactive/hypoxic regions of tumor while not increasing the dose to the normal tissue. Beam selection can further enhance both 3D radiotherapy and IMRT, provided there is the possibility of aligning beam orientations to exploit delivery through less functional regions of normal tissue. The extent to which functional image– guided dose delivery may be achieved can also depend on the capabilities of the planning system. A planning system that is capable of manipulating the dose to each voxel based on user specifications would be ideal and perhaps necessary for “dose painting tumor volumes by number” (see article by Bentzen and Gregoire in this issue). However, commercial planning systems typically treat a conglomerate of voxels as 1 entity, as in the case of organs or tumor, precluding elaborate voxel-specific spatial sculpting of the dose. Consequently, it may be easier to treat a grouping of similarly functional voxels within normal organs and a tumor as 1 monolithic functional region. In general, a radiotherapy treatment planning system (RTPS) with some sort of flexible “optimization” engine (which would preferably allow biological cost functions) is likely more important for the integration of these types of

Figure 1 A coronal view showing plans with (bottom) and without (top) SPECT guidance. The central purple and pink structures are primary and boost targets; the lung is shaded by SPECT activity intensity, ranging from red (highest) to green (lowest). Isodose lines show that SPECT guidance decreased the dose to the higher perfusion regions. (Reprinted with permission.30)

data for the potential reduction in normal tissue function loss than is whether the treatment plans are 3D conformal radiation therapy or IMRT. Their use could permit isotoxicitybased treatment planning25-27 based on the predicted loss of function.19,28 Next we illustrate some of the planning methodologies seen in literature. Seppenwoolde et al29 used the U-Mplan system (developed at the University of Michigan) to develop 3D plans with selected beam directions and beam weight optimization to minimize SPECT perfusion-weighted dose to the lung. This is an example of a more accessible planning system, with respect to customization. The system allowed the optimization of an objective function to weight the dose to the voxels by SPECT perfusion. Individual beam directions were selected to minimize the ratio of the mean perfused lung dose to the mean lung dose. The optimization minimized the mean lung dose, the lung volume above 20 Gy, and the mean SPECT perfusion-weighted lung dose. In a similar context, the work by McGuire et al30 is an example of perfusion SPECT-guided IMRT planning, but it was conducted within the confines of a commercial planning system (Fig. 1). The lung was segmented into 4 regions based on perfusion, and 9-beam IMRT plans were generated using a hierarchical method. The method consisted of sequential optimizations, starting with the dose allowed only through the least functional region. With each subsequent optimization, more functional regions

Functional and molecular image guidance

Figure 2 Plot of mean dose versus beam angle, with arrows indicating the beams selected. (Reprinted with permission.31)

were slowly relaxed to absorb greater a dose until the target coverage was met. The sequential optimization process implicitly gave a higher priority to the more functional lung. The heterogeneous nature of the functional distribution suggests that beam directions can be particularly important in the quest to improve functional sparing. Beam directions that pass through lower functional normal tissue to reach the tumor are obviously favorable. However, the issue is not quite as trivial as it appears because sparing the functional tissue has to be balanced against achieving the target coverage, which generally requires irradiation from multiple directions and IMRT for concave target volumes. Thus, in cases in which regions of low function are clustered together, it would be necessary for some beams to go through higher-functioning tissue to achieve target coverage. Examples of beam direction selection are as follows. Munawar et al31 used lung ventilation SPECT with 30-MBq 99mTc-labeled ultrafine graphite particles to steer the dose away from the highly ventilated regions. The normal lung was segmented into 2 functional regions: the ventilated lung at greater than 50% and 70% of maximum SPECT intensity. IMRT plans with 9 equally spaced beams were compared with plans with 3 beams that were selected from 36 equally spaced possibilities, such that the 3 beams individually had mean ventilated lung doses close to the minima (Fig. 2). In cases in which the planning target volume (PTV) was heterogeneously surrounded by the well-ventilated lung, the 3-beam plans achieved superior ventilation sparing. Shirai et al32 identified hepatocellular carcinoma radiation therapy angles that would least irradiate the functional liver (as imaged using Tc99m-galactosyl human serum albumin SPECT). Radiotherapy was well tolerated, with no cases of grade 3 or higher radiation-induced liver disease. McGuire et al33 showed for perfusion-weighted lung sparing that using 4 selected beam directions can result in a substantial functional dose reduction when compared with an equally spaced 9-beam nonfunctional-guided IMRT plan. The beam orientations used were selected from a larger set of candidate angles, which were individually optimized to minimize functional dose while achieving PTV minimum target coverage.

113 The 4 beams with greatest functional sparing over the whole dose range were then selected. Similar to the heterogeneous distribution of function in normal organs, tumors can also reflect functional heterogeneity in the form of metabolism, proliferation, or hypoxia. Treatment planning to achieve selective spatial dose escalation to specific target regions based on function could presumably improve local control (as discussed in other articles in this issue) but would have to be weighed against the incidental increase in the dose to surrounding critical organs. Pinkawa et al34 planned dose boosting to intraprostatic lesions identified using F-18-choline PET. A boost of 18 Gy resulted in only a minimal increase of the equivalent uniform dose to the rectum and bladder. Seppala et al35 used carbon-11 acetate PET to identify prostate subvolumes for boosting (Fig. 3). Comparing standard plans with 77.9 Gy to the PTV versus boosted plans with 72 Gy to the PTV and up to 90 Gy to the boosted volume, there was no significant normal tissue complication probability increase in rectum and bladder. Prostate subvolumes may also be identified for dose boosting using MRI/spectroscopy.36,37 Lee et al38 used F-18 –labeled fluormisonidazole to identify regions of head and neck cancers that were hypoxic. Boosting the hypoxia-avid regions by 14 Gy (80 Gy to the gross tumor volume (GTV)) was possible without exceeding normal tissue constraints. Similarly, reductions in head and neck tumor volumes planned to receive high doses based on PET tumor volume data led to a reduced dose to the surrounding normal tissues as reported by Geets et al.39 Optimized planning (either conformal 3D or IMRT) based on boosting tumor subvolumes identified via functional imaging could be expected to lead to similar results for treatments of tumors at other anatomic locations as well. These reductions in the overall volumes of the tumor required to receive a high-boost dose could enable the ability to deliver high doses to those subvolumes with no increase in predicted toxicity to normal tissues. However, in some cases, the additional information provided by the functional imaging can make composite (anatomic plus functional) target volumes larger as well.40,41

Potential Uses of Functional Imaging for Adaptive Image Guided Radiation Therapy Geets et al42 showed that the fludeoxyglucose (FDG) PET avid volume decreased during the course of radiotherapy in 11 patients with pharyngeolaryngeal squamous cell carcinoma. They suggest the possibility of adapting the radiotherapy dose during the course of radiation, such that the primary preradiotherapy volume remains unaltered during the course of radiotherapy, but the simultaneously integrated boost volume is reduced based on PET imaging during radiotherapy. The main advantage would likely be increased sparing to the spinal cord, parotid glands, and oral cavity. These functional imaging– based adaptive approaches for the treatment of head and neck cancers should, of course, be approached with caution because other authors, such as

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S.K. Das and R.K. Ten Haken neck squamous cell carcinoma. Cao et al45 earlier showed the potential for adaptive treatment planning changes based on blood volume alterations in head and neck cancers as shown using DCE MRI. Another area under active consideration for functional image– based adaptive therapy is non–small-cell lung cancer. Reductions in the size of PET-avid tumor regions have been observed by imaging studies using 18F-FDG46 as well as 18Ffluorothymidine (FLT).47 Such volume reductions suggest the potential to act on these changes while maintaining or reducing predicted lung toxicity.47-49 Care needs to be exercised in these target volume reductions; however, as Sonke and Belderbos indicate,50 reduced metabolic activity on PET does not necessarily correspond to geometric tumor volume changes; in some cases, erosion (clinical target volume appears to remain constant while the GTV shrinks) takes place instead. These same types of FDG and FLT studies may also help in assessing normal tissue toxicities during treatment,47,51 augmenting or potentially replacing the SPECT perfusion and ventilation studies mentioned previously. Lately, there have been indications from Mayr et al52 that longitudinal changes in tumor perfusion of cervical cancer as seen in DCE MRI might also be used to adapt treatment.

Dosimetric Results of Functional Image–Guided Radiotherapy Planning

Figure 3 The distribution of carbon-11 acetate in the axial (top), coronal (middle), and sagittal (bottom) planes superimposed on the corresponding CT planes through the prostate. (Reprinted with permission.35)

Hentschel et al,43 conclude that a reduction in treatment volumes may not be possible based on FDG-PET for head and neck cancer because of therapy-associated inflammation and possibly on which source to background algorithm is used. By contrast, Dirix et al44 present evidence for the added value (over FDG-PET) of 18F-fluoromisonidazole PET and suggest potential for both diffusion-weighted and dynamic contrastenhanced (DCE) MRI in the adaptive treatment of head and

The ultimate benefit of functional guidance in radiotherapy would either be a reduction of normal tissue toxicity, an increase in local control rate, or both. This benefit has yet to be shown, primarily because of the lack of full-scale trials to address this question. However, the dosimetric advantages of function sparing are evident in the radiotherapy planning articles in the literature. One can only anticipate that this dosimetric advantage will translate into an actual clinical benefit in the future. Seppenwoolde et al29 found that, only in patients with large perfusion defects, perfusion-weighted optimization improved over the geometric optimization. McGuire et al30 was able to show reductions in perfusion-weighted volumes above 20 Gy and 30 Gy of 13.6% ⫾ 5.2% and 10.5% ⫾ 5.8%, respectively. The difference in the conclusions between these 2 works is likely that Seppenwoolde et al29 used 3D planning as opposed to IMRT planning, which was used by McGuire et al.30 To answer the question of 3D radiotherapy versus IMRT, Lavrenkov et al53 found that in 6 of 17 patients there was no advantage to using IMRT over 3D to spare the SPECT functional lung over 20 Gy. In the remaining cases, the IMRT functional lung volume over 20 Gy was reduced to 74% of the 3D plan value. Shioyama et al54 segmented the highest 50% and 90% of the SPECT hyperperfused lung. Compared with IMRT plans without functional avoidance, IMRT plans with functional avoidance lowered mean doses to the 50% and 90% functional lung regions by 2.2 and 4.2 Gy, respectively (prescription dose of 63 Gy). Overall, IMRT does seem to provide improved dosimetric benefit compared with 3D for SPECT perfusion-weighted planning. Radiation therapy planning with ventilation imag-

Functional and molecular image guidance ing has also been used to show an improved dosimetric benefit. Four-dimensional CT-derived lung ventilation imaging55 has been used with IMRT planning to show the possibility of reducing the dose to the regions of the lung with the highest ventilation. Munawar et al31 showed that there was a dosimetric advantage in cases in which less than 5% of the highest 50% of the ventilation volume was in the PTV. An important finding by several groups is that the dosimetric gain possible is dependent on the nature of the spatial distribution of function. Munawar et al31 found that the dosimetric gain was lower when the well-ventilated lung completely surrounded the PTV. In examining 3D radiotherapy versus IMRT, Lavrenkov et al56 showed that IMRT was more capable of perfused lung sparing if highly perfused lung regions were closer to the PTV and the overall functional distribution was more heterogeneous. Shioyama et al54 also saw that more sparing was seen in patients with a greater spatial heterogeneity in function. The results of these works suggest that functional sparing in the lung shows the greatest dosimetric benefit when the functional distribution is heterogeneous and highly functional regions do not completely surround the target. The clinical significance of changes in tumor volume dose distributions will only result from prospective clinical trials that use functional image data to redesign dose distributions. These studies are ongoing.

Issues Related to the Use of Functional Image Guidance in Radiotherapy Planning Important issues regarding the use of functional image– guidance in radiotherapy include image registration, delineation of volumes, image reconstruction, and the question of the extent to which the dose should be escalated to the subregions of the tumor. Indeed, the ability to make firm conclusions related to the usefulness on defining the tumor subregions with functional imaging methods likely relies on the accuracy of these techniques and methods.41,57-59 An informative series of articles have recently been published that highlight the issues related to quantitative imaging for evaluating tumor response,60 tumor change measurement, truth data and error sources from MRI,61 x-ray CT62 and PET/CT,63 and statistical considerations related to the same.64 Many task groups and initiatives now exist within nearly every medical physics, radiation oncology, radiology, and nuclear medicine society or collaborative group with an intent to address standardization and qualityassurance issues related to the use of functional imaging in planning and assessing cancer treatments.65,66 Accurate image registration38,67 between the functional image set and the treatment planning set is key to ensuring a dosimetric benefit. Inaccurate image registration can, in the worst case, result in the opposite effect being achieved68 (ie, a higher dose to more functional normal tissue regions). Modern-day functional imagers typically include CT (CT-PET and CT-SPECT), allowing the planning CT images to be easily registered to the CT images from the functional imaging

115 machines, thereby indirectly registering the planning CT and functional image sets. However, this assumes that the CT acquired at the time of functional imaging is exactly aligned with the functional image set. This assumption is not necessarily true if the patient inadvertently moves between the CT and functional imaging acquisitions. To a large extent, the immobilization used for radiotherapy would ensure that the 2 image sets are aligned, not to mention that this would also improve the quality of image registration between the planning CT and the CT acquired at the time of functional imaging. Image reconstruction parameters, such as the SPECT attenuation and scatter corrections, can influence the SPECT voxel values. Consequently, they can also influence the shapes of functional subregions. However, the net dosimetric effect on metrics, such as the mean perfused dose or function above certain dose thresholds, may be less pronounced because of the aggregate nature of these metrics.69 Indeed, even the evaluation of the quality of the image registration may require consideration of multiple accuracy metrics. For example, Yin et al70 found that although sophisticated nonrigid registrations between the CT components of SPECT/CTs and planning CTs provided a higher degree of accuracy than rigid methods, irregularities in some of the deformation fields, when applied to the SPECT images, resulted in unacceptable changes in the SPECT intensity distributions that would preclude their use in RT planning. Target delineation can also present problems because there is no ground truth related to the thresholding of image intensities for segmentation, and the results can change depending on segmentation technique. Roels et al41 recently reported that although integration of MRI and FDG-PET into radiotherapy planning appeared to be feasible for use in image-guided radiotherapy of rectal cancer, automated segmentation was recommended for the PET images. Segmentation issues can be especially problematic in assessing PET signals because of the partial volume effect71; uptake regions may have their imaging signal spread over several imaging voxels. Methods continue to be explored to automatically address this problem.72 Even though there is evidence that high and low FDG uptake regions appear to remain stable,73 the reproducibility of image segmentation implementation is very important for prospective clinical studies, especially those that will attempt to adapt treatment plans during the course of treatment. Thus, much attention is being given to the application of these methods of target delineation from PET scans74,75 although similar matters need to be considered in the use of function derived from MRI signals.76 Beyond the issues identified by the series of articles referred to at the end of the first paragraph of this section, consensus recommendations and standards for PET image acquisition,77 monitoring response,78 and use in RTPSs79 have begun to appear. In the context of dose escalation to subregions of the tumor, a yet to be answered question is the extent to which dose should be raised in accordance with the functional image (PET/MRI) intensity. For example, should the dose be linear or nonlinear with intensity? Various planning studies have speculatively used dose-intensity relationships. Das et al80

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PET, Thorwarth et al82 used a dose-escalation scheme that assumed the time needed for reoxygenation was inversely proportional to perfusion and that radioresistance was proportional to tracer retention. In general, the nature of the formulation relating the required dose escalation versus functional image intensity (to achieve spatially uniform tumor control) awaits basic science and clinical feedback. Once such a formulation is available, planning studies could determine the extent to which achieving the required dose escalation is compromised by toxicity-limiting dose constraints to surrounding normal organs.

References

Figure 4 The isodose lines and FDG-PET intensity color map superimposed on the axial (top), sagittal (middle), and coronal (bottom) slices. The numbers indicate the dose in Gy per fraction, reflecting dose escalation to regions of higher FDG uptake. (Reprinted with permission.81)

assigned a linear dose-to-FDG intensity prescription over and above a baseline uniform therapeutic dose. Vanderstraeten et al81 compared a linear versus uniform boost prescription from FDG PET (Fig. 4). For hypoxia imaging with FMISO

1. Jaffray DA: Image-guided radiation therapy: From concept to practice. Semin Radiat Oncol 17:243-244, 2007 2. Yan D: Adaptive radiotherapy: Merging principle into clinical practice. Semin Radiat Oncol 20:79-83, 2010 3. Evans ES, Hahn CA, Kocak Z: The role of functional imaging in the diagnosis and management of late normal tissue injury. Semin Radiat Oncol 17:72-80, 2007 4. Jeraj R, Cao Y, Ten Haken RK: Imaging for assessment of radiationinduced normal tissue effects. Int J Radiat Oncol Biol Phys 76:S140S144, 2010 5. Belkacemi Y, Tsoutsou P, Magne N: Metabolic functional imaging for tumor radiosensitivity monitoring. Crit Rev Oncol/Hematol 62:227239, 2007 6. Rudin M: Imaging readouts as biomarkers or surrogate parameters for the assessment of therapeutic interventions. Eur Radiol 17:2441-2457, 2007 7. Guha C, Alfieri A, Blaufox MD: Tumor biology-guided radiotherapy treatment planning: Gross tumor volume versus functional tumor volume. Semin Nucl Med 38:105-113, 2008 8. Nimmagadda S, Ford EC, Wong JW: Targeted molecular imaging in oncology: Focus on radiation therapy. Semin Radiat Oncol 18:136148, 2008 9. Grosu A-L, Piert M, Weber WA: Positron emission tomography for radiation treatment planning. Strahlenther Onkol 181:483-499, 2005 10. Khoo VS, Joon DL: New developments in MRI for target volume delineation in radiotherapy. Br J Radiol 79:S2-15, 2006 11. Payne GS, Leach MO: Applications of magnetic resonance spectroscopy in radiotherapy treatment planning. Br J Radiol 79:S16-S26, 2006 12. Gregoire V, Haustermans K, Geets X: PET-based treatment planning in radiotherapy: A new standard? J Nucl Med 48:68S-77S, 2007 13. Heron DE, Andrade RS, Beriwal S: PET-CT in radiation oncology: The impact on diagnosis, treatment planning, and assessment of treatment response. Am J Clin Oncol 31:352-362, 2008 14. Ford EC, Herman J, Yorke E: 18F-FDG PET/CT for image-guided and intensity-modulated radiotherapy. J Nucl Med 50:1655-1665, 2009 15. Nestle U, Weber W, Hentschel M: Biological imaging in radiation therapy: Role of positron emission tomography. Phys Med Biol 54:R1-25, 2009 16. Zaidi H, Vees H, Wissmeyer M: Molecular PET/CT imaging-guided radiation therapy treatment planning. Acad Radiol 16:1108-1133, 2009 17. Alber M, Paulsen F, Eschmann SM: On biologically conformal boost dose optimization. Phys Med Biol 48:N31-N35, 2003 18. Brahme A: Biologically optimized 3-dimensional in vivo predictive assay-based radiation therapy using positron emission tomography-computerized tomography imaging. Acta Oncol 42:123-136, 2003 19. Nioutsikou E, Partridge M, Bedford JL: Prediction of radiation-induced normal tissue complications in radiotherapy using functional image data. Phys Med Biol 50:1035-1046, 2005 20. Yang Y, Xing L: Towards biologically conformal radiation therapy (BCRT): Selective IMRT dose escalation under the guidance of spatial biology distribution. Med Phys 32:1473-1484, 2005

Functional and molecular image guidance 21. Kim Y, Tomé WA: Risk-adaptive optimization: Selective boosting of high-risk tumor subvolumes. Int J Radiat Oncol Biol Phys 66: 1528-1542, 2006 22. South CP, Partridge M, Evans PM: A theoretical framework for prescribing radiotherapy dose distributions using patient-specific biological information. Med Phys 35:4599-4611, 2008 23. Søvik Å, Malinen E, Olsen DR: Strategies for biologic image-guided dose escalation: A review. Int J Radiat Oncol Biol Phys 73:650-658, 2009 24. Søvik Å, Malinen E, Olsen DR: Adapting biological feedback in radiotherapy. Semin Radiat Oncol 20:138-146, 2010 25. Ten Haken RK, Martel MK, Kessler ML: Use of Veff and iso-NTCP in the implementation of dose escalation protocols. Int J Radiat Oncol Biol Phys 27:689-695, 1993 26. McGinn CJ, Ten Haken RK, Ensminger WD: Treatment of intrahepatic cancers with radiation doses based on a normal tissue complication probability model. J Clin Oncol 16:2246-2252, 1998 27. Hayman JA, Martel MK, Ten Haken RK: Dose escalation in non-smallcell lung cancer using three-dimensional conformal radiation therapy: Update of a phase I trial. J Clin Oncol 19:127-136, 2001 28. van Baardwijk A, Bosmans G, Bentzen SM: Radiation dose prescription for non-small-cell lung cancer according to normal tissue dose constraints: An in silico clinical trial. Int J Radiat Oncol Biol Phys 71:11031110, 2008 29. Seppenwoolde Y, Engelsman M, De Jaeger K: Optimizing radiation treatment plans for lung cancer using lung perfusion information. Radiother Oncol 63:165-177, 2002 30. McGuire SM, Zhou S, Marks LB: A methodology for using SPECT to reduce intensity-modulated radiation therapy (IMRT) dose to functioning lung. Int J Radiat Oncol Biol Phys 66:1543-1552, 2006 31. Munawar I, Yaremko BP, Craig J: Intensity modulated radiotherapy of non-small-cell lung cancer incorporating SPECT ventilation imaging. Med Phys 37:1863-1872, 2010 32. Shirai S, Sato M, Suwa K: Feasibility and efficacy of single photon emission computed tomography-based three-dimensional conformal radiotherapy for hepatocellular carcinoma 8 cm or more with portal vein tumor thrombus in combination with transcatheter arterial chemoembolization. Int J Radiat Oncol Biol Phys 76:1037-1044, 2010 33. McGuire SM, Marks LB, Yin FF: A methodology for selecting the beam arrangement to reduce the intensity-modulated radiation therapy (IMRT) dose to the SPECT-defined functioning lung. Phys Med Biol 55:403-416, 2010 34. Pinkawa M, Attieh C, Piroth MD: Dose-escalation using intensity-modulated radiotherapy for prostate cancer—Evaluation of the dose distribution with and without 18F-choline PET-CT detected simultaneous integrated boost. Radiother Oncol 93:213-219, 2009 35. Seppälä J, Seppänen M, Arponen E: Carbon-11 acetate PET/CT based dose escalated IMRT in prostate cancer. Radiother Oncol 93:234-240, 2009 36. Mazaheri Y, Shukla-Dave A, Muellner A: Imaging of the prostate in clinical practice. Magma Magn Reson Mater Phys Biol Med 21:379-392, 2008 37. Chang J, Thakur SB, Huang W: Magnetic resonance spectroscopy imaging (MRSI) and brain functional magnetic resonance imaging (fMRI) for radiotherapy treatment planning of glioma. Technol Cancer Res Treat 7:349-362, 2008 38. Lee NY, Mechalakos JG, Nehmeh S: Fluorine-18-labeled fluoromisonidazole positron emission and computed tomography-guided intensity-modulated radiotherapy for head and neck cancer: A feasibility study. Int J Radiat Oncol Biol Phys 70:2-13, 2008 39. Geets X, Daisne J-F, Tomsej M: Impact of the type of imaging modality on target volumes delineation and dose distribution in pharyngo-laryngeal squamous cell carcinoma: Comparison between pre- and pertreatment studies. Radiother Oncol 78:291-297, 2006 40. Grills IS, Yan D, Black QC: Clinical implications of defining the gross tumor volume with combination of CT and 18FDG-positron emission tomography in non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 67:709-719, 2007 41. Roels S, Slagmolen P, Nuyts J: Biological image-guided radiotherapy in

117

42.

43.

44.

45.

46.

47.

48.

49.

50. 51.

52.

53.

54.

55.

56.

57.

58. 59. 60.

rectal cancer: Challenges and pitfalls. Int J Radiat Oncol Biol Phys 75:782-790, 2009 Geets X, Tomsej M, Lee JA: Adaptive biological image-guided IMRT with anatomic and functional imaging in pharyngo-laryngeal tumors: Impact on target volume delineation and dose distribution using helical tomotherapy. Radiother Oncol 85:105-115, 2007 Hentschel M, Appold S, Schreiber A: Serial FDG-PET on patients with head and neck cancer: Implications for radiation therapy. Int J Radiat Biol 85:796-804, 2009 Dirix P, Vandecaveye V, De Keyzer F: Dose painting in radiotherapy for head and neck squamous cell carcinoma: Value of repeated functional imaging with (18)F-FDG PET 18F-fluoromisonidazole PET, diffusionweighted MRI, and dynamic contrast-enhanced MRI. J Nucl Med 50: 1020-1027, 2009 Cao Y, Popovtzer A, Li D: Early prediction of outcome in advanced head-and-neck cancer based on tumor blood volume alterations during therapy: A prospective study. Int J Radiat Oncol Biol Phys 72:12871290, 2008 Kong F-MS, Frey KA, Quint LE: A pilot study of [18F]fluorodeoxyglucose positron emission tomography scans during and after radiationbased therapy in patients with nonsmall-cell lung cancer. J Clin Oncol 25:3116-3123, 2007 Everitt S, Hicks RJ, Ball D: Imaging cellular proliferation during chemoradiotherapy: A pilot study of serial 18F-FLT positron emission tomography/computed tomography imaging for non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 75:1098-1104, 2009 Gillham C, Zips D, Ponisch F: Additional PET/CT in week 5-6 of radiotherapy for patients with stage III non-small cell lung cancer as a means of dose escalation planning? Radiother Oncol 88:335-341, 2008 Feng M, Kong F-M, Gross M: Using fluorodeoxyglucose positron emission tomography to assess tumor volume during radiotherapy for nonsmall-cell lung cancer and its potential impact on adaptive dose escalation and normal tissue sparing. Int J Radiat Oncol Biol Phys 73:12281234, 2009 Sonke J-J, Belderbos J: Adaptive radiotherapy for lung cancer. Semin Radiat Oncol 20:94-106, 2010 De Ruysscher D, Houben A, Aerts HJWL: Increased 18F-deoxyglucose uptake in the lung during the first weeks of radiotherapy is correlated with subsequent radiation-induced lung toxicity (RILT): A prospective pilot study. Radiother Oncol 91:415-420, 2009 Mayr NA, Wang JZ, Zhang D: Longitudinal changes in tumor perfusion pattern during the radiation therapy course and its clinical impact in cervical cancer. Int J Radiat Oncol Biol Phys 77:502-508, 2010 Lavrenkov K, Christian JA, Partridge M: A potential to reduce pulmonary toxicity: The use of perfusion SPECT with IMRT for functional lung avoidance in radiotherapy of non-small cell lung cancer. Radiother Oncol 83:156-162, 2007 Shioyama Y, Jang SY, Liu HH: Preserving functional lung using perfusion imaging and intensity-modulated radiation therapy for advancedstage non-small cell lung cancer. Int J Radiat Oncol Biol Phys 68:13491358, 2007 Yaremko BP, Guerrero TM, Noyola-Martinez J: Reduction of normal lung irradiation in locally advanced non-small-cell lung cancer patients, using ventilation images for functional avoidance. Int J Radiat Oncol Biol Phys 68:562-571, 2007 Lavrenkov K, Singh S, Christian JA: Effective avoidance of a functional spect-perfused lung using intensity modulated radiotherapy (IMRT) for non-small cell lung cancer (NSCLC): An update of a planning study. Radiother Oncol 91:349-352, 2009 Bowen SR, Flynn RT, Bentzen SM: On the sensitivity of IMRT dose optimization to the mathematical form of a biological imaging-based prescription function. Phys Med Biol 54:1483-1501, 2009 Kim Y, Tome WA: On the impact of functional imaging accuracy on selective boosting IMRT. Phys Med 25:12-24, 2009 Niyazi M, Bartenstein P, Belka C: Choline PET based dose-painting in prostate cancer—Modelling of dose effects. Radiol Oncol 5:23, 2010 Meyer CR, Armatto SG, Fenimore CP: Quantitative imaging to assess tumor response to therapy: Common themes of measurement, truth data, and error sources. Transl Oncol 2:198-210, 2009

S.K. Das and R.K. Ten Haken

118 61. Jackson EF, Barboriak DP, Bidaut LM: Magnetic resonance assessment of response to therapy: Tumor change measurement, truth data and error sources. Transl Oncol 2:211-215, 2009 62. McNitt-Gray MF, Bidaut LM, Armatto SG: Computed tomography assessessment of response to therapy: Tumor volume change measurement, truth data, and error. Transl Oncol 2:216-222, 2009 63. Kinahan PE, Doot RK, Wanner-Roybal M: PET/CT assessment of response to therapy: Tumor change measeurment, truth data, and error. Transl Oncol 2:223-230, 2009 64. Barnhart HX, Barboriak DP: Applications of the repeatability of quantitative imaging biomarkers: A review of statistical analysis of repeat data sets. Transl Oncol 2:231-235, 2009 65. Clarke LP, Sriram RD, Schilling LB: Imaging as a biomarker: Standards for change measurements in therapy workshop summary. Acad Radiol 15:501-530, 2008 66. Clarke LP, Croft BS, Nordstrom R: Qualitative imaging for evaluation of response to cancer therapy. Transl Oncol 2:195-197, 2009 67. Kessler ML: Image registration and data fusion in radiation therapy. Br J Radiol 79:S99-S108, 2006 68. Papavasileiou P, Divoli A, Hatziioannou K: The importance of the accuracy of image registration of SPECT images for 3D targeted radionuclide therapy dosimetry. Phys Med Biol 52:N539-N548, 2007 69. Yin L, Shcherbinin S, Celler A: Incorporating quantitative single photon emission computed tomography into radiation therapy treatment planning for lung cancer: Impact of attenuation and scatter correction on the single photon emission computed tomography-weighted mean dose and functional lung segmentation. Int J Radiat Oncol Biol Phys 78:587-594, 2010 70. Yin LS, Tang L, Hamarneh G: Complexity and accuracy of image registration methods in SPECT-guided radiation therapy. Phys Med Biol 55:237-246, 2010 71. Soret M, Bacharach SL, Buvat I: Partial-volume effect in PET tumor imaging. J Nucl Med 48:932-945, 2007 72. Barbee DL, Flynn RT, Holden JE: A method for partial volume correction of PET-imaged tumor heterogeneity using expectation maximiza-

73.

74.

75.

76.

77. 78.

79.

80.

81.

82.

tion with a spatially varying point spread function. Phys Med Biol 55:221-236, 2010 Aerts HJWL, Bosmans G, van Baardwijk AAW: Stability of 18F-deoxyglucose uptake locations within tumor during radiotherapy for NSCLC: A prospective study. Int J Radiat Oncol Biol Phys 71:1402-1407, 2008 Nestle U, Kremp S, Schaefer-Schüler A: comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-small cell lung cancer. J Nucl Med 46:1342-1348, 2005 Hatt M, Cheze le Rest C, Descourt P: Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys 77:301-308, 2010 Cao Y, Li D, Shen Z: Sensitivity of quantitative metrics derived from DCE MRI and a pharmacokinetic model to image quality and acquisition parameters. Acad Radiol 17:468-478, 2010 Boellaard R: Standards for PET image acquisition and quantitative data analysis. J Nucl Med 50:11S-20S, 2009 Shankar LK, Hoffman JM, Bacharach S: Consensus recommendations for the use of 18F-FDG PET as an indicator of therapeutic response in patients in National Cancer Institute trials. J Nucl Med 47:1059-1066, 2006 MacManus M, Nestle U, et al: Use of PET and PET/CT for radiation therapy planning: IAEA expert report 2006-07. Radiother Oncol 91: 85-94, 2009 Das SK, Miften MM, Zhou S: Feasibility of optimizing the dose distribution in lung tumors using fluorine-18-fluorodeoxyglucose positron emission tomography and single photon emission computed tomography guided dose prescriptions. Med Phys 31:1452-1461, 2004 Vanderstraeten B, Duthoy W, Gersem WD: [18F]fluoro-deoxy-glucose positron emission tomography ([18F]FDG-PET) voxel intensity-based intensity-modulated radiation therapy (IMRT) for head and neck cancer. Radiother Oncol 79:249-258, 2006 Thorwarth D, Eschmann S-M, Paulsen F: Hypoxia dose painting by numbers: A planning study. Int J Radiat Oncol Biol Phys 68:291-300, 2007

Diffusion Magnetic Resonance Imaging in Cancer Patient Management Anwar R. Padhani, MB, BS, FRCP, FRCR Diffusion-weighted MRI (DW-MRI) is able to detect and characterize tissues because it incorporates sensitivity to water content and water movements into the images that are produced. Compared with other imaging modalities used in oncologic assessments, DWMRI does not expose patients to ionizing radiations, and no injection of isotopes or any other contrast medium is necessary. Regional or whole-body examinations are possible in reasonably short examination times, allowing DW-MRI to be incorporated into oncologic imaging practice. The information obtained can be quantified and displayed as parametric maps, thus enabling spatial heterogeneity of tissues/tumors to be analyzed. Clinical applications for DW-MRI include lesion detection, characterization, and response assessments. DW-MRI has the potential to direct radiation therapy planning. In the response assessment setting, DW-MRI observations appear to reflect interactions between the mechanism of action of treatments and the underlying structural biology of tissues. Semin Radiat Oncol 21:119-140 © 2011 Elsevier Inc. All rights reserved.

M

agnetic resonance imaging (MRI) plays a pivotal role in the management of patients with cancer; it is used at every stage of the cancer patient’s journey. Although clearly important, morphologic MRI assessments have substantial limitations. The detection of malignant lesions can often be difficult, particularly when the disease burden is small or when the disease is intermixed with normal tissues or benign disease processes (eg, the detection of prostate cancer in the presence of coexisting benign nodular prostatic hyperplasia). Lesion characterizations can be equally problematic (eg, determining the nature of small solid liver masses or small cystic renal lesions). In therapy assessments, there is an increasing awareness that anatomic approaches have significant limitations, including tumors that cannot be measured and poor measurement reproducibility. Faced with these limitations, more sophisticated measurements (such as tumor volume and changes in tumor computed tomography [CT] density values after contrast medium administration) are being considered for the evaluation of therapy response.1,2 However, with the increasing clinical use of cytostatic therapeutics, there is a recognition that anatomic evaluations are insensitive to changes that may inform on the overall therapeutic success of

novel agents.3-5 Assessments of the activity of residual disease are also problematic for morphologic imaging methods. Finally, determining the presence of relapsed disease in areas of therapyinduced scarring can be difficult, requiring careful comparisons to be made with prior examinations. Many of the limitations of morphologic imaging can be addressed by functional imaging techniques, such as fluorodeoxyglucose positron emission tomography (FDG-PET), dynamic contrast medium-enhanced MRI (DCE-MRI), and diffusionweighted MRI (DW-MRI). Functional imaging techniques are increasingly being used as pharmacodynamic biomarkers of response for therapies with novel mechanisms of action6 to see if tumor physiology, biochemistry, and the microenvironment are correspondingly altered.7 In this article, we focused on DWMRI, describing the biological basis of the observations, methods of quantification, and data display.8-11 We linked DW-MRI– derived biomarkers with important histologic correlates. We outlined the potential roles that DW-MRI can play in the management of cancer patients receiving a variety of treatments, including radiotherapy and chemoradiation. We concentrated mainly on the experience gained in human studies seeking to use the more extensive animal literature to understand the biological underpinning of the observations made.

Paul Strickland Scanner Centre, Mount Vernon Cancer Center, Northwood, Middlesex, United Kingdom. Address reprint requests to Anwar R. Padhani, MB, BS, FRCP, FRCR, Consultant Radiologist, Paul Strickland Scanner Centre, Mount Vernon Cancer Center, Rickmansworth Road, Northwood, Middlesex, UK.HA6 2RN UK. E-mail: [email protected]

DW-MRI Is a Biomarker Relevant for Cancer

1053-4296/11/$-see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.semradonc.2010.10.004

In a recent US National Cancer Institute–sponsored consensus conference report, it was noted that there was “an extraor119

120 dinary opportunity for DW-MRI to evolve into a clinically valuable tool, potentially important of drug development.”8 Major advantages of DW-MRI include that no ionizing radiation is administered and no injection of isotope or any other contrast medium is necessary for examinations. Regional or whole-body examinations are possible in reasonably short examination times allowing DW-MRI to be incorporated into “routine clinical practice.” The information obtained can be quantified and displayed as parametric maps, thus enabling spatial heterogeneity of tissues/tumors to be analyzed before and in response to treatment. DW-MRI derived parameters, such as the apparent diffusion coefficient (ADC), are theoretically independent of magnetic field strength and the relative simplicity of data acquisitions facilitates multicenter and longitudinal studies.

Biological Basis for Observations At human body temperature, random water molecules migrate approximately 30 ␮m over 50 milliseconds but only if there are no barriers to their motion. Because most human cells are in the same order of magnitude and subcellular structures have even smaller dimensions, the likelihood that tissue water molecules will encounter cellular components is extremely high. In fact, it is highly likely that water molecules will have many interactions over such a short measurement interval. As a result, water movement in tissues is neither entirely free nor random, being modified by interactions with cell membranes, intracellular organelles, macromolecules, and flows within tubular channels, such as blood vessels and ducts. Imaging the thermally driven motion of water is uniquely assessed by DW-MRI. Interested readers are referred to other excellent reviews on technical aspects of DW-MRI.12-14 Basically, MRI is able to measure water diffusivity by modifying T2-weighted spin-echo sequences, incorporating diffusion sensitizing gradients within the pulse design with echo-planar readouts of the data. The strength and duration of the application of diffusion sensitizing gradients is indicated by their “b value.” Generally, a range of b values (2 or more) is used in a DW-MRI study to interrogate the water diffusion properties of tissues (Fig. 1). In the absence of diffusion sensitizing gradients (b value ⫽ 0 s/mm2), free water appears bright because of intrinsic T2 weighting. In images acquired with low b values (50-100 s/mm2), vessels and cerebrospinal fluid show marked signal attenuation because water molecules will have moved over a relatively large distance during the time of application of the diffusion sensitizing gradients. Because signal intensity from blood vessels is attenuated on low b-value images, these images are often termed “black blood” images. With increasing b values, signal intensity attenuates steadily in other tissues initially disappearing in free water (eg, urine in the bladder), then in glandular tissues (eg, prostate, salivary glands, and pancreas), and then in tissues showing highly organized cellular structure, such as the liver.

A.R. Padhani Because water movement is relatively impeded in highly packed tissues, such as tumors, very cellular tissues appear persistently bright against a darkening background at high b values of 500 to 1,000 seconds/mm2. For the same reasons, several normal but highly cellular tissues also appear bright on high b-value images, such as the brain, spinal cord, spleen (variable), and normal lymphatic tissues (tonsils, adenoids, lymph nodes). In routine clinical imaging, the signal intensity decay observed in tissues with increasing b values generally follows biexponential behavior; the initial exponential behavior is due to signal losses caused by blood flow, and the second exponential is caused by extravascular, extracellular water movements (Fig. 1). Multiexponential behaviors are rarely seen in clinical practice unless very high b values are used (b ⬎2,000 s/mm2). In practice, most commercially available software applications calculate the diffusivity using monoexponential fitting for each pixel, displaying diffusivity values as parametric images, with areas of restricted diffusion appearing darker on gray scale images. Proprietary software packages using biexponential and stretched exponential model fitting can also be found but are rarely used in everyday practice. Because tissue water movements are not free but impeded, tissue diffusivity is often termed the ADC (units: ␮m2/s or ⫻10⫺3 mm2/s). In our discussion so far we have assumed that impediments to tissue water movements are non-directional (isotropic). Observations show that impediments to water mobility in normal tissues can be directional (i.e., anisotropic). White matter in the brain and renal tubules are particular examples of tissues where the apparent water mobility varies strongly according to the relative orientation of the measurement direction. In order to map anisotropy, diffusion sensitizing gradients must be applied along multiple directions (at least six diffusion gradient axes), plus b ⫽ 0. Again, interested readers are referred elsewhere for technical discussions on diffusion tensor imaging (DTI).12-14 Using these methods, eigenvalues (␭1, ␭2, ␭3) which represent the orthogonal diffusion components with voxels can be calculated. Convention has it that ␭1 represent the highest diffusivity value and ␭2 and ␭3 which have lower values are perpendicular to ␭1 (␭1 ⱖ ␭2 ⱖ ␭3). So with non-directional water movements, the eigenvalues would be approximately the same (␭1⬇ ␭2 ⬇ ␭3). Eigenvalues and eigenvectors can be used to calculate the degree of anisotropy of a tissue and the directionality of water movement. With anisotropy and directionality indices, inferences of tissue cyto-architectural features can be made. A commonly used anisotropy measure is fractional anisotropy (FA) which is a dimensionless quantity bound between 0 (non-directional isotropic) and 1 (highly directional anisotropic). For reference, the white matter of the high directional corpus callosum of the brain is FA ⬇ 0.7-0.8. FA is generally reduced in primary brain tumors (Fig. 2) due to disorganized architecture resulting from neuronal death, axonal loss, and irregular tumor cellular growth.15 FA values have been correlated with brain tumor cellularity and the degree of tumor infiltration.16 The directionality of tissue water motion anisotropy can be displayed by linking and color coding of

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Figure 1 DW-MRI of the liver of a 68-year-old woman with metastatic esophageal cancer to the liver. Multiple b-value images were obtained and used to generate the ADC map by monoexponential fitting of signal intensity decay. The signal intensity from blood vessels is attenuated on the b100 image (black blood image). Three lesions are seen on the b250 images (arrow heads), only 2 of which remain visible at b750. The bile duct (arrowhead with asterisk) disappears at b500. The graphs show that the signal intensity decay of the cerebral spinal fluid and liver follow biexponential behavior. The signal intensity decay of the cyst, tumor, and kidney is largely monoexponential.

eigenvectors, which allows the mapping of fiber tracts in the brain and of renal tubules. Diffusion tensor imaging (DTI) can be used to assess in detail the relationship between tumors and nearby white matter tracts of the brain, potentially differentiating between tumor infiltration of white matter tracts from displacement (Fig. 2) which can be useful for preoperative planning.17 Again interested readers are referred elsewhere for excellent reviews on DTI and fiber tractography, along with their clinical applications for more detailed discussions.18,19 In everyday oncologic practice, only the simple measures of water diffusivity are used as biomarkers of tissue properties. These include relative signal intensity at different b-values, water diffusivity (D), perfusion fraction (Fp), apparent diffusion coefficient (ADCtotal), fractionated ADC (ADCfast & ADCslow) and fractional anisotropy (FA). ADCfast is calculated using low b-values (0-100 s/mm2); it is dominated by the perfusion component of the total tissue diffusivity. At higher

b-values (⬎100 s/mm2), the perfusion component is largely extinguished, so ADCslow measurements are more heavily determined by water diffusion within the cellular matrix. In most clinical studies only ADCtotal is reported (usually simply written as ADC) so it is not possible to distinguish between the perfusion and non-perfusion components to the quoted diffusivity. This is an important point to remember when interpreting clinical data with regards to lesion characterization and therapy effects on tissues.

Clinical Protocols Initial clinical applications of DW-MRI focused on the evaluation of intracranial pathologies, finding application in stroke imaging in particular. Recent technological breakthroughs have enabled the development of DW-MRI for extracranial disease assessments. Specifically, the combination of echo-planar and parallel imaging techniques has led to the

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Figure 2 Diffusion tensor imaging of malignant brain masses. (A) A 70-year-old man with metastatic mucinous cancer of the ethmoidal air cells. (B) A 40-year-old man with glioblastoma multiforme (GBM). For both tumors, axial T2-weighted, postcontrast T1-weighted, b1000, ADC map, fractional anisotropy (FA) map, and DTI are shown. On the DTI image, anterior-posterior tracts are indicated in green, blue denotes superior-inferior tracts, and laterally directed tracts are indicated by red colors. FA is generally reduced in both tumors compared with normal brain white matter. Note how the slower growing mucinous metastases cancer seems to displace white-matter tracts seen on the FA maps and on the DTI. The GBM infiltrates the surrounding white matter of the brain, leading to lower relative FA values and loss of the normal white matter directionality.

ability to perform ultrafast, snapshot MRI. The ability to acquire images at very short echo times (⬍80 milliseconds) has resulted in improved signal-to-noise ratios of diffusionweighted images. Body DW-MRI is currently best performed at 1.5 T using surface coils for signal reception because of the ability to successfully suppress fat over large field of views. Imaging at 3.0 T has the potential to improve image signalto-noise ratios but is more technologically challenging because of the higher incidence of susceptibility artifacts and

poorer fat suppression particularly in whole-body applications. Currently, several techniques are used for single-body part assessments, including single-shot, breath-hold, and multiple averaging, free breathing DW-MRI. Breath-hold imaging limits the number of b values and/or signal averages that can be accommodated into a single breath hold duration because of which the images acquired are noisier and ADC estimates are more error prone. Free breathing techniques are gen-

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Figure 3 Diffusion MRI of the bone marrow in an 80-year-old man with hormone refractory metastatic prostate cancer. The lower pelvic bone marrow was irradiated at the treatment of the primary cancer and has undergone yellow marrow atrophy (ADC value of 319 ⫾ 257 ␮m2/s). The nonirradiated upper pelvic red bone marrow has a higher ADC value (ADC value of 581 ⫾ 313 ␮m2/s). The sacral metastasis has metastatic sacral metastasis has the highest ADC value (795 ⫾ 238 ␮m2/s) and is of high signal intensity on the b1100 image.

erally used in the clinic, with or without respiratory or cardiac motion compensation. When data are acquired without motion compensation, the efficiency of data acquisitions is increased allowing improvements organ coverage and improved signal-to-noise ratio of the acquired images; the later technique has been used to obtain the illustrative material of this text. Whole-body, multiple averaging, free breathing DW-MRI is becoming an increasingly important clinical tool. The number and range of b values used is dependent on clinical and technical requirements. Measurements of perfusion fraction (Fp) require that a number of low b-value images be acquired for accurate estimates. Multiple b-value images (more than 2) are generally used in organ/region assessments because they enable more accurate curve fitting for the derivation of ADC values. Generally, only 2 b values are acquired for whole-body protocols (0-50 and 800-1000 s/mm2), which still enable estimates of ADC to be made. There are practical limits related to signal-to-noise ratio and hardware limitations that determine how large a diffusion sensitizing gradient can be applied in human studies, and in practice most clinical DW-MRI studies rarely exceed b values ⬎1,000 to 1,500 seconds/mm2. Further detailed discussions of clinical imaging protocols is beyond the scope of this article, and interested readers are referred to other comprehensive review articles that cover the practical tradeoff that need to be undertaken to obtain clinically useful images.9,10,20,21

Histologic Correlates The basic biological premise for the use of DW-MRI in cancer is that malignant tissues are generally more cellular and have high-water content than benign/normal tissues, both of which lead to high signal intensity on high b-value images. In fact, on closer observation, there are a number of microscopic

organizational features that affect tissue water diffusivity, including tissue perfusion, cell density (number of cells/ high-power field), distribution of cell sizes within a tissue, integrity of cellular membranes, and tissue organization (Fig. 2). Inverse correlations between ADC and cell density in gliomas, metastatic brain tumors, renal and prostate cancers, and some childhood tumors can be found in the literature.22-29 However, this is not a universal finding, and poorer correlations between ADC values and cell density have been noted in breast30 adenocarcinomas. The latter may reflect the opposing effects of water movement in more than 1 tissue compartment (more limited in extracellular fluid space and relatively increased within glandular lumina in adenocarcinomas). ADC correlations in bone marrow because of disease merits separate consideration. The literature suggests that ADC correlations with cellularity of the bone marrow is biphasic, with initial increases in high b-value signal intensity and ADC, because fat cells of yellow marrow are progressively replaced by normal red marrow elements and/or tumor cells.31,32 The lower signal intensity and ADC values of yellow marrow appears to be related to reduced water content and that larger-sized fat cells may theoretically impede water movements to a greater extent than smaller-sized hematopoietic cells. However, once all fat cells are lost, then increasing bone marrow cellularity results in ADC declines (but there are continued increases in signal intensity on high b-value images) (Fig. 3). There are other important histologic properties that correlate with ADC, including tumor proliferation index,33,34 tumor grade,35-38 presence of necrosis,28,39,40 and in the therapy setting, tumor cell apoptosis.39,41,42 Because many of these histologic properties also determine the likelihood of a tumor responding to treatment, DW-MRI measurements of water diffusivity may also be used to predict treatment effectiveness as will be discussed later.

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Figure 4 Whole-body diffusion MRI in a 54-year-old woman with metastatic breast cancer. The top-left panel shows a MIP (inverted scale) of a whole-body examination acquired using a diffusion value of b800. The primary breast cancer (indicted on the top right panel, MIP using the original gray scale) and bong metastases are clearly visualized as high signal intensity regions that can be segmented for the calculation of total tumor volume (87.2 cm3) and the corresponding ADC histogram from all segmented voxels (mean 943 ␮m2/s; standard deviation [SD] ⫾ 264; range, 303-2070). Normal brain and spinal cord and normal axillary lymph nodes (arrows) are of higher signal intensity than the background. The splenic signal is often variable and in this example is lower than the metastatic deposits. Images were processed using Oncotreat Software (Siemens HeathCare, Erlangen, Germany).

Assessing Diffusion Images and Diffusivity Maps Image Display In the clinic, DW-MRI can be evaluated qualitatively by observing signal intensity usually on b-value images obtained and/or quantitatively by reviewing ADC maps. There are a number of ways of displaying diffusion-weighted images. Inverted gray or arbitrary color scales (so-called false color

maps) are often used to visualize the data as sliced data or they can be stacked and viewed as multiplanar reconstructions and maximal intensity projections (MIPs) (Fig. 4). Reader should note that although no color scales are especially suited for the display of high b-value images, convention has it that inverted Gray scales are used. It is also possible to fuse high b-value images with anatomic images using advanced software algorithms. Fusion of high b-value images with anatomic images has not been found to aid in lesion

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Figure 5 Delineating recurrent rectal cancer with fusion imaging in a 52-year-old man with recurrent rectal cancer within the resection bed of an abdominoperineal resection. A volumetric acquired T2-weighted sequence is fused with b800 diffusion images and displayed using a false color map (hot body scale). Three-dimensional fusion imaging of high b-value DW-MRI with anatomic imaging can aid in the delineation of tumor borders for radiotherapy planning in the presence of postoperative scarring. Anatomic T2W images were used to delineate normal structures. Corresponding isodose distributions in 3 planes are shown. Note the reversal of left-right sides on the radiotherapy plans.

detection but appears to be helpful for lesion localization.43 Three-dimensional fusion imaging of high b-value DW-MRI with anatomic imaging have a number of uses, including data presentation to clinicians, for depicting and guiding biopsy to variable tumor cell sites in areas of scarring and for aiding the CT volume determination of radiotherapy plans (Fig. 5).

Qualitative Analysis Visual assessments of signal intensity are clinically useful particularly when global assessments of the tumor burden are being undertaken particularly for whole-body diffusion MRI techniques. This method of assessment is practically useful and clinically appealing for both radiologists and referring physicians because it enables “at a glance” assessments of disease burden and distribution (Figs. 4 and 6). Disease progression on high b-value images is displayed as new areas of abnormal signal intensity or by changes in the extent, symmetry, and intensity of abnormalities. With successful treatment, cell killing results in the lowering of signal intensity. The physiological reasons for changes in tissue signal intensity with respect to the therapy discussed in detailed later and summarized in Table 1. There are a number of potential confounding factors that

demonstrate high signal intensity on high b-value images, such as tissue edema after radiation therapy or surgery (Fig. 6). Increased lesion/tissue signal intensity on high b-value images (with corresponding increased ADC values) results from increased water content and is termed “T2 shine through.” T2 shine through is responsible for the visibility of normal salivary and prostate glands, gall bladder, liver hemangiomas, and normal breast parenchyma commonly seen on whole-body DW-MRI (Fig. 6). To avoid misinterpretations arising from signal intensity assessments, it is necessary to correlate high b-value images with corresponding ADC maps, taking into account morphologic features as detailed in Table 2.

Quantitative Analyses There are a number of approaches for analyzing ADC maps. Most studies report on single median or mean values of ADC using whole tumor regions of interest or selected areas within tumors. However, visual inspection shows that tumors are often heterogeneous in their spatial ADC distributions. Simple measures of central tendency have limited ability to detect treatment-related changes if there are both increases and decreases in ADC values. In these cases, the net mean or median

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Figure 6 Detecting bony metastases with whole body diffusion MRI 43-year-old female with margin positive invasive breast cancer having undergone surgical resection on left side with left axillary dissection. Right breast surgery for ductal-carcinoma-in-situ also. (A) Bone scan versus whole body diffusion MRI (b900). Two equivocal lesions (arrows) on the bone scan are confirmed to be metastases on whole body diffusion images (arrows), but several other bone metastases can be seen. The signal intensity of the breasts is high (asterix) due to edema following recent surgery. A seroma in the left axilla is visible. Note also high signal intensity of the gall bladder contents (GB), spleen, endometrium and of the descending colon. (B) CT scan (top row) versus diffusion MRI (b900, bottom row). Lytic metastases seen on CT scan are clearly depicted by the diffusion MRI scan also (arrows). The sacral metastasis is better seen on the diffusion MRI scan.

ADC Change

Therapy Type

Dominant Biological Explanation

Radiation and chemotherapy

Massive liquefactive necrosis

Often persistent over a long period

Bone

Firm

Correlation with other techniques is essential to avoid this pitfall.

Mostly2 but can remain1 in bony lesions due necrosis

Chemotherapy

Tumor cell death

Increases in ADC can be seen within a few days but duration of effects can be variable

Various

Firm

1

Often1 in the short terms

Radiation

Tumor cell death, edema, inflammation and microvessel leak

Increases in ADC can be seen within 1-2 d but are more prolonged compared with chemotherapy

Bone, brain, other pelvic organs

Moderate to firm

1

Mixed

Embolization

Ischemic tumor cell death

Transient

Liver

Moderate

1¡

Mixed

Radiofrequency ablation

Interstitial edema, hemorrhage, carbonization, necrosis and fibrosis

Liver, lung

Preliminary

Can be1 because of reduced extracellular space

Antiangiogenics

Vascular normalization

Early increases of ADC values but time courses of changes not well defined Onset at the time of reduced vascular permeability. persistent

Brain tumors Primary liver malignancy

Moderate

ADC reduces at a variable time as tumor cells are cleared, with tissues remodeling, vascular normalization and as mature fibrosis develops. Bony sclerosis and secondary chemotherapy induced marrow fibrosis also lower ADC values. Residual active disease also results in low ADC values at the end of treatment. Radiation induced increased ADC values dominates other processes attempting to normalize ADC, such as those mention above resulting in prolonged ADC elevations. Associated with nonenhancement because of vascular shutdown. Time course of appearances within different organs and ability to predict outcomes are yet to be fully defined. Appearance of necrosis can cause ADC increases. Vascular disruptive agents cause ADC increases via necrosis.

11 (T2-shine through) 1

2

Signal Intensity 11

Onset and/or Duration of DW-MRI Changes

Organ or Tissues

Strength of Observations*

Additional Comments

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Table 1 Summary of Water Diffusivity Changes With Therapies and Possible Biological Explanations

*Based on authors’ own observations and evaluations of literature data.

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128 Table 2 Image Interpretation Guidelines for DW-MRI Signal Intensity on High b-value Images

ADC Maps

1

2

1

1

2

1

2

2 ↔

Interpretation Generally, high cellularity tumor; rarely abscess, viscous fluids, or blood products T2-shine through; liquefactive necrosis Fluid; necrosis; lower cellularity; tumors with gland formation Fibromuscular issues; fat Mature fibrous tissue with low water content

changes in ADC values are reduced, thus hiding treatmentrelated effects. It is possible to capture some aspects of displayed heterogeneity in the descriptors of histograms, such as range, standard deviation, centile values, skewness, kurtosis, and percent of voxels above or below ADC cutoff values. Changes in histogram descriptors can then be correlated with therapy response (Fig. 7). Of course, the histogram approach need not be simplistic, and more sophisticated histogram analysis approaches have appeared in the literature; unfortunately, these relatively complex approaches, such as principal component descriptors, remove them from clinical usability. Another major disadvantage of the histogram approach is that the spatial distribution of this information can be lost.44

A new analytic method for retaining spatial information in response to therapy is called the functional diffusion map or the ADC parametric response map (PRMADC).45 This method requires that pre- and post-therapy ADC volumes are spatially registered using sophisticated software. Differences in the registered voxel data between the 2 examinations are determined, and threshold values for ADC change are applied according to predetermined criteria. Changes in voxels are then color labeled (such as “red” for increase, “green” for no change, and “blue” for decrease) and overlaid onto anatomic images, enabling the spatial distribution of changed voxels to be appreciated. This approach also allows for the quantification of the relative tumor volume in which changes have occurred. These statistical changes can then be correlated with therapy response in their own right (Fig. 8). As noted in the report by Gerstner and Sorensen (in this issue), quantitative abstractions, such as the functional diffusion map or the ADC parametric response map (PRMADC),45 have shown potential prognostic value in studies of brain tumors.46 Further studies have evaluated the value of PRMADC in head and neck47 cancer and metastatic prostate cancer to bones.48 It is not clear how this kind of approach will be useful in clinical practice particularly for tumors located in areas of the body with significant physiological motions, such as the thorax and abdomen, and for organs with significant tissue and tumor distortions, such as the breast.49 In addition, the technique cannot be easily applied when there is an observable change in tumor size or shape after treatment. Furthermore, thresholds of individual voxel differences are needed to define the percentage of voxels that are changed in

Figure 7 Hodgkin lymphoma: early response to chemotherapy in a 29-year-old man with Hodgkin lymphoma. Images are acquired before (top row) and 8 days (bottom row) after the first cycle of ABVD chemotherapy (doxorubicin, bleomycin, vinblastine, and dacarbazine). Some reduction in the size of upper abdominal lymph nodes is seen (arrows) with increasing ADC values. ADC histograms show an increase in mean ADC for the upper abdominal lymph nodes (prior 1,124 ␮m2/s, SD ⫾ 316; current 1,658 ␮m2/s, SD ⫾ 373). The bottom right panel shows a segmented histogram with a cutoff using ADC values of 650 and 1,500 ␮m2/s. L, liver; S, spleen; LK and RK, left and right kidney; D, intervertebral disk. Images were processed using Oncotreat Software.

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Figure 8 The PRM methodology PRMsADC are created by spatially registering pre- and posttherapy ADC volumes. Differences in the registered voxel data between the 2 examinations are determined, and a threshold value for ADC change is applied. Changes in voxels are then color labeled (such as “red” for increase, “green” for no change, and “blue” for decrease) and overlaid onto anatomic images, enabling the spatial distribution of changed voxels to be appreciated. Images were processed using Oncotreat Software.

response to a therapeutic intervention; the levels of these thresholds are yet to be defined and are urgently needed.

Lesion Detection and Characterization DW-MRI can be used for lesion detection because it incorporates 2 contrast mechanisms: increased water content and impeded water diffusivity, both of which increase the signal intensity of tumors on high b-value images. Lesion detection is usually done on b-value images in which there is maximal contrast between lesions and surrounding normal tissues, which will differ by the anatomic location. Because of signalto-noise considerations, this often means that detection is best performed on lower b-value images, particularly in the liver. The basic premise for lesion characterization is similar to that for lesion detection, but it is the signal intensity at high b-values (b value ⬎500-1,000 s/mm2) together with corresponding morphologic images and ADC values, which enables lesion characterizations to be undertaken (Table 2). In this section, we first consider the use of regional DW-MRI for improving lesion detection taking as examples tumors of the prostate gland and liver. We then consider the use of wholebody DW-MRI as a method for assessing the burden of metastatic disease. Lesion characterization is discussed in the context of brain and liver lesions as well as in the prostate gland.

Prostate Gland In prostate gland evaluations, conventional T2-weighted images are of limited value for lesion detection particularly

within the transition zone (TZ, where 30% of tumors occur) because it is often impossible to distinguish benign prostatic hyperplasia (BPH) from malignant tumors. Even in the peripheral zone, the detection of cancer can be difficult because of coexistent prostatitis, prostatic intraepithelial neoplasia, biopsy hemorrhage, hormonal therapy effects, and fibrosis. The detection of prostate cancer is done using both high b-value images and ADC maps. Clinical experience shows that lesion detection in the prostate gland often requires the usage of ultrahigh b-value images (sometimes up to 2,000 s/mm2) because of the need to minimize the normal high signal intensity arising from normal glandular tissues (Fig. 9).50 Importantly, observing ADC maps calculated with ultrahigh b-values images are less successful in lesion detection because of low signal-to-noise ratio of the ADC images that are produced.51 Oftentimes, DW-MRI can be helpful for detecting tumors even when hemorrhage from biopsies is present.52 There is a growing body of evidence that mean ADC values of malignant nodules are lower than those of normal glandular peripheral zone.53 For example, Tamada et al37 evaluated 90 patients with prostate cancer comprising 69 peripheral zone cancers and 32 transition zone tumors. Mean ADC values of cancer regions in both peripheral zone (1,020 ⫾ 250 ␮m2/s) and TZ (940 ⫾ 210 ␮m2/s) were significantly lower than those of the corresponding normal volunteers (P ⬍ .0001).37 Moreover, in a prospective, histopathologically correlated study, Haider et al demonstrated combined T2weighted and DW-MRI to be superior to T2-weighted MRI alone in the detection of prostate carcinoma within the peripheral zone. However, no significant difference in the detection of malignant nodules within the central gland was

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Figure 9 The detection of prostate cancer with diffusion MRI in a 79-year-old man with prostate cancer (Gleason 3 ⫹ 4; prostate specific antigen (PSA), 8.4 ng/mL). Note the marked benign prostatic hypertrophy. A tumor is not visualized on the T2-weighted image but is seen as a hyperintense lesion on the b800 and b1400 images with corresponding low ADC values (arrow). Note how the prostatic signal reduces to the background on the b1400 image, enabling improved depiction of the tumor.

shown because BPH nodules have lower ADC values related to their fibrous nature.54 Studies combining T2W and DW for localizing prostate cancer show that sensitivity in the detection of significant cancers within the peripheral zone increases compared with T2W imaging alone.54,55 The average sensitivity for the detection of clinically significant cancers has been reported to be 71% to 87% and specificity 61% to 89%54,56-59 depending on the histopathological gold standard used. Interestingly, important data are now beginning to accumulate showing that ADC values correlate inversely with tumor aggressiveness,60 with lower tumor ADC values indicating higher Gleason scores.36,37,52,59 Langer et al61 recently reported that sparse tumors (ie, those with ⬎50% of cross sectional tumor area composed of normal peripheral zone) had ADC values similar to normal peripheral zone tissues, indicating that the smaller the percentage of tumor involvement on core biopsies, the less likely was the delectability of tumors.36 These data taken together suggest that DW-MRI of the prostate maybe helpful for directing patient management.

Liver Lesions DW-MRI is also increasingly being used for the evaluation of focal liver lesions.62 Lesion detection is best at lower b-value images (eg, 50-150 s/mm2). As discussed previously, vessels are usually dark at these values (black blood images), thus enabling improved contrast between liver and hyperintense lesions. In this regard, it should be noted that most liver lesions regardless of their nature are bright at these low b values and that lesion characterization is achieved by noting signal intensity at higher b values. DW-MRI using low bvalue images has been shown to be significantly better at detecting focal liver lesions (88%) than T2-weighted imaging (70%).63 In fact, DW-MRI when combined with other MRI methods seems to be optimal for lesion detection.62 For example, Koh et al64 recently compared detection rates of liver metastases in colorectal cancer patients between 2 MRI techniques: manganese dipyridoxyldiphosphate (a hepatocytespecific contrast agent)-enhanced MRI and DW-MRI. Using receiver operating characteristic curves analyses, they found that the combined dataset had higher diagnostic accuracy.

Diffusion MRI and cancer patients With regard to liver lesion characterization, it has been shown that the mean ADC values of malignant lesions are often significantly lower than those of benign lesions although varying degrees of overlap exist depending on the anatomic location. For example, in a prospective study, in 66 patients with focal liver lesions, Taouli et al65 found the mean ADCs of malignant lesions (1,080 ⫾ 500 ␮m2/s) to be significantly lower than nonmalignant lesions (2,450 ⫾ 960 ␮m2/ s). As with other studies, the lowest mean ADC values were found in metastases (940 ⫾ 600 ␮m2/s) and hepatocellular carcinoma (1,330 ⫾ 130 ␮m2/s) in keeping with the highly cellular nature of these lesions. The highest ADC values were found in simple cysts (3,630 ⫾ 560 ␮m2/s) and to a lesser extent hemangiomas (2,450 ⫾ 960 ␮m2/s). Benign lesions of hepatocellular origin (eg, focal nodular hyperplasia and hepatic adenomas) were found to have intermediate ADC values. However, when cystic, necrotic, and treated metastases are included, then liver lesion characterization is not as good.63

Brain Lesions The main clinical application of DW-MRI in the brain is in the diagnosis of acute ischemia, where DW-MRI can be used with perfusion imaging to predict the likely clinical outcome after thrombolytic therapy. In the differential diagnosis of cystic brain lesions, DW-MRI can help distinguish abscesses from necrotic primary brain tumors, such as high-grade gliomas, with lower ADC values usually detected in abscesses. Impeded water diffusivity in an abscess is probably related to the presence of microorganisms, macromolecules, and intact inflammatory cells.66 For solid intracranial masses, the main determinant of diffusivity is the volume of the extracellular space. Tumor hypercellularity decreases the extracellular volume and thus leads to impeded diffusion, such as that seen in primary central nervous system lymphomas. Lower ADC values in lymphomas, compared with gliomas, correlates well with cellularity.67 We have already noted that ADC values also correlated with tumor cellularity within astrocytomas27,29 although the value of DW-MRI in tumor grading is still debatable. DW-MRI has also been investigated as a surrogate marker of response to treatment in brain tumors, with increased diffusion values detected shortly after treatment initiation suggesting a favorable outcome.68,69

Whole Body DW-MRI Whole-body DW-MRI is an attractive technique because it enables “at-a-glance” assessments, immediately drawing attention to all potential lesions and thus reducing the image interpretation time of anatomic whole-body MRI alone.21 The ability of whole-body DW-MRI to detect lesions is highly dependent on the tumor histologic type and grade. Thus, detection ability on high b-value images is better for breast cancers, myeloma, and lymphoma and for tumors with highly packed small cells, such as neuroendocrine tumors and small cell cancers (Figs. 4 and 6). Metastatic disease from these tumors appears bright on high b-value images. In contradistinction, primary and metastases from renal cancers are

131 often less well observed, probably because of lower packing density and larger cellular size resulting in generally higher ADC values.22,38,70 Furthermore, it has been noted that welldifferentiated or low-grade masses are less well seen because they have a more organized structure causing less restriction of tissue water. The latter point is not a hard-and-fast rule because tumor grade is determined by many cytologic features aside from cellular density. As a general rule, lytic bony metastases are better seen than sclerotic or treated metastases because of the lower water content of sclerotic and treated lesions. There are several whole-body DW-MRI “blind spots” in which lesion detection is impaired (leading to false negative results), including the mediastinum, at the pulmonary hila, and high up in the left hepatic lobe just beneath the heart. At each of these sites, it is complex (incoherent) motion that leads to signal losses on high b-value images. Lastly, small lung metastases are poorly shown on whole-body DW-MRI. It is for these reasons that whole-body DW-MRI is considered a supplement for anatomic whole-body MRI, improving the performance of the latter.43 Whole-body DW-MRI is better than CT scans and bone scans for detecting lytic bony disease (Fig. 6) while at the same time providing added information about soft tissue disease. Whole-body DW-MRI excels at lesion detection particularly in the bone marrow, but its ability to characterize softtissue lesions is more limited. There are several reasons for this, particularly when signal intensity information alone is used for assessments without regard to morphologic appearances and ADC values. Another reason for poorer specificity of DW-MRI in comparison with FDG-PET scanning is the fact that there are normal tissues that are of high signal intensity on DW-MRI either because of long T2 relaxation times and/or impeded water movements. These include brain, spinal cord, nerve plexus, ganglia, normal lymph nodes, and spleen (highly variable). The adrenal and salivary glands, penis, testes, ovaries, normal endometrium, and prostate gland can all appear of intermediate-to-high signal intensity. The normal red bone marrow can have a very varied appearance depending on patient age, sex, and bone mineral density. Some causes of “false-positives” are not insurmountable using anatomic knowledge and the guidance given in Table 2. A case in point is lymph node assessment on DW-MRI, which is still most commonly evaluated by using size criteria;71 the latter is known to be a poor discriminator between benign and malignant nodal disease. There is growing evidence that the relative intensity of lymph nodes on high b-value images (eg, comparing pelvic nodes with groin nodes)43,72 and ADC values provide some discriminatory value.73-81 The hope that DW-MRI may improve the characterization of normal-sized, pelvic nodes harboring microscopic metastases has not yet been realized.82 A cause of false-positive (hyperintense signal intensity with low ADC values) lymph nodes is follicular hyperplasia because of the dense packing of lymph nodes with lymphocytes. Recently, a novel approach for increasing the accuracy of nodal evaluation with DW-MRI has been proposed by Thoeny et al,83 who performed DW-MRI after

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132 the intravenous injection of a novel magnetic resonance lymphography contrast agent based on the ultrasmall superparamagnetic iron oxide (USPIO) particles (ferumoxatran-10). The presence of the USPIO particles only within normal nodes extinguishes the signal on high b-value images, thus improving the detection of lymph node metastases in normal-sized nodes in patients with bladder and prostate cancers.83 Unfortunately, formally testing this novel approach has been curtained by the recent withdrawal from further investigations of USPIOs at the time of the writing of this article.

Pretreatment Prediction of Therapy Effectiveness A number of preclinical and clinical studies have noted that pretherapy ADC values may indicate therapy outcomes, with most studies showing that tumors with higher ADC values respond less favorably to treatments.84-93 In this context, high pretherapy ADC values may reflect the presence of microscopic and macroscopic necrosis. Tumor necrosis is well recognized to be associated with poorer therapy outcomes.94,95 The linking hypothesis appears to be the association between necrosis and low oxygen tension in tumors. Tumor hypoxia mediates resistance to chemotherapy, radiation, and photodynamic therapy and leads to the selection of more aggressive tumor clones capable of evading the hostile tumor microenvironment.96,97 Lemaire et al85 showed that rat mammary tumors with lower pretreatment ADC values did not regrow 7 days after chemotherapy, unlike the tumors with higher initial ADC values in which tumor regrowth was seen; they ascribed these observations to necrosis and suggested that hypoxia resulted in clonal selection of more aggressive therapy resistant tumor cells.85 Roth et al84 examined C26 colon tumors and also showed a similar correlation between high pretreatment ADC values and poor responsiveness to doxorubicin chemotherapy. There have been several studies in primary rectal cancer patients showing that higher tumor ADC values are associated with poorer responses when patients are treated with chemoradiation.88-91 Devries et al89 highlighted the potential pitfall of using mean tumor ADC values for prognostication in 34 rectal cancer patients undergoing chemoradiation.89 They showed no differences between mean pretreatment ADC in the 18 patients who responded and the 16 patients who were nonresponders. However, histogram plots of tumor ADC values revealed that the nonresponding group had a greater fraction of high ADCs compared with the responding group, implying that nonresponders had more necrosis. Similar correlations between high pretherapy ADC values and poorer response to chemotherapy have also been shown in patients with metastatic cancer to the liver.86,87 Koh et al86 reported an inverse correlation between the percentage size reduction of metastatic liver lesions from colorectal cancer and pretreatment ADC values (r ⫽ ⫺0.58; P ⫽ .03).86 However, Cui et al87 (who studied liver metastases from colorectal

and stomach cancers) found a weaker correlation (r ⫽ ⫺0.293, P ⫽ .006) between tumor size reduction and pretreatment ADC values, suggesting that tumor histology and intrinsic chemotherapy effectiveness may play important roles in determining the ability of baseline water diffusivity to predict response to chemotherapy and radiation. It should be noted that the association between high ADC values and less favorable responses to treatments does not apply to all therapy types.98 Indeed, the opposite has been found for vascular disruptive agents, which induce massive central necrosis within tumors. Early preclinical99,100 and clinical studies101 indicate that higher pretreatment ADC values were more susceptible to the effects of combretastatinA4-phosphate (see later for more details).

Assessing Therapies That Induce Tumor Cell Death As a general rule, any pharmacologic, physical, or radioactive process that causes necrosis or cellular lysis will lead to increases in extracellular space water diffusion with lowering of signal intensity on high-b-value images and corresponding increases in ADC values.11,102,103 Because cellular death in response to treatment precedes changes in lesion size, changes in DW-MRI may be an effective early marker of response for therapies that induce apoptosis.11,102 Thus, most studies have shown that successful tumor cell killing is reflected by increases in tumor ADC values (Figs. 7 and 10). Increases in ADC values with tumor cell death are often transient, and ADC values fall subsequently as dead cells are removed by tissue macrophages, as tissues become remodeled with vascular normalization, and as mature fibrosis develops.104 Of course, if tumors become therapy resistant, then that too may lower ADC values to tumor levels, a potential pitfall.

Chemotherapy Rising ADC values with successful chemotherapy have been noted in several anatomic sites, including breast cancers,105,106 primary and metastatic cancers to the liver,87,107,108 primary bone sarcomas,109,110 and brain malignancies.46,111 In bone sarcomas, increased ADC values are associated with pathologic response gauged by the extent of necrosis in resection specimens.112-114 In soft-tissue sarcomas treated with chemotherapy, increases in ADC values are associated with reductions in tumor size and vice versa; as a result, strong negative correlations between tumor volume and ADC changes have been reported (r ⫽ ⫺0.925, P ⬍ 0.0001).115 Both animal tumors116,117 and some human cancer studies have shown that increases in ADC values can occur rapidly after the first dose of chemotherapy at a time consistent with the onset of apoptosis (Fig. 7). A recent human study looked at the onset of changes in ADC in metastatic liver lesions from stomach and colorectal cancer, noting ADC increases as early as 3 to 7 days after the first dose of chemotherapy and also observed that ADC increases correlated with therapy response.87 Theilmann et al118 evaluated 13 women with breast

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Figure 10 The radiation therapy effect on bony metastasis in a 69-year-old man with metastatic sigmoid cancer. The metastasis in the right acetabulum (arrow), which has been irradiated on 2 previous occasions, shows high ADC values indicating largely inactive disease. The normal soft tissues around the radiated bone also show elevation of the ADC, thus defining the anterior-posterior radiation portal used for radiotherapy delivery (highlighted as a semitransparent area on the axial and coronal ADC maps). Note also the hyperintense signal intensity on the short tau inversion recovery (STIR) sequence indicating increased tissue water in the irradiated area.

cancer evaluating 60 liver metastases before and after chemotherapy. Increases in ADC values were observed 4 to 11 days after the start of therapy particularly in smaller lesions. A number of studies have evaluated ADC changes in patients with primary breast cancer treated with neoadjuvant chemotherapy.119,120 Stepwise increases in ADC values were shown with each therapy cycle in responding patients, with changes in diffusivity preceding changes in tumor size/volume.

Radiotherapy and Chemoradiation A number of preclinical and clinical studies have evaluated the effects of external beam radiation therapy with DW-MRI. Preclinical studies show that ADC increases occur rapidly in tumors that are radiation sensitive,121 with changes visible as early as 24 to 72 hours after a single large, fraction of radiation.122 This finding occurred in the absence of tumor size change and coincided with metabolic disruption characterized by increases in lipid signals and decreases in lactate resonances on hydrogen spectroscopy.122 Such increases in ADC values are not seen in squamous cell cancer that is radiation resistant unless concomitant chemotherapy is given.123 Increases in ADC values appear to occur incrementally in fractionated regimens with the greatest increases visible at the end of therapy.104 These increases in ADC values are considered

related to tumor cell death, the development of tissue edema related to inflammation, and increased microvessel leakiness. Increases in ADC values have also been seen in patients with malignant liver lesions 7 to 14 days after the start of external beam radiotherapy, with greater increases correlating with higher doses.124 Early increases in ADC values (1-3 weeks) have also been noted for squamous cell head and neck cancers, brain gliomas, locally advanced rectal cancer, and uterine cervical cancers treated with chemoradiation.47,91-93,125,126 In fact, a number of clinical studies have shown that the failure to increase tumor ADC values in response to radiation or chemoradiotherapy results in a poorer response to therapy.46,47,124,127 Occasionally, within the malignant bone marrow, massive liquefactive necrosis occurs in response to successful chemotherapy and radiation treatment. This has been noted particularly in patients with multiple myeloma, lymphoma, and occasionally in other solid metastatic neoplasms. Persistent high signal intensity is observable on high b-value images with high ADC values, which can last for years. By contrast, nonmetastatic red bone marrow responds with fatty atrophy when irradiated resulting in low signal intensity on high bvalue images and low ADC values (Fig. 3). In contradistinction, when radiotherapy is given to normal soft tissues, there is often a prolonged, persistent elevation of ADC often defining the radiation portal, which is presumed to be caused by

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Figure 11 The effect of bevacizumab on brain gliosarcoma in a 25-year-old man with a gliosarcoma of the left cerebral hemisphere. The left column shows before therapy and the middle column after 2 weeks of bevacizumab. The top row shows postcontrast medium enhanced T1-weighted images, and the bottom row shows the corresponding T2weighted image. The region of interest outlines the T2-weighted hyperintense change, including the enhancing mass. The color overlay on the T2-weighted image is the corresponding ADC map. Note that the white-matter change extends across the midline through the corpus callosum. Reductions in the size and of enhancement of the tumor mass are seen. Reductions in enhancement are consistent with reduced vascular permeability with blood brain-barrier normalization. Note the reductions in the mean ADC values (1,336-1,137 ␮m2/s), which are also shown in the corresponding histograms (top right panel). The ADC (pre- vs posttreatment) scatter plot shows that 44.5% of voxels reduce in the ADC value by more than 200 ␮m2/s; 5.3% of voxels are unchanged. A parametric response ADC map is shown in the bottom right panel (threshold value of 200 ␮m2/s). Voxels are color labeled (“red” for increase, “green” for no change, and “blue” for decrease), allowing the spatial distribution of changed voxels to be appreciated. Decreasing ADC values are seen both in the tumor mass but also in the associated white-matter change. Images were processed using Oncotreat Software.

ongoing microvascular hyperpermeability and tissue inflammation (Fig. 10).

Assessing Therapies Targeting Tumor Blood Vessels A number of studies have shown that antiangiogenic treatments directed toward vascular endothelial growth factor (VEGF) causes reductions in tumor ADC values that coincide with reductions in contrast enhancement (Fig. 11). This observation has been noted in brain glioblastomas treated with the anti-VEGF antibody bevacizumab128 and with the small molecular weight tyrosine kinase inhibitor of VEGF receptors cediranib.129 Similar reductions in ADC values have been noted for sorafenib, an antiangiogenic therapy used for treatment of hepatocellular carcinoma.130 The mechanisms for this observation has been investigated in brain xenograft models and in patients with brain tumors who were given cediranib as a monotherapy.131 The principal explanation appears to be reductions of lesion extravascular-extracellular space secondary to vascular normalization and the lowering of vascular permeability.129,132 Batchelor et al129 evaluated patients with recurrent glioblastomas treated with cediranib with multifunctional MRI examinations.129 They were able to show rapid reductions in microvessel permeability (inflow transfer constant), extracellular leakage space, and water diffusivity after treatment, which was interpreted as evidence of microvessel normalization.

Readers should be aware that the direct opposite (increased ADC) can be seen if there is significant tumor necrosis caused by antivascular treatments. Increases in ADC values have been noted for vascular disruptive agents, which induce massive necrosis within tumor centers. For example, Thoeny et al99,100 evaluated the effects of the vascular disruptive agent combretastatin-A4-phosphate on rat rhabdomyosarcoma and that ADC increases coincided with the onset of histologic necrosis. Rising ADC values have also been seen in clinical studies by Koh et al101 when combretastatin-A4-phosphate was given with the anti-VEGF antibody bevacizumab. Like the study of Thoeny et al,99,100 Koh et al101 found that increases in ADC were more likely to occur in tumors with higher pretreatment ADC values, indicating that partially necrotic tumors were more susceptible to the effects of combretastatin-A4-phosphate.

Potential in Radiation Therapy Planning Morphologic MRI has several intrinsic advantages, including superb soft-tissue image contrast, any plane imaging, and volume acquisition capability together with a lack of bony artifacts, making it well suited for the delineation of organ boundaries and tumors. MRI-guided target volume definitions are now the standard of care for some pelvic organs, for the brain, and for head and neck tumors. Combining func-

Diffusion MRI and cancer patients tional MRI (fMRI) information with radiotherapy has the potential to extend the sophistication of therapy planning (and to increase its complexity!).

Rationale The recently developed ability to combine image depicted biology with IGRT techniques has opened the way for further refinements of target delineation and dose delivery, such that it is now possible to shape dose volume distributions not only to the geometry of targets but also to differences in the radiobiology across tumors.133,134 As a result, treatments can be given to the desired dose to an organ or tumor with dose modulation based on radiobiologically relevant characteristics, such as tumor grade, proliferation index, hypoxia, or other features related to therapeutic outcome. All of this could be done while minimizing normal tissue radiation toxicity. fMRI techniques enable the spatial depiction of key cancer hallmarks, including proliferation, angiogenesis, metastasis, apoptosis, and altered metabolism.135 Histologic correlates of DW-MRI have already been discussed previously but include cell density, tumor proliferation index,33,34 tumor grade,35-38 and the presence of necrosis.28,39,40 We have also noted that tumors with high pretherapy ADC values respond less favorably to treatments, including radiotherapy,84-93 probably because high tumor ADC values indicate the presence of necrosis, which is associated with tumor hypoxia and then in turn to poorer therapy outcomes.94-97

Potential Applications Proof of concept studies, mostly retrospective, have shown that fMRI techniques can influence the placement of radiation treatment fields for organs, such as the prostate gland where preferential targeting of the dominant cancer nodule/ index lesion for higher dose delivery can be undertaken. As an example, in the prostate gland, DW-MRI can be helpful for detecting the presence of prostate cancer particularly in the peripheral zone on the basis of lower mean ADC values of malignant nodules37,53,54,56-59 (Fig. 9). We have also noted that DW-MRI can be helpful for detecting likely foci of containing higher Gleason grade cancers,36,37,52,59 thus enabling the possibility to deliver high doses of radiation to these more aggressive, larger regions. Potentially, DW-MRI can be repeated during a course of radiotherapy with a view to dynamic adaptation of radiotherapy plans as tumors respond to therapy. We have already noted that there is emerging evidence that the failure to see increased ADC values early after beginning radiation therapy may indicate radiation resistance,46,47,124,127 thus DW-MRI may provide the opportunity to help modulate the radiation dose to the in vivo response of tumors. For patients undergoing radiotherapy, it is argued that for those with an appreciable risk of microscopic lymph node involvement, irradiating regional nodal regions might improve outcomes.136 The ability to accurately map nonenlarged lymph nodes, which could be treated with intensitymodulated radiation therapy or conformal techniques to

135 reduce normal tissue toxicity, is needed. Limitations of CT methods include that fact that most normal nodes are not be visible on planning CT scans, thus necessitating the use of vascular anatomy and/or pelvic bony landmarks as surrogates for the positions of lymph nodes. Studies on the spatial distribution of nodes using the magnetic resonance lymphographic contrast agent ferumoxatran-10 have shown that lymph nodes are more closely related to vascular landmarks than to bony anatomy. Thus, volume expansion based on vascular anatomy in the pelvis encompasses 95% to 99% of lymph nodes detected on magnetic resonance lymphography in gynecologic tumors.137 Mapping of normal nodes in relation to the bony pelvis and pelvic vessels can be readily done by DW-MRI without the need for any contrast medium (Fig. 12). DW-MRI has some ability to characterize nodes based on nodal size and the relative intensity of nodes on high b-value images,43,72 with ADC values providing additional discriminatory value.73-81 With regard to the pelvis, it is unlikely that DW-MRI will improve the characterization of normal-sized nodes harboring microscopic metastases from pelvic cancers.82 Nevertheless, DW-MRI could be used as an aid to create individualized clinical target volumes. Lymph nodes that appear suspicious on the basis of signal intensity on high b-value images or by ADC values would be included, and could be considered for a “boost” dose of radiotherapy. The remaining but not obviously involved lymph nodes in regions considered at risk would be treated on the basis that although DW-MRI can show some microscopic disease, not all metastases will be depicted. Individualized clinical target volumes defined in this manner have the potential to further reduce the volume of normal tissue irradiated over and above the method based on vascular expansion described earlier.77 Intrinsic to the radiotherapy planning process is avoidance or minimization of normal tissue toxicity, which in the brain includes minimization of radiation dose to vital sensory and motor functions. Recent data have shown that fMRI, which monitors changes in regional brain vascularity bought on by the performance of a task in an activation paradigm, can enable statistical maps to be generated indicating the regions of the brain that respond significantly to a task. fMRI when combined with fiber tracking techniques using diffusion tensor image (DTI) can successfully show the relationships between tumors and eloquent adjacent areas as well as involvement of connecting white matter tracts. Such techniques have shown significant interindividual differences in the positioning and involvement of eloquent tissues and their connecting white-matter tracts. It has been stated that fMRI (for eloquent areas identification) and DTI (for fiber tracking) should be incorporated into image-guided radiotherapy (IGRT) planning of brain tumors.138 However, when such functional depictions are being incorporated into planning volumes, it should be borne in mind that there may be areas that are silent (that is not tested for) but eloquent. So these techniques should be used with extreme caution to be ensure that brain areas are not sacrificed by being classified as being nonfunctional but that may be important for other sets of skills or behaviors.

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Figure 12 A depiction of normal nodes with diffusion MRI in a 64-year-old man with prostate cancer (Gleason 3 ⫹ 4; serum PSA 7.1 ng/mL). On T1- and T2-weighted images, normal-sized pelvic side wall nodes (arrow heads) are difficult to appreciate. The b1100 diffusion-weighted image clearly depicts hyperintense, normal-sized nodes. Fusion of the b1100 and T1W images enables nodal positions to be determined with regard to pelvic sidewall vessels. Note also the hyperintense signal of small bowel loops and of the rectal mucosa.

Realizing the Opportunity

Conclusions

When considering the opportunities for DW-MRI to influence radiotherapy planning afore mentioned, it should be remembered that the perceived advantages of such approaches are without a sound evidence base regarding selection of patients who would benefit from these more complex approaches and whether improved patient outcomes will ultimately be seen (incomplete clinical validation). Furthermore, DW-MRI itself is at an early stage of development with incomplete validation or acceptance of standards for data acquisition and analysis.8 No broadly applicable frameworks for the development of functional and molecular imaging in support of refining radiation dose distribution have as yet emerged. Additionally, there are practical challenges for incorporating both anatomic and fMRI into image-guided radiotherapy systems. These include image distortions, the lack of electron density information, and the fact that not all patients are suitable for MRI. Distortions are greater for DWMRI, which may be a limiting factor for its direct use in guiding radiotherapy. It will be necessary to overcome these limitations if DW-MRI is to make a significant impact on IGRT in the longer-term.

We need to think of DW-MRI as a quantitative method of evaluating tumors or tissues relating to their microstructural organization, perfusion, and cellularity. We have seen that the direction, extent, and duration of ADC changes can vary depending on the type of treatment administered, tumor or tissue type, and the timing of imaging with respect to the treatment given. To be able to understand these findings, DW-MRI needs to be correlated with other complementary anatomic and fMRI techniques, which together provides a wealth of biologically relevant information concerning the nature of tumor response to therapy.135 Observations from multiparametric MRI studies indicate that ADC changes are dependent on complex interplays of biophysical processes in response to therapy, reemphasizing the need to better understand therapy-induced tissue changes that may be reflected in DW-MRI parameters. DW-MRI data are clinically useful in terms of disease detection and characterization. In the disease-response setting, many studies show that changes can be directly related to mechanism of action of treatments. However, it has not been convincingly shown that DW-MRI en-

Diffusion MRI and cancer patients ables personalization of therapy response or that survival of cancer patients is improved by making use of DW-MRI response data. DW-MRI is emerging as a powerful clinical tool for directing the management of patients with cancer.

References 1. Buckler AJ, Mozley PD, Schwartz L, et al: Volumetric CT in lung cancer: An example for the qualification of imaging as a biomarker. Acad Radiol 17:107-115, 2010 2. Benjamin RS, Choi H, Macapinlac HA, et al: We should desist using RECIST, at least in GIST. J Clin Oncol 25:1760-1764, 2007 3. Llovet JM, Ricci S, Mazzaferro V, et al: Sorafenib in advanced hepatocellular carcinoma. N Engl J Med 359:378-390, 2008 4. Friedman HS, Prados MD, Wen PY, et al: Bevacizumab alone and in combination with irinotecan in recurrent glioblastoma. J Clin Oncol 27:4733-4740, 2009 5. Hurwitz H, Fehrenbacher L, Novotny W, et al: Bevacizumab plus irinotecan, fluorouracil, and leucovorin for metastatic colorectal cancer. N Engl J Med 350:2335-2342, 2004 6. Van den Abbeele AD, Badawi RD: Use of positron emission tomography in oncology and its potential role to assess response to imatinib mesylate therapy in gastrointestinal stromal tumors (GISTs). Eur J Cancer 38:S60-S65, 2002 (suppl 5) 7. Workman P, Aboagye EO, Chung Y-L, et al: Minimally invasive pharmacokinetic and pharmacodynamic technologies in hypothesis-testing clinical trials of innovative therapies. J Natl Cancer Inst 98:580598, 2006 8. Padhani AR, Liu G, Koh DM, et al: Diffusion-weighted magnetic resonance imaging as a cancer biomarker: Consensus and recommendations. Neoplasia 11:102-125, 2009 9. Koh DM, Collins DJ: Diffusion-weighted MRI in the body: Applications and challenges in oncology. AJR Am J Roentgenol 188:16221635, 2007 10. Thoeny HC, De Keyzer F: Extracranial applications of diffusionweighted magnetic resonance imaging. Eur Radiol 17:1385-1393, 2007 11. Patterson DM, Padhani AR, Collins DJ: Technology insight: Water diffusion MRI—A potential new biomarker of response to cancer therapy. Nat Clin Pract Oncol 5:220-233, 2008 12. Hagmann P, Jonasson L, Maeder P, et al: Understanding diffusion MR imaging techniques: From scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiographics 26:S205-S223, 2006 (suppl 1) 13. Le Bihan D, Mangin JF, Poupon C, et al: Diffusion tensor imaging: Concepts and applications. J Magn Reson Imaging 13:534-546, 2001 14. Basser PJ, Jones DK: Diffusion-tensor MRI: Theory, experimental design and data analysis—A technical review. NMR Biomed 15:456467, 2002 15. Goebell E, Paustenbach S, Vaeterlein O, et al: Low-grade and anaplastic gliomas: Differences in architecture evaluated with diffusion-tensor MR imaging. Radiology 239:217-222, 2006 16. Stadlbauer A, Ganslandt O, Buslei R, et al: Gliomas: Histopathologic evaluation of changes in directionality and magnitude of water diffusion at diffusion-tensor MR imaging. Radiology 240:803-810, 2006 17. Mori S, Frederiksen K, van Zijl PC, et al: Brain white matter anatomy of tumor patients evaluated with diffusion tensor imaging. Ann Neurol 51:377-380, 2002 18. Mori S, van Zijl PC: Fiber tracking: Principles and strategies—A technical review. NMR Biomed 15:468-480, 2002 19. Melhem ER, Mori S, Mukundan G, et al: Diffusion tensor MR imaging of the brain and white matter tractography. AJR Am J Roentgenol 178:3-16, 2002 20. Koh DM, Takahara T, Imai Y, et al: Practical aspects of assessing tumors using clinical diffusion-weighted imaging in the body. Magn Reson Med Sci 6:211-224, 2007 21. Kwee TC, Takahara T, Ochiai R, et al: Whole-body diffusion-weighted magnetic resonance imaging. Eur J Radiol 70:409-417, 2009 22. Manenti G, Di Roma M, Mancino S, et al: Malignant renal neoplasms:

137

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

36.

37.

38.

39.

40.

41.

42.

Correlation between ADC values and cellularity in diffusion weighted magnetic resonance imaging at 3 T. Radiol Med (Torino) 113:199-213, 2008 Hayashida Y, Hirai T, Morishita S, et al: Diffusion-weighted imaging of metastatic brain tumors: Comparison with histologic type and tumor cellularity. AJNR Am J Neuroradiol 27:1419-1425, 2006 Humphries PD, Sebire NJ, Siegel MJ, et al: Tumors in pediatric patients at diffusion-weighted MR imaging: Apparent diffusion coefficient and tumor cellularity. Radiology 245:848-854, 2007 Zelhof B, Pickles M, Liney G, et al: Correlation of diffusion-weighted magnetic resonance data with cellularity in prostate cancer. BJU Int 103:883-888, 2009 Liu Y, Bai R, Sun H, et al: Diffusion-weighted magnetic resonance imaging of uterine cervical cancer. J Comput Assist Tomogr 33:858862, 2009 Sugahara T, Korogi Y, Kochi M, et al: Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging 9:53-60, 1999 Lyng H, Haraldseth O, Rofstad EK: Measurement of cell density and necrotic fraction in human melanoma xenografts by diffusion weighted magnetic resonance imaging. Magn Reson Med 43:828-836, 2000 Ellingson BM, Malkin MG, Rand SD, et al: Validation of functional diffusion maps (fDMs) as a biomarker for human glioma cellularity. J Magn Reson Imaging 31:538-548, 2010 Yoshikawa MI, Ohsumi S, Sugata S, et al: Relation between cancer cellularity and apparent diffusion coefficient values using diffusionweighted magnetic resonance imaging in breast cancer. Radiat Med 26:222-226, 2008 Nonomura Y, Yasumoto M, Yoshimura R, et al: Relationship between bone marrow cellularity and apparent diffusion coefficient. J Magn Reson Imaging 13:757-760, 2001 Tang GY, Lv ZW, Tang RB, et al: Evaluation of MR spectroscopy and diffusion-weighted MRI in detecting bone marrow changes in postmenopausal women with osteoporosis. Clin Radiol 65:377-381, 2010 Wang XZ, Wang B, Gao ZQ, et al: Diffusion-weighted imaging of prostate cancer: Correlation between apparent diffusion coefficient values and tumor proliferation. J Magn Reson Imaging 29:1360-1366, 2009 Calvar JA, Meli FJ, Romero C, et al: Characterization of brain tumors by MRS, DWI and Ki-67 labeling index. J Neuro Oncol 72:273-280, 2005 Arvinda HR, Kesavadas C, Sarma PS, et al: Glioma grading: Sensitivity, specificity, positive and negative predictive values of diffusion and perfusion imaging. J Neuro Oncol 94:87-96, 2009 Woodfield CA, Tung GA, Grand DJ, et al: Diffusion-weighted MRI of peripheral zone prostate cancer: Comparison of tumor apparent diffusion coefficient with Gleason score and percentage of tumor on core biopsy. AJR Am J Roentgenol 194:W316-W322, 2010 Tamada T, Sone T, Jo Y, et al: Apparent diffusion coefficient values in peripheral and transition zones of the prostate: Comparison between normal and malignant prostatic tissues and correlation with histologic grade. J Magn Reson Imaging 28:720-726, 2008 Sandrasegaran K, Sundaram CP, Ramaswamy R, et al: Usefulness of diffusion-weighted imaging in the evaluation of renal masses. AJR Am J Roentgenol 194:438-445, 2010 Geschwind JF, Artemov D, Abraham S, et al: Chemoembolization of liver tumor in a rabbit model: Assessment of tumor cell death with diffusion-weighted MR imaging and histologic analysis. J Vasc Interv Radiol 11:1245-1255, 2000 Lang P, Wendland MF, Saeed M, et al: Osteogenic sarcoma: Noninvasive in vivo assessment of tumor necrosis with diffusion-weighted MR imaging. Radiology 206:227-235, 1998 Kim H, Morgan DE, Zeng H, et al: Breast tumor xenografts: Diffusionweighted MR imaging to assess early therapy with novel apoptosisinducing anti-DR5 antibody. Radiology 248:844-851, 2008 Liimatainen T, Hakumaki JM, Kauppinen RA, et al: Monitoring of gliomas in vivo by diffusion MRI and (1)H MRS during gene therapy-

A.R. Padhani

138

43.

44.

45.

46.

47.

48.

49.

50.

51.

52.

53.

54.

55.

56.

57.

58.

59.

60.

61.

62. 63.

induced apoptosis: Interrelationships between water diffusion and mobile lipids. NMR Biomed 22:272-279, 2009 Fischer MA, Nanz D, Hany T, et al: Diagnostic accuracy of whole-body MRI/DWI image fusion for detection of malignant tumours: A comparison with PET/CT. Eur Radiol 2010 [Epub ahead of print] Rose CJ, Mills SJ, O’Connor JP, et al: Quantifying spatial heterogeneity in dynamic contrast-enhanced MRI parameter maps. Magn Reson Med 62:488-499, 2009 Galban CJ, Chenevert TL, Meyer CR, et al: The parametric response map is an imaging biomarker for early cancer treatment outcome. Nat Med 15:572-576, 2009 Hamstra DA, Galban CJ, Meyer CR, et al: Functional diffusion map as an early imaging biomarker for high-grade glioma: Correlation with conventional radiologic response and overall survival. J Clin Oncol 26:3387-3394, 2008 Galban CJ, Mukherji SK, Chenevert TL, et al: A feasibility study of parametric response map analysis of diffusion-weighted magnetic resonance imaging scans of head and neck cancer patients for providing early detection of therapeutic efficacy. Transl Oncol 2:184-190, 2009 Lee KC, Bradley DA, Hussain M, et al: A feasibility study evaluating the functional diffusion map as a predictive imaging biomarker for detection of treatment response in a patient with metastatic prostate cancer to the bone. Neoplasia 9:1003-1011, 2007 Ma B, Meyer CR, Pickles MD, et al: Voxel-by-voxel functional diffusion mapping for early evaluation of breast cancer treatment. Inf Proc Med Imaging 21:276-287, 2009 Katahira K, Takahara T, Kwee TC, et al: Ultra-high-b-value diffusionweighted MR imaging for the detection of prostate cancer: Evaluation in 201 cases with histopathological correlation. Eur Radiol, 2010 Kim CK, Park BK, Kim B: High-b-value diffusion-weighted imaging at 3 T to detect prostate cancer: Comparisons between b values of 1,000 and 2,000 s/mm2. AJR Am J Roentgenol 194:W33-W37, 2009 Rosenkrantz AB, Kopec M, Kong X, et al: Prostate cancer vs. postbiopsy hemorrhage: Diagnosis with T2- and diffusion-weighted imaging. J Magn Reson Imaging 31:1387-1394, 2010 Kim CK, Park BK, Kim B: Diffusion-weighted MRI at 3 T for the evaluation of prostate cancer. AJR Am J Roentgenol 194:1461-1469, 2010 Haider MA, van der Kwast TH, Tanguay J, et al: Combined T2weighted and diffusion-weighted MRI for localization of prostate cancer. AJR Am J Roentgenol 189:323-328, 2007 Desouza NM, Reinsberg SA, Scurr ED, et al: Magnetic resonance imaging in prostate cancer: The value of apparent diffusion coefficients for identifying malignant nodules. Br J Radiol 80:90-95, 2007 Lim HK, Kim JK, Kim KA, et al: Prostate cancer: Apparent diffusion coefficient map with T2-weighted images for detection--A multireader study. Radiology 250:145-151, 2008 Kajihara H, Hayashida Y, Murakami R, et al: Usefulness of diffusionweighted imaging in the localization of prostate cancer. Int J Radiat Oncol Biol Phys 74:399-403, 2009 Kim JH, Kim JK, Park BW, et al: Apparent diffusion coefficient: Prostate cancer versus noncancerous tissue according to anatomical region. J Magn Reson Imaging 28:1173-1179, 2008 Yoshimitsu K, Kiyoshima K, Irie H, et al: Usefulness of apparent diffusion coefficient map in diagnosing prostate carcinoma: Correlation with stepwise histopathology. J Magn Reson Imaging 27:132-139, 2008 deSouza NM, Riches SF, Vanas NJ, et al: Diffusion-weighted magnetic resonance imaging: A potential non-invasive marker of tumour aggressiveness in localized prostate cancer. Clin Radiol 63:774-782, 2008 Langer DL, van der Kwast TH, Evans AJ, et al: Intermixed normal tissue within prostate cancer: Effect on MR imaging measurements of apparent diffusion coefficient and T2—sparse versus dense cancers. Radiology 249:900-908, 2008 Taouli B, Koh DM: Diffusion-weighted MR imaging of the liver. Radiology 254:47-66, 2009 Parikh T, Drew SJ, Lee VS, et al: Focal liver lesion detection and characterization with diffusion-weighted MR imaging: Comparison

64.

65.

66.

67.

68.

69.

70.

71.

72.

73.

74.

75.

76.

77.

78.

79.

80.

81.

82.

83.

with standard breath-hold T2-weighted imaging. Radiology 246:812-822, 2008 Koh DM, Brown G, Riddell AM, et al: Detection of colorectal hepatic metastases using MnDPDP MR imaging and diffusion-weighted imaging (DWI) alone and in combination. Eur Radiol 18:903-910, 2008 Taouli B, Vilgrain V, Dumont E, et al: Evaluation of liver diffusion isotropy and characterization of focal hepatic lesions with two singleshot echo-planar MR imaging sequences: Prospective study in 66 patients. Radiology 226:71-78, 2003 Reddy JS, Mishra AM, Behari S, et al: The role of diffusion-weighted imaging in the differential diagnosis of intracranial cystic mass lesions: A report of 147 lesions. Surg Neurol 66:246-250, 2006 Guo AC, Cummings TJ, Dash RC, et al: Lymphomas and high-grade astrocytomas: Comparison of water diffusibility and histologic characteristics. Radiology 224:177-183, 2002 Chenevert TL, Stegman LD, Taylor JM, et al: Diffusion magnetic resonance imaging: An early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst 92:2029-2036, 2000 Sijens PE, Heesters MA, Enting RH, et al: Diffusion tensor imaging and chemical shift imaging assessment of heterogeneity in low grade glioma under temozolomide chemotherapy. Cancer Invest 25:706-710, 2007 Paudyal B, Paudyal P, Tsushima Y, et al: The role of the ADC value in the characterisation of renal carcinoma by diffusion-weighted MRI. Br J Radiol 83:336-343, 2010 Lin C, Luciani A, Itti E, et al: Whole-body diffusion-weighted magnetic resonance imaging with apparent diffusion coefficient mapping for staging patients with diffuse large B-cell lymphoma. Eur Radiol 20:2027-2038, 2010 Whittaker CS, Coady A, Culver L, et al: Diffusion-weighted MR imaging of female pelvic tumors: A pictorial review. RadioGraphics 29: 759-774, 2009 Lin G, Ho KC, Wang JJ, et al: Detection of lymph node metastasis in cervical and uterine cancers by diffusion-weighted magnetic resonance imaging at 3T. J Magn Reson Imaging 28:128-135, 2008 Holzapfel K, Duetsch S, Fauser C, et al: Value of diffusion-weighted MR imaging in the differentiation between benign and malignant cervical lymph nodes. Eur J Radiol 72:381-387, 2009 Nomori H, Mori T, Ikeda K, et al: Diffusion-weighted magnetic resonance imaging can be used in place of positron emission tomography for N staging of non-small cell lung cancer with fewer false-positive results. J Thorac Cardiovasc Surg 135:816-822, 2008 Eiber M, Beer AJ, Holzapfel K, et al: Preliminary results for characterization of pelvic lymph nodes in patients with prostate cancer by diffusion-weighted MR-imaging. Invest Radiol 45:15-23, 2010 Dirix P, Vandecaveye V, De Keyzer F, et al: Diffusion-weighted MRI for nodal staging of head and neck squamous cell carcinoma: Impact on radiotherapy planning. Int J Radiat Oncol Biol Phys 76:761-766, 2010 Nakayama J, Miyasaka K, Omatsu T, et al: Metastases in mediastinal and hilar lymph nodes in patients with non-small cell lung cancer: Quantitative assessment with diffusion-weighted magnetic resonance imaging and apparent diffusion coefficient. J Comput Assist Tomogr 34:1-8, 2010 Chen YB, Liao J, Xie R, et al: Discrimination of metastatic from hyperplastic pelvic lymph nodes in patients with cervical cancer by diffusion-weighted magnetic resonance imaging. Abdom Imaging 2009 [Epub ahead of print] Perrone A, Guerrisi P, Izzo L, et al: Diffusion-weighted MRI in cervical lymph nodes: Differentiation between benign and malignant lesions. Eur J Radiol 2009 [Epub ahead of print] Kosucu P, Tekinbas C, Erol M, et al: Mediastinal lymph nodes: Assessment with diffusion-weighted MR imaging. J Magn Reson Imaging 30:292-297, 2009 Roy C, Bierry G, Matau A, et al: Value of diffusion-weighted imaging to detect small malignant pelvic lymph nodes at 3 T. Eur Radiol 20: 1803-1811, 2010 Thoeny HC, Triantafyllou M, Birkhaeuser FD, et al: Combined ultrasmall superparamagnetic particles of iron oxide-enhanced and diffusion-weighted magnetic resonance imaging reliably detect pelvic

Diffusion MRI and cancer patients

84.

85.

86.

87.

88.

89.

90.

91.

92.

93.

94.

95.

96.

97.

98.

99.

100.

101.

102.

103.

lymph node metastases in normal-sized nodes of bladder and prostate cancer patients. Eur Urol 55:761-769, 2009 Roth Y, Tichler T, Kostenich G, et al: High-b-value diffusion-weighted MR imaging for pretreatment prediction and early monitoring of tumor response to therapy in mice. Radiology 232:685-692, 2004 Lemaire L, Howe FA, Rodrigues LM, et al: Assessment of induced rat mammary tumour response to chemotherapy using the apparent diffusion coefficient of tissue water as determined by diffusion-weighted 1H-NMR spectroscopy in vivo. MAGMA 8:20-26, 1999 Koh DM, Scurr E, Collins D, et al: Predicting response of colorectal hepatic metastasis: Value of pretreatment apparent diffusion coefficients. AJR Am J Roentgenol 188:1001-1008, 2007 Cui Y, Zhang XP, Sun YS, et al: Apparent diffusion coefficient: Potential imaging biomarker for prediction and early detection of response to chemotherapy in hepatic metastases. Radiology 248:894-900, 2008 Dzik-Jurasz A, Domenig C, George M, et al: Diffusion MRI for prediction of response of rectal cancer to chemoradiation. Lancet 360:307308, 2002 DeVries AF, Kremser C, Hein PA, et al: Tumor microcirculation and diffusion predict therapy outcome for primary rectal carcinoma. Int J Radiat Oncol Biol Phys 56:958-965, 2003 Lambrecht M, Deroose C, Roels S, et al: The use of FDG-PET/CT and diffusion-weighted magnetic resonance imaging for response prediction before, during and after preoperative chemoradiotherapy for rectal cancer. Acta Oncol 49:956-963, 2010 Sun YS, Zhang XP, Tang L, et al: Locally advanced rectal carcinoma treated with preoperative chemotherapy and radiation therapy: Preliminary analysis of diffusion-weighted MR imaging for early detection of tumor histopathologic downstaging. Radiology 254:170-178, 2010 Liu Y, Bai R, Sun H, et al: Diffusion-weighted imaging in predicting and monitoring the response of uterine cervical cancer to combined chemoradiation. Clin Radiol 64:1067-1074, 2009 Kim S, Loevner L, Quon H, et al: Diffusion-weighted magnetic resonance imaging for predicting and detecting early response to chemoradiation therapy of squamous cell carcinomas of the head and neck. Clin Cancer Res 15:986-994, 2009 Brizel D, Scully S, Harrelson J, et al: Tumor oxygenation predicts for the likelihood of distant metastases in human soft tissue sarcoma. Cancer Res 56:941-943, 1996 Swinson DE, Jones JL, Richardson D, et al: Tumour necrosis is an independent prognostic marker in non-small cell lung cancer: Correlation with biological variables. Lung Cancer 37:235-240, 2002 Gray LH, Conger AD, Ebert M, et al: The concentration of oxygen dissolved in tissues at the time of irradiation as a factor in radiotherapy. Br J Radiol 26:638-648, 1953 Leek RD, Landers RJ, Harris AL, et al: Necrosis correlates with high vascular density and focal macrophage infiltration in invasive carcinoma of the breast. Br J Cancer 79:991-995, 1999 Niwa T, Ueno M, Ohkawa S, et al: Advanced pancreatic cancer: The use of the apparent diffusion coefficient to predict response to chemotherapy. Br J Radiol 82:28-34, 2009 Thoeny HC, De Keyzer F, Chen F, et al: Diffusion-weighted MR imaging in monitoring the effect of a vascular targeting agent on rhabdomyosarcoma in rats. Radiology 234:756-764, 2005 Thoeny HC, De Keyzer F, Chen F, et al: Diffusion-weighted magnetic resonance imaging allows noninvasive in vivo monitoring of the effects of combretastatin a-4 phosphate after repeated administration. Neoplasia 7:779-787, 2005 Koh DM, Blackledge M, Collins DJ, et al: Reproducibility and changes in the apparent diffusion coefficients of solid tumours treated with combretastatin A4 phosphate and bevacizumab in a two-centre phase I clinical trial. Eur Radiol 19:2728-2738, 2009 Hamstra DA, Rehemtulla A, Ross BD: Diffusion magnetic resonance imaging: A biomarker for treatment response in oncology. J Clin Oncol 25:4104-4109, 2007 Thoeny HC, Ross BD: Predicting and monitoring cancer treatment response with diffusion-weighted MRI. J Magn Reson Imaging 32:216, 2010

139 104. Larocque MP, Syme A, Yahya A, et al: Monitoring T2 and ADC at 9.4 T following fractionated external beam radiation therapy in a mouse model. Phys Med Biol 55:1381-1393, 2010 105. Pickles MD, Gibbs P, Lowry M, et al: Diffusion changes precede size reduction in neoadjuvant treatment of breast cancer. Magn Reson Imaging 24:843-847, 2006 106. Yankeelov TE, Lepage M: A C, et al: integration of quantitative DCEMRI and ADC mapping to monitor treatment response in human breast cancer: Initial results. Magn Reson Imaging 25:1-13, 2007 107. Theilmann RJ, Borders R, Trouard TP, et al: Changes in water mobility measured by diffusion MRI predict response of metastatic breast cancer to chemotherapy. Neoplasia 6:831-837, 2004 108. Kamel IR, Liapi E, Reyes DK, et al: Unresectable hepatocellular carcinoma: Serial early vascular and cellular changes after transarterial chemoembolization as detected with MR imaging. Radiology 250: 466-473, 2009 109. Hayashida Y, Yakushiji T, Awai K, et al: Monitoring therapeutic responses of primary bone tumors by diffusion-weighted image: Initial results. Eur Radiol 16:2637-2643, 2006 110. Uhl M, Saueressig U, van Buiren M, et al: Osteosarcoma: Preliminary results of in vivo assessment of tumor necrosis after chemotherapy with diffusion- and perfusion-weighted magnetic resonance imaging. Invest Radiol 41:618-623, 2006 111. Mardor Y, Pfeffer R, Spiegelmann R, et al: Early detection of response to radiation therapy in patients with brain malignancies using conventional and high b-value diffusion-weighted magnetic resonance imaging. J Clin Oncol 21:1094-1100, 2003 112. Hayashida Y, Yakushiji T, Awai K, et al: Monitoring therapeutic responses of primary bone tumors by diffusion-weighted image: Initial results. Eur Radiol 16:2637-2643, 2006 113. Uhl M, Saueressig U, Koehler G, et al: Evaluation of tumour necrosis during chemotherapy with diffusion-weighted MR imaging: Preliminary results in osteosarcomas. Pediatr Radiol 36:1306-1311, 2006 114. Oka K, Yakushiji T, Sato H, et al: The value of diffusion-weighted imaging for monitoring the chemotherapeutic response of osteosarcoma: A comparison between average apparent diffusion coefficient and minimum apparent diffusion coefficient. Skeletal Radiol 39:141146, 2010 115. Dudeck O, Zeile M, Pink D, et al: Diffusion-weighted magnetic resonance imaging allows monitoring of anticancer treatment effects in patients with soft-tissue sarcomas. J Magn Reson Imaging 27:11091113, 2008 116. Jennings D, Hatton BN, Guo J, et al: GR: early response of prostate carcinoma xenografts to docetaxel chemotherapy monitored with diffusion MRI. Neoplasia 4:255-262, 2002 117. Jordan BF, Runquist M, Raghunand N, et al: Dynamic contrast-enhanced and diffusion MRI show rapid and dramatic changes in tumor microenvironment in response to inhibition of HIF-1alpha using PX478. Neoplasia 7:475-485, 2005 118. Theilmann RJ, Borders R, Trouard TP, et al: Changes in water mobility measured by diffusion MRI predict response of metastatic breast cancer to chemotherapy. Neoplasia 6:831-837, 2004 119. Pickles MD, Gibbs P, Lowry M, et al: Diffusion changes precede size reduction in neoadjuvant treatment of breast cancer. Magn Reson Imaging 24:843-847, 2006 120. Sharma U, Danishad KK, Seenu V, et al: Longitudinal study of the assessment by MRI and diffusion-weighted imaging of tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. NMR Biomed 22:104-113, 2009 121. De Keyzer F, Vandecaveye V, Thoeny H, et al: Dynamic contrastenhanced and diffusion-weighted MRI for early detection of tumoral changes in single-dose and fractionated radiotherapy: Evaluation in a rat rhabdomyosarcoma model. Eur Radiol 19:2663-2671, 2009 122. Lee SC, Poptani H, Pickup S, et al: Early detection of radiation therapy response in non-Hodgkin’s lymphoma xenografts by in vivo 1H magnetic resonance spectroscopy and imaging. NMR Biomed 23:624-632, 2010 123. Hamstra DA, Lee KC, Moffat BA, et al: Diffusion magnetic resonance imaging: An imaging treatment response biomarker to chemoradio-

A.R. Padhani

140

124.

125.

126.

127.

128.

129.

130.

therapy in a mouse model of squamous cell cancer of the head and neck. Transl Oncol 1:187-194, 2008 Eccles CL, Haider EA, Haider MA, et al: Change in diffusion weighted MRI during liver cancer radiotherapy: Preliminary observations. Acta Oncol 48:1034-1043, 2009 Hamstra DA, Chenevert TL, Moffat BA, et al: Evaluation of the functional diffusion map as an early biomarker of time-to-progression and overall survival in high-grade glioma. Proc Natl Acad Sci U S A 102: 16759-16764, 2005 Harry VN, Semple SI, Gilbert FJ, et al: Diffusion-weighted magnetic resonance imaging in the early detection of response to chemoradiation in cervical cancer. Gynecol Oncol 111:213-220, 2008 Dirix P, Vandecaveye V, De Keyzer F, et al: Dose painting in radiotherapy for head and neck squamous cell carcinoma: Value of repeated functional imaging with (18)F-FDG PET 18F-fluoromisonidazole PET, diffusion-weighted MRI, and dynamic contrast-enhanced MRI. J Nucl Med 50:1020-1027, 2009 Jain R, Scarpace LM, Ellika S, et al: Imaging response criteria for recurrent gliomas treated with bevacizumab: Role of diffusion weighted imaging as an imaging biomarker. J Neuro Oncol 96:423431, 2010 Batchelor TT, Sorensen AG, di Tomaso E, et al: AZD2171, a panVEGF receptor tyrosine kinase inhibitor, normalizes tumor vasculature and alleviates edema in glioblastoma patients. Cancer Cell 11:8395, 2007 Schraml C, Schwenzer NF, Martirosian P, et al: Diffusion-weighted

131.

132.

133.

134. 135. 136.

137.

138.

MRI of advanced hepatocellular carcinoma during sorafenib treatment: Initial results. AJR Am J Roentgenol 193:W301-W307, 2009 Kamoun WS, Ley CD, Farrar CT, et al: Edema control by cediranib, a vascular endothelial growth factor receptor-targeted kinase inhibitor, prolongs survival despite persistent brain tumor growth in mice. J Clin Oncol 27:2542-2552, 2009 Batchelor TT, Duda DG, di Tomaso E, et al: Phase II study of cediranib, an oral pan-vascular endothelial growth factor receptor tyrosine kinase inhibitor, in patients with recurrent glioblastoma. J Clin Oncol 28:2817-2823, 2010 Ling CC, Humm J, Larson S, et al: Towards multidimensional radiotherapy (MD-CRT): Biological imaging and biological conformality. Int J Radiat Oncol Biol Phys 47:551-560, 2000 Bentzen SM: Theragnostic imaging for radiation oncology: Dosepainting by numbers. Lancet Oncol 6:112-117, 2005 Padhani AR, Miles KA: Multiparametric imaging of tumor response to therapy. Radiology 256:348-364, 2010 Roach M 3rd, DeSilvio M, Lawton C, et al: Phase III trial comparing whole-pelvic versus prostate-only radiotherapy and neoadjuvant versus adjuvant combined androgen suppression 9413 Radiation Therapy Oncology Group. J Clin Oncol 21:1904-1911, 2003 Taylor A, Rockall AG, Reznek RH, et al: Mapping pelvic lymph nodes: Guidelines for delineation in intensity-modulated radiotherapy. Int J Radiat Oncol Biol Phys 63:1604-1612, 2005 Chang J, Narayana A: Functional MRI for radiotherapy of gliomas. Technol Cancer Res Treat 9:347-358, 2010

Diffusion and Diffusion Tensor Imaging in Brain Cancer Elizabeth R. Gerstner, MD,* and A. Gregory Sorensen, MD† Conventional magnetic resonance imaging provides important information on the structure of tumors, but new imaging tools are emerging that provide more detailed functional information on tumor biology. In particular, diffusion-weighted imaging (DWI) is sensitive to the tumor microenvironment so it may serve as a prognostic biomarker and an indicator of tumor response to chemoradiation. Furthermore, DWI and diffusion tensor imaging may play a role in radiation planning and assessing the long-term effects of radiation. DWI is increasingly being incorporated into clinical practice and clinical trials, but more work needs to be done to standardize acquisition techniques and analysis methods of DWI. Semin Radiat Oncol 21:141-146 © 2011 Elsevier Inc. All rights reserved.

T

he search for noninvasive, imaging biomarkers of cancer is an active area of research. In particular, imaging has moved beyond simply showing gross structural anatomy to revealing information on functional and physiological tissue processes. These new techniques promise to shed light on the complicated interactions of cancer cells, radiation, and chemotherapy agents. The information generated by studies that incorporate new imaging tools will generate a wealth of data that can be used to optimize therapy and hopefully improve patient outcome. One particularly promising imaging biomarker is magnetic resonance diffusion-weighted imaging (DWI). DWI is an indicator of tumor cellularity as well as cellular structural integrity. It has shown promise in prognosis, the early assessment of therapy effectiveness, and as an assessment tool of damage from cancer as well as therapy. This article discusses the metrics of diffusion; their measurement; and their roles in prognosis, treatment planning, intratherapy prediction of efficacy, and posttreatment assessment with a focus primarily on the brain and central nervous system and on human studies that include radiotherapy.

*Department of Neurology, Massachusetts General Hospital Cancer Center, Boston, MA. †AA Martinos Center for Biomedical Imaging and Division of Health Sciences and Technology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School and Massachusetts Institute of Technology, Cambridge, MA. Address reprint requests to Elizabeth R. Gerstner, MD, Department of Neurology, Massachusetts General Hospital Cancer Center, 55 Fruit Street, Yawkey 9E, Boston, MA 02114. E-mail: [email protected]

1053-4296/11/$-see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.semradonc.2010.10.005

How DWI Is Acquired The diffusion of water in tissue is restricted (ie, water diffuses less than if no tissue were present). In some tissues, such as white-matter tracts in the brain, the resulting net water mobility is not the same in each direction and is termed anisotropic; the directionality appears to be imparted by the major myelinated white-matter tracts. This directionality can be quantified using a variant of DWI, diffusion tensor imaging (DTI), in which the diffusion is assumed to be described by a Gaussian-shaped function. This, in turn, allows images of this directionality to be generated and then connections between voxels to be assumed and fiber tracts generated, a process termed tractography (Fig. 1). DTI requires that diffusion be measured in at least 6 predetermined directions (and preferably more) from which the full tensor describing the assumed movement of water in each voxel can be generated. From a clinical point of view, the resulting tensor directionality (ie, how strongly or not the water mobility is measured to be anisotropic) is frequently assumed to be related to the integrity of the underlying white matter. For example, infiltrating tumor cells, peritumoral swelling, or radiationinduced damage of tracts all appear to disrupt the normal anisotropy as measured by DTI. Although there are several parameters that can be calculated with DTI, fractional anisotropy (FA) is typically the most commonly reported. Using DTI and DWI to measure the disruption of normal tissue architecture holds great promise for detecting pathologic changes in the tumor microenvironment at diagnosis and over time in response to therapy although to date changes in FA or apparent diffusion coefficient (ADC) have not been able to clearly demarcate tumor versus surrounding edema. 141

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Figure 1 Tractography in a glioblastoma patient before and 4 months after receiving cediranib showing the reemergence of white-matter tracts with treatment.

Clinical Applications Because it measures the tissue microenvironment, DWI has been proposed to evaluate tumor cellularity (areas in which water movement is restricted by increased cell density have low ADC values) or tumor edema/necrosis (areas of freely moving water have high ADC values). Several studies in human have compared tumor ADC values with histologic sections and confirmed this general association; however, not all studies concur, and proposed threshold ADC values to distinguish different pathologic processes have varied by study.1-5 Still, these early studies support the potential for DWI to serve as an imaging biomarker in brain cancer, and as a result a number of clinical trials are collecting DWI data to determine the degree of utility. Because of the popularity of DWI for other neurologic applications, it is available on most clinical magnetic resonance imaging (MRI) systems, and therefore DWI is easily obtained in patients with brain cancer. Furthermore, DWI has the additional benefit of not requiring contrast injection. Proposed clinical uses for DWI include assessing prognosis, monitoring tumor response to treatment, early prediction of response, assessing tissue damage from tumor or treatment, and distinguishing tumor recurrence from treatment effect. Although the data supporting these various uses appear encouraging, so far there have been substantial limitations to these studies, including small sample sizes, lack of multicenter studies, and variability in data analysis that all necessitate validation in larger studies.

The Role of DWI in Assessing Prognosis Before Radiation Several studies have examined the role of pretreatment tumor ADC values in predicting prognosis. In both cervical and

head and neck cancers, prechemoradiation tumor ADC values were significantly lower in patients who achieved a complete response to treatment compared with a partial response.6,7 This seems a little counterintuitive because low ADC has been suggested to be associated with more aggressive tumors8 but may reflect a sensitivity of these tumors to chemoradiation. In contrast, patients with malignant astrocytoma and a minimum preradiation tumor ADC ⱕ1.0 ⫻ 10⫺3 mm2/s had a 2-year survival of 14% compared with 84% 2-year survival in patients with minimum ADC ⬎1.0 ⫻ 10⫺3 mm2/s9; this study confirmed prior results in malignant astrocytoma showing that a preradiation threshold ADC value of 0.90 ⫻ 10⫺3 mm2/s was able to distinguish between patients with poor prognosis and favorable prognosis with a sensitivity of 79% and specificity of 81%.5 Lower “slow” ADC values (defined at b ⫽ 100-800) were also able to stratify patients into low-risk prostate cancer (stage T1/T2a, Gleason score ⱕ6, or prostate specific antigen [PSA] ⬍10) versus high-risk disease (stage ⱖT2b, Gleason score ⱖ7, or PSA ⬎10).10 These studies suggest that the specific tumor type needs to be considered when using DWI to determine tumor prognosis. Eventually, DWI might be incorporated into risk stratification scores that may influence treatment selection for individual patients; however, data allowing this are not yet available.

The Role of DWI in Treatment Planning As of yet, information provided by diffusion imaging has not significantly influenced routine radiation planning. Nevertheless, hope exists that diffusion imaging may identify areas of tumors not seen on conventional imaging or may help predict tumor growth patterns that could shape radiation

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Table 1 Select Studies Showing a Change in DWI During Treatment Study

Disease

n

al18

Rectal cancer

37

Kim et al19

Rectal cancer

40

Liu et al6

Cervical cancer

17

Harry et al20

Cervical cancer

20

Dirix et al21

Head and Neck cancer

15

Kim et al19

Head and Neck cancer

33

Eccles et al13

Liver cancer*

11

Rhee et al14

Hepato-cellular cancer

20

Tomura et al15

Brain tumors*

20

Sun et

Timing of ADC Measurement Pre-CRT, week 1-2 CRT, post-CRT/ presurgery Pre-CRT, post-CRT/ presurgery Pre-CRT, week 1-2 CRT, 1-2 mo post-CRT Pre-CRT, week 2 CRT, 1 wk post CRT Pre-CRT, week 2 CRT, week 4 CRT, 3 wk post-CRT Pre-CRT, week 1 CRT, 2 wk post-CRT Pre-RT, week 1-2 RT, 4 wk after RT Pre-RT, 1 and 3 mo post-RT Pre-SRS, 2-4 wk post SRS

During or Posttreatment ADC Values ADC ⬎⬎ increased ⬎⬎ in downstaged group mean percent change in ADC ⬎⬎ in downstaged group Mean ADC was significantly higher in patients who achieved CR Mean percent change in ADC greater in patients who achieved CR ADC values at week 2 positively correlated with response Change in ADC values at week 2 correlated with response Lower ADC at week 4 CRT and 3 weeks post CRT associated with subsequent recurrence Significantly higher ADC week 1 of CRT in patients who achieved CR vs PR Mean ADC increase at week 1-2 correlated with sustained response Percent change from baseline in ADC at 1 month predicted tumor response at 3 months Significant difference in ADC values between responders and nonresponders at 2-4 weeks postSRS

CRT, chemoradiation; RT, radiation; SRS, stereotactic radiosurgery. *Included metastatic disease.

fields. The added value of DWI has been shown in prostate cancer where the radiographic sensitivity for detecting pathologically confirmed tumor improved from 26% to 51% with T2-weighted images alone to 47% to 72% when incorporating both techniques.11 This improvement in diagnostic accuracy has also been seen in other prostate cancer studies6 and suggests that using DWI may identify a previously unseen tumor in need of radiation. The incorporation of DWI may also lead to organ sparing in planning radiation fields. In head and neck patients who underwent imaging before neck dissection, Dirix et al12 compared the radiation fields based on conventional imaging, DWI, and pathologic findings. When compared with the radiation plan based on pathologic findings, the radiation field using conventional imaging was significantly larger than the one using DWI. Therefore, DWI may help focus radiation beams to only areas with a tumor and minimize radiation to normal tissue. In addition to identifying a tumor to treat, there is a great deal of interest in using DTI to predict relapse patterns of tumors to extend radiation to areas at high risk of relapse. Based on the knowledge that gliomas infiltrate along whitematter tracks, brain tumors are a specific tumor in which there is an interest in using DTI to guide radiation planning. Unfortunately, however, little data exist yet on how best to incorporate DTI into radiation planning, and prospective studies are needed.

The Role of DWI in Predicting Response During or After Treatment In most studies that measure ADC values during or soon after radiation treatment, there is an increase in ADC consistent with the development of vasogenic edema or necrosis from tumor cell death.6,13-16 Furthermore, studies in a variety of tumor types have shown that changes in ADC can be useful in predicting tumor response during or soon after the completion of radiation treatment (Table 1). In particular, early changes in ADC during treatment have been suggested to be a biomarker of response and may be useful in distinguishing responders from nonresponders.17 Increases in ADC during chemoradiation7,18-20 or radiation alone13,16 have been associated with an improved response in several tumor types, including cervical, rectal, head and neck, brain, and liver cancer. Conversely, a lower ADC during chemoradiation has been associated with a subsequent relapse.21 Even after radiation, a change in ADC may persist as a marker of response as was seen in brain tumor patients in whom significantly higher ADC values 2 to 4 weeks after stereotactic radiosurgery were observed in responders versus nonresponders.15 If DWI can aid in the early identification of patients at high risk for recurrence, it could help guide treatment choices and tumor surveillance.

144 Rectal cancer studies using neoadjuvant chemoradiation followed by surgery have provided some pathologic confirmation for changes in ADC during chemoradiation predicting response. In 37 patients with rectal cancer treated with chemoradiation before surgery, a greater increase in tumor ADC and mean percent change in tumor ADC 1 to 2 weeks into chemoradiation was observed in patients who were downstaged at the time of surgery.18 The mean prostate tumor ADC after chemoradiation but before surgery was also higher in those patients who achieved a pathologic complete response to chemoradiation.19 Less robust results have been reported with muscle-invasive bladder cancer where the sensitivity and specificity of DWI in detecting pathologically confirmed complete response to chemoradiation was 44% and 38%, respectively22; therefore, more work needs to be done to clarify the role of DWI in individual tumor types. In addition to looking at ADC values at particular time points during therapy, several studies have found that the mean percent change in ADC during or immediately after chemoradiation6,20 or radiation alone13 was a predictor of response. In liver cancer, a change in ADC was a better predictor of response than Response Evaluation Criteria In Solid Tumors (RECIST) criteria during radiation.13 A study of 33 patients with newly diagnosed head and neck cancer found that a change in ADC from baseline to week 1 of chemoradiation resulted in an accuracy (area under the receiver operator curve [ROC]) of 0.88, sensitivity of 86%, and specificity of 83% in predicting treatment response, which is an improvement when compared with pretreatment ADC values alone (accuracy ⫽ 0.80, sensitivity ⫽ 65%, and specificity ⫽ 86%).7 Therefore, tracking the change in ADC during therapy may help distinguish who will respond from those who will not. Prospective studies comparing a change in ADC versus ADC values at particular time points will need to be completed to clarify the optimal technique for predicting tumor response. In addition, questions remain on how best to measure tumor ADC. Is median tumor ADC value or minimum tumor ADC value most useful? Clinical studies use different techniques, which makes comparison across studies challenging.

The Role of DTI in Assessing Damage From Cancer or Therapy DTI has been most commonly used in the brain to detect radiation-induced damage because of the high degree of anisotropy white-matter tracts confer. One hypothesis is that damaged fiber tracts will lose anisotropy and be poorly visualized on tractography or FA maps. However, it is important to note that there are regional variations in brain FA that must be considered when interpreting such data. In any case, it currently appears difficult to distinguish patterns of whitematter damage induced by infiltrating glioma cells from patterns of radiation-mediated loss of anisotropy. With these caveats, small studies have shown reversible decreases in DTI parameters in normal-appearing white matter after radiation

E.R. Gerstner and A.G. Sorensen in patients with low-grade gliomas23 and more acute decreases in FA after prophylactic whole-brain radiation for small-cell lung cancer24 or medulloblastoma.25,26 At least in medulloblastoma patients, certain areas of the brain (eg, frontal lobes) seemed more sensitive to the effects of radiation than other areas.27 Because cancer patients are living longer, the cognitive side effects of radiation are increasingly of concern to patients, and DTI has been proposed as a tool to track treatment-induced damage to the brain and aid in drug development or radiation planning to prevent or reverse that damage. A critical step will be to better correlate changes on DTI with neurocognitive deficits.

Detecting Recurrence After Radiation and Distinguishing Recurrence Versus Treatment Effect After treatment with radiation or chemotherapy, DWI may be useful in detecting a recurrent tumor that is not obvious on conventional imaging. In 24 patients with prostate cancer and rising PSA after radiation, combining DWI with dynamic contrast-enhanced MRI was superior to conventional T2weighted MRI in detecting pathologically confirmed recurrent local prostate cancer.28 Recurrent glioblastoma patients treated with cediranib (Recentin; AstraZeneca, London, England) also had an increase in the percentage volume of the tumor comprised by low ADC values without a corresponding increase in contrast-enhancing disease, suggesting a relapse via infiltrative tumor growth.29 Therefore, there may be a role for DWI in characterizing cancer, especially when conventional imaging is ambiguous. Distinguishing a recurrent tumor from radiation effect is a particularly challenging clinical dilemma because the tissue response to treatment as measured by MRI can vary widely from patient to patient. In some situations, there are often changes on MRI (eg, increasing contrast enhancement and mass effect) that suggest active tumor growth. If one postulates that low ADC values represent tumor and higher ADC values represent edema/necrosis, ADC may be able to help distinguish between a tumor (with low ADC) and radiation effect (with high ADC). In brain tumors, increasing ADC with increasing enhancement has been suggested to represent radiation necrosis, whereas decreasing ADC with increasing enhancement may represent a growing tumor.30,31 In head and neck tumors, ADC values were significantly lower in a recurrent tumor compared with a nontumor,32,33 and when confirmed by pathology, correlation had a sensitivity of 94.6% and a specificity of 95.9%.33 For studies attempting to correlate ADC with tumor versus treatment effect, pathologic confirmation of radiographic findings is critical. Unfortunately, however, tumors often have mixed histology with both tumor and necrosis present limiting the utility of median tumor ADC values. Therefore, more sophisticated and spatially precise analysis techniques are needed.34

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Figure 2 Functional diffusion maps (fDM) from patients receiving cediranib: example patients with partial response (PR), stable disease (SD) and progressive disease (PD) in the upper row, and correlation between fDM and treatment effect (Vi ⫽ voxels with increased ADC, Vt ⫽ voxels that show either increase or decrease of ADC) in the lower row.

Approaches to Analyzing DWI Although mean or median tumor ADC might be associated with a particular overall tissue type, such average values do not capture the known heterogeneity in tumors. Therefore, parametric response maps (PRMs) that capture change over time in the entire tumor have been suggested to be a more accurate representation of the true tumor biological response to treatment. PRM coregister serial images and examine the change in the same tumor voxel over time with treatment (Fig. 2). One such technique has been applied to DWI in head and neck cancers as well as gliomas and is also called a functional diffusion map. In head and neck cancer, PRMs may be better at predicting patient response/outcome than calculating the percent change in mean tumor ADC.35 Several studies by researchers at the University of Michigan have shown that the volume of tumor with increased ADC on PRMs at week 3 of radiation was a strong predictor of 1-year survival in glioma patients.17,36,37 Although this technique holds promise as an early predictive marker, coregistration of image voxels over time can be challenging especially because tumors shift in nonlinear ways during and after therapy or if the tumor is small relative to the resolution of the DWI voxel size. Nevertheless, PRMs appear to effectively capture the

heterogeneity of tumors and may provide insights into the variability of underlying tumor biology.

Conclusions MRI has advanced beyond anatomic imaging to now being able to provide information on pathophysiological processes as well. By measuring water diffusion, DWI provides a measure of the tumor microenvironment that can be used to predict prognosis, monitor the response to therapy, and ideally risk stratify patients. Consensus needs to be reached on the optimal analysis techniques for DWI, but there is great promise that this tool will improve patient care and provide important insights into tumor biology.

References 1. Gerstner ER, Frosch MP, Batchelor TT: Diffusion magnetic resonance imaging detects pathologically confirmed, nonenhancing tumor progression in a patient with recurrent glioblastoma receiving bevacizumab. J Clin Oncol 28:e91-e93, 2010 2. Ellingson BM, Malkin MG, Rand SD, et al: Validation of functional diffusion maps (fDMs) as a biomarker for human glioma cellularity. J Magn Reson Imaging 31:538-548, 2010 3. Sadeghi N, D’Haene N, Decaestecker C, et al: Apparent diffusion coefficient and cerebral blood volume in brain gliomas: Relation to tumor

E.R. Gerstner and A.G. Sorensen

146

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

cell density and tumor microvessel density based on stereotactic biopsies. AJNR Am J Neuroradiol 29:476-482, 2008 Seierstad T, Roe K, Olsen DR: Noninvasive monitoring of radiationinduced treatment response using proton magnetic resonance spectroscopy and diffusion-weighted magnetic resonance imaging in a colorectal tumor model. Radiother Oncol 85:187-194, 2007 Higano S, Yun X, Kumabe T, et al: Malignant astrocytic tumors: Clinical importance of apparent diffusion coefficient in prediction of grade and prognosis. Radiology 241:839-846, 2006 Liu Y, Bai R, Sun H, et al: Diffusion-weighted imaging in predicting and monitoring the response of uterine cervical cancer to combined chemoradiation. Clin Radiol 64:1067-1074, 2009 Kim S, Loevner L, Quon H, et al: Diffusion-weighted magnetic resonance imaging for predicting and detecting early response to chemoradiation therapy of squamous cell carcinomas of the head and neck. Clin Cancer Res 15:986-994, 2009 Razek AA, Gaballa G, Denewer A, et al: Invasive ductal carcinoma: Correlation of apparent diffusion coefficient value with pathological prognostic factors. NMR Biomed 23:619-623, 2010 Murakami R, Sugahara T, Nakamura H, et al: Malignant supratentorial astrocytoma treated with postoperative radiation therapy: Prognostic value of pretreatment quantitative diffusion-weighted MR imaging. Radiology 243:493-499, 2007 deSouza NM, Riches SF, Vanas NJ, et al: Diffusion-weighted magnetic resonance imaging: A potential non-invasive marker of tumour aggressiveness in localized prostate cancer. Clin Radiol 63:774-782, 2008 Kajihara H, Hayashida Y, Murakami R, et al: Usefulness of diffusionweighted imaging in the localization of prostate cancer. Int J Radiat Oncol Biol Phys 74:399-403, 2009 Dirix P, Vandecaveye V, De Keyzer F, et al: Diffusion-weighted MRI for nodal staging of head and neck squamous cell carcinoma: Impact on radiotherapy planning. Int J Radiat Oncol Biol Phys 76:761-766, 2010 Eccles CL, Haider EA, Haider MA, et al: Change in diffusion weighted MRI during liver cancer radiotherapy: Preliminary observations. Acta Oncol 48:1034-1043, 2009 Rhee TK, Naik NK, Deng J, et al: Tumor response after yttrium-90 radioembolization for hepatocellular carcinoma: Comparison of diffusion-weighted functional MR imaging with anatomic MR imaging. J Vasc Interv Radiol 19:1180-1186, 2008 Tomura N, Narita K, Izumi J, et al: Diffusion changes in a tumor and peritumoral tissue after stereotactic irradiation for brain tumors: Possible prediction of treatment response. J Comput Assist Tomogr 30:496500, 2006 Mardor Y, Pfeffer R, Spiegelmann R, et al: Early detection of response to radiation therapy in patients with brain malignancies using conventional and high b-value diffusion-weighted magnetic resonance imaging. J Clin Oncol 21:1094-1100, 2003 Hamstra DA, Galbán CJ, Meyer CR, et al: Functional diffusion map as an early imaging biomarker for high-grade glioma: Correlation with conventional radiologic response and overall survival. J Clin Oncol 26:3387-3394, 2008 Sun YS, Zhang XP, Tang L, et al: Locally advanced rectal carcinoma treated with preoperative chemotherapy and radiation therapy: Preliminary analysis of diffusion-weighted MR imaging for early detection of tumor histopathologic downstaging. Radiology 254:170-178, 2010 Kim SH, Lee JM, Hong SH, et al: Locally advanced rectal cancer: Added value of diffusion-weighted MR imaging in the evaluation of tumor response to neoadjuvant chemo- and radiation therapy. Radiology 253: 116-125, 2009 Harry VN, Semple SI, Gilbert FJ, et al: Diffusion-weighted magnetic resonance imaging in the early detection of response to chemoradiation in cervical cancer. Gynecol Oncol 111:213-220, 2008 Dirix P, Vandecaveye V, De Keyzer F, et al: Dose painting in radiotherapy for head and neck squamous cell carcinoma: Value of repeated

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

36.

37.

functional imaging with (18)F-FDG PET, p 18F-fluoromisonidazole PET, diffusion-weighted MRI, and dynamic contrast-enhanced MRI. J Nucl Med 50:1020-1027, 2009 Yoshida S, Koga F, Kawakami S, et al: Initial experience of diffusionweighted magnetic resonance imaging to assess therapeutic response to induction chemoradiotherapy against muscle-invasive bladder cancer. Urology 75:387-391, 2010 Haris M, Kumar S, Raj MK, et al: Serial diffusion tensor imaging to characterize radiation-induced changes in normal-appearing white matter following radiotherapy in patients with adult low-grade gliomas. Radiat Med 26:140-150, 2008 Welzel T, Niethammer A, Mende U, et al: Diffusion tensor imaging screening of radiation-induced changes in the white matter after prophylactic cranial irradiation of patients with small cell lung cancer: First results of a prospective study. AJNR Am J Neuroradiol 29:379-383, 2008 Leung LH, Ooi GC, Kwong DL, et al: White-matter diffusion anisotropy after chemo-irradiation: A statistical parametric mapping study and histogram analysis. Neuroimage 21:261-268, 2004 Khong PL, Kwong DL, Chan GC, et al: Diffusion-tensor imaging for the detection and quantification of treatment-induced white matter injury in children with medulloblastoma: A pilot study. AJNR Am J Neuroradiol 24:734-740, 2003 Qiu D, Kwong DL, Chan GC, et al: Diffusion tensor magnetic resonance imaging finding of discrepant fractional anisotropy between the frontal and parietal lobes after whole-brain irradiation in childhood medulloblastoma survivors: Reflection of regional white matter radiosensitivity? Int J Radiat Oncol Biol Phys 69:846-851, 2007 Kim CK, Park BK, Park W, et al: Prostate MR imaging at 3T using a phased-arrayed coil in predicting locally recurrent prostate cancer after radiation therapy: Preliminary experience. Abdom Imaging 35:246252, 2010 Gerstner ER, Chen PJ, Wen PY, et al: Infiltrative patterns of glioblastoma spread detected via diffusion MRI after treatment with cediranib. Neuro Oncol 12:466-472, 2010 Asao C, Korogi Y, Kitajima M, et al: Diffusion-weighted imaging of radiation-induced brain injury for differentiation from tumor recurrence. AJNR Am J Neuroradiol 26:1455-1460, 2005 Goldman M, Boxerman JL, Rogg JM, et al: Utility of apparent diffusion coefficient in predicting the outcome of gamma knife-treated brain metastases prior to changes in tumor volume: A preliminary study. J Neurosurg 105:175-182, 2006 (suppl) Abdel Razek AA, Kandeel AY, Soliman N, et al: Role of diffusionweighted echo-planar MR imaging in differentiation of residual or recurrent head and neck tumors and posttreatment changes. AJNR Am J Neuroradiol 28:1146-1152, 2007 Vandecaveye V, De Keyzer F, Nuyts S, et al: Detection of head and neck squamous cell carcinoma with diffusion weighted MRI after (chemo)radiotherapy: Correlation between radiologic and histopathologic findings. Int J Radiat Oncol Biol Phys 67:960-971, 2007 Rock JP, Scarpace L, Hearshen D, et al: Associations among magnetic resonance spectroscopy, apparent diffusion coefficients, and imageguided histopathology with special attention to radiation necrosis. Neurosurgery 54:1111-1117, 2004 Galban CJ, Mukherji SK, Chenevert TL, et al: A feasibility study of parametric response map analysis of diffusion-weighted magnetic resonance imaging scans of head and neck cancer patients for providing early detection of therapeutic efficacy. Transl Oncol 2:184-190, 2009 Moffat BA, Chenevert TL, Lawrence TS, et al: Functional diffusion map: A noninvasive MRI biomarker for early stratification of clinical brain tumor response. Proc Natl Acad Sci U S A 102:5524-5529, 2005 Hamstra DA, Chenevert TL, Moffat BA, et al: Evaluation of the functional diffusion map as an early biomarker of time-to-progression and overall survival in high-grade glioma. Proc Natl Acad Sci U S A 102: 16759-16764, 2005

The Promise of Dynamic Contrast-Enhanced Imaging in Radiation Therapy Yue Cao, PhD Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and computed tomography (CT) scanning are emerging as valuable tools to quantitatively map the spatial distribution of vascular parameters, such as perfusion, vascular permeability, blood volume, and mean transit time in tumors and normal organs. DCE MRI/CT have shown prognostic and predictive value for response of certain cancers to chemotherapy and radiation therapy. DCE MRI/CT offer the promise of early assessment of tumor response to radiation therapy, opening a window for adaptively optimizing radiation therapy based upon functional alterations that occur earlier than morphologic changes. DCE MRI/CT has also shown the potential of mapping dose responses in normal organs and tissue for evaluation of individual sensitivity to radiation, providing additional opportunities to minimize risks of radiation injury. The evidence for potentially applying DCE MRI and CT for selection and delineation of radiation boost targets is growing. The clinical use of DCE MRI and CT scanning as a biomarker or even a surrogate endpoint for radiation therapy assessment of tumor and normal organs must consider technical validation issues, including standardization, reproducibility, accuracy and robustness, and clinical validation of the sensitivity and specificity for each specific problem of interest. Although holding great promise, to date, DCE MRI and CT scanning have not been qualified as a surrogate endpoint for radiation therapy assessment or for treatment modification in any prospective phase III clinical trial for any tumor site. Semin Radiat Oncol 21:147-156 © 2011 Elsevier Inc. All rights reserved.

M

olecular, functional, and metabolic imaging methods have been developed and evaluated intensively as potential biomarkers for the assessment of tumor response and outcome in radiation therapy as well as for radiation target selection and delineation. The general investigational hypothesis is that biological imaging has superior sensitivity and specificity to tumor biological processes and, therefore, can be used for better selection of radiation targets or for better indication of tumor early response to treatment as well as normal tissue injury.1,2 These biological indications could provide additional information, either prognostic or predictive, beyond the conventional clinical factors.3-5 A prognostic indicator can be used to stratify patients for different treatment strategies, whereas a predictive indicator can be used

Departments of Radiation Oncology and Radiology, University of Michigan, Ann Arbor, MI. Supported in part by NIH P01 CA59827, NCI RO1 CA132834, RO1 NS064973 and R21 CA126137. Address reprint requests to Yue Cao, PhD, Department of Radiation Oncology and Radiology, University of Michigan, 400 W William Street, Argus Building 1, Ann Arbor, MI 48103. E-mail: [email protected]

1053-4296/11/$-see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.semradonc.2010.11.001

for therapy modification (eg, treatment intensification for nonresponsive tumors). Similarly, functional imaging has been used to assess individual variations in sensitivity to radiation-induced normal tissue toxicity, with the intent of applying these methods to adjust radiation doses to organs at risk. Dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSc) magnetic resonance imaging (MRI) and DCE computed tomography (CT) scanning have been developed and evaluated, to some extent, technically and pathophysiologically for the characterization of vascular properties in the tumor and normal organ tissue.6-14 Vascular properties derived from these imaging studies include blood volume, blood flow, vascular permeability, and mean transit time as well as distribution volume and available interstitial space for the contrast agent. Many malignant tumors manifest neovascularity or angiogenesis, a process of recruiting, synthesizing, and forming new vascular networks, as a possible aspect of tumor growth, proliferation, and metastasis.10-12,15-20 As a result, these newly synthesized vessels manifest high permeability, tortuosity, and density heterogeneity as well as poor functionality. Therefore, vascular 147

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148 characteristics of a tumor could prove a prognostic indicator of its aggressiveness.5,15,16,18 Also, vascular networks provide oxygen and nutrition supply to the tumor and normal tissue. Without adequate blood and oxygen supply to tumor cells, effectiveness of radiation treatment (RT) of the tumor can be compromised. For example, a hypoxic tumor or tumor subvolume responds to radiation therapy poorly compared with a similarly treated cluster of normoxic tumor cells.21-24 Perfusion characteristics of a tumor or tumor subvolume before RT as well as the subsequent alteration during the early course of treatment could provide guidance on where to intensify the treatment.3,5,15,16,18,25,26 Recent developments in prognostic biomarkers (eg, O-6-methylguanine-DNA methyltransferase promoter methylation status for glioblastoma (GBM) treated by concomitant radiochemotherapy27 and human papillomavirus [HPV] for oropharyngeal cancer)28,29 provide a means to differentiate certain classes of patients with favorable versus unfavorable outcome. However, imaging prognostic or predictive biomarkers could address where in the tumor as well as which tumor does not respond to RT. For instance, despite the improvements in treatment regimens for oropharyngeal cancer, there are still up to 20% to 50% failure rates, including metastatic and locoregional failures, especially in patients who are smokers30 and testing negative for HPV infection.29,31 Combining these biological and imaging biomarkers could improve the predictive power for outcome. Furthermore, normal tissue vasculature is prone to radiation damage, which can result in injury of organ function and cause poor quality of life and even mortality in severe cases. Radiation-induced injury to normal tissue has presented as a limiting factor for high-dose radiation therapy for certain body sites (eg, the liver, lung, and brain). Monitoring tumor and normal tissue response to treatment during the early course of radiation therapy could allow us to better estimate a therapy index for individual patients and modify the treatment strategy based on the individual risk and benefit. DCE imaging as a biomarker for the prediction of tumor response and outcome to RT, as evidenced in brain, head and neck, and cervical cancers, is discussed in this review. DCE imaging of normal tissue dose response as well as its role in the prediction of organ toxicity are emerging and are discussed. Vascular features in the tumor or subvolume of the tumor, delineated by quantitative analysis of DCE imaging, has been recognized to have the potential to define radiation boost volume. Limited pathologic and clinical validation, particularly for radiation therapy–specific changes, hinder the advancement of this class of imaging biomarkers for application in radiation therapy. DCE imaging still faces great challenges in technical standardization and validation as well as in pathologic and clinical validation to qualify it as a surrogate endpoint for radiation therapy assessment and to use it for radiation target definition. These issues are discussed.

Prognostic and Predictive Indicators for Tumor Response Assessment Malignant gliomas, particularly GBM, exhibit neovascularity characterized as abnormally rapid growth of vasculature with high density, great vessel leakage, abnormal perfusion, and prolonged mean transit time, which is possibly mediated by angiogenesis and has become the target of antiangiogenic therapy.32,33 Cerebral blood volume (CBV), cerebral blood flow (CBF), and vascular permeability in gliomas mapped by DCE or DSc MRI before radiation have been shown to be prognostic factors for treatment response and outcome.4,5,15,16 The common and consistent finding from these studies is that a malignant glioma with high CBV, high CBF or high permeability is associated with poor response, short time to progression, or worse survival. An early prospective study shows that for patients who have high-grade gliomas with great fractional tumor volumes of high CBV survival is significantly worse than those who have small fractional tumor volumes with high CBV (Fig. 1).16 A retrospective study with a large series of 189 patients with low- or high-grade glioma shows that relative tumor CBV before RT predicts time to progression after therapy better than the pathologic grade.5 For tumors with high relative CBV (⬎1.75) before RT, irrespective of pathologic grades, the median of time to progression was found to be 8.8 months, whereas for tumors with low relative CBV (⬍1.75) before RT, there was a significant difference in time to progression between patients with low- and high-grade gliomas. Another study showed that a large subvolume of the tumor with high vascular permeability in patients with high-grade gliomas before RT was associated with worse survival, whereas the tumor volume defined by fluid attenuation inversion recovery abnormality, post-gadolinium T1 image, or contrast-enhanced rim was not.15 Mapping the glioma CBV and vascular permeability before RT could aid in identifying patients at risk for poor outcome for treatment intensification by either radiation or combining radiation with chemotherapy and/or targeted therapy. Reassessment of treatment strategies during the early course of RT can provide a feedback measure allowing for readjustment of individual therapy. Evaluation of the response rate of glioma CBV and CBF during the midcourse of fractionated RT may be valuable for identifying non-responders from responders. Several studies of malignant gliomas have shown changes in tumor CBV and CBF after 3 weeks of the 6-week course of RT to be predicative for outcome16,34 and to differentiate pseudoprogression from progression.35 In addition to assessing overall tumor response to RT, identifying the subvolume of the tumor resistant to therapy during the course of treatment can have substantial implication in selection and delineation of radiation boost targets. This requires development and validation of quantitative analysis methodologies to differentiate tumor responsive subvolumes from nonresponsive ones and their association with patterns of failure and outcome.

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Figure 1 Survival is significantly worse for the patients who have high-grade gliomas with great fractional tumor volumes with high CBV pre-RT than those with small fractional tumor volumes with high CBV.

These heterogeneous responses are possibly caused by tumor heterogeneity, which can be targeted by local therapy like radiation. The use of CBV and CBF for target definition of malignant gliomas in radiation therapy requires further validation, including pathophysiology and/or tumor pattern failure analyses. Finally, the alteration of the response pattern of CBV and CBF in gliomas treated by concurrent RT and targeted therapy (eg, antiangiogenesis drugs)33 requires studies to reestablish these biomarkers under altered therapeutic conditions. The predictive value of quantitative CBF and CBV imaging for response assessment has also been shown in patients who have brain metastases and are treated with stereotactic radiosurgery (SRS).36,37 Although pre-SRS regional CBV and CBF failed to predict therapy outcome in these studies, perfusion imaging at the 6-week follow-up shows high sensitivity and specificity for treatment outcome prediction. A decrease in tumor CBV 6 weeks after SRS has 91% sensitivity and 71% specificity for the prediction of treatment outcome determined 5 months later, which is superior to the 64% sensitivity and 43% specificity of morphologic changes in tumor volume evaluated at the same time.36 It seems that functional and physiological changes of tumor tissue after irradiation occur earlier than morphologic changes. Although pseudoprogression is less pronounced in brain metastases treated by SRS than GBM treated by chemoradiation therapy,38 changes in regional CBV and CBF during follow-up evaluation as early as 6 weeks may help in distinguishing the extent of response, thus potentially supporting earlier adjuvant treatment decisions. Hypoxia has significant implications for the effectiveness of radiation therapy. Certain tumors have been long recog-

nized to have a high probability to be hypoxic (eg, cervical and head and neck cancers [HNCs]). It is reasonable to assume that low blood flow and low blood volume in cervical tumors or a subvolume of such tumors can indicate a patient who is prone to fail conventional RT. This hypothesis has motivated several studies to investigate the prognostic value of dynamic contrast-enhancement patterns on MRI in cervical cancer patients before radiation therapy.39-42 The subvolume of a cervical tumor with poor contrast enhancement is identified clinically as a predictor for local failure, which is independent of the tumor volume. Interestingly, a histopathological study shows that the well-enhanced subvolumes are predominantly composed of cancer cell fascicles, whereas poorly enhanced areas are composed of fibrous tissue with scattered cancer cells.42 Nevertheless, radiation therapy is more effective for the well-perfused tumor cells than poorly perfused ones.42 A recent study further investigated the temporal changes of dynamic enhanced patterns in cervical cancer during the course of radiation therapy and their association with local control and survival.43 This study confirmed the previous finding that better 5-year local control and survival rates are achieved in the patients who have no or small subvolumes of tumors with poor enhancement than ones that have largely poorly enhanced subvolumes within the tumor. Most interestingly, if tumor poor enhancement improved during the early course of radiation therapy, the patients had tumor local control and survival better than those patients who had poor enhancement persist to the midcourse of treatment. Although outcome was favorable in patients with initially high contrast-enhanced cervical cancers, a longitudinal investigation of microenvironment changes in the tumor (eg, perfu-

150 sion and oxygenation) during the early course of RT could further guide individualized therapy. Similar to cervical cancer, tumor oxygenation in the primary sites and metastatic lymph nodes of HNC has been recognized as a prognostic factor independent of other known clinical variables.21-23 Noninvasive, in vivo mapping of oxygenation and perfusion of head and neck tumors have suggested its added value for prognosis of outcome. Recently, prognostic and predictive functional and metabolic imaging studies that assess tumor hypoxia or tumor perfusion before therapy in HNC have been described.3,26,44-49 Interestingly, studies of the patients with HNC using dynamic 18Ffluoromisonidazole (FMISO) positron emission tomography (PET)49 or DCE MRI and FMISO PET50 have found that hypoxia and perfusion provided complementary information and an inverse correlation in the tumor and the pattern of pre-RT tumor hypoxia or perfusion is correlated with outcomes of RT. It appears that the parametric map derived from DCE imaging is able to detect the poorly perfused subtumor volume below the detectability of FMISO, as indicated in a study.50 In a large series of a perfusion study of 105 patients with HNC treated by RT, low tumor perfusion before RT was associated with high local failure,3 suggesting that poorly perfused HN tumors respond poorly to RT. Furthermore, pretherapy tumor perfusion has been identified as an independent predictor of T-stage classification for local failures. Similar to the studies performed in cervical cancer, alterations in tumor perfusion parameters early after the start of chemo-RT for HNC were investigated for the prediction of outcome. In 1 investigation, improved perfusion after 2 weeks of a 7-week definite chemo-RT treatment course correlated with better local control than persistent poor perfusion in advanced HNC.25 With extensive promising and converging evidence, we still lack a quantitative understanding of the extent of reliability (uncertainty) of these metrics derived from DCE MRI, and such a gap currently hinders our ability to use it for clinical decision making in individualized radiation therapy outside of a trial setting.

DCE Imaging for Assessment of Normal Tissue and Organ Response to Radiation Dose Radiation-induced vascular injury in normal tissue and organs can pose a risk for organ function. Radiation can cause vascular damage, such as vessel dilation, endothelial cell death and apoptosis, microvessel hemorrhage, and eventually vessel occlusion.51-55 Vascular damage can subsequently affect organ function (eg, in the brain, liver, and rectum).56-59 This risk hinders the attempt to increase radiation dose to achieve a better tumor control or even cure the cancer. Early monitoring of vascular response to RT has the potential to predict the outcome of organ function after therapy, thereby selecting the patient who is resistant to radiation for a higher dose, potentially leading to a better chance of tumor local control and better overall therapeutic outcome.

Y. Cao The liver is an organ that is sensitive to radiation. The risk of radiation-induced liver disease (RILD)60 is a limiting factor for the treatment of intrahepatic cancer with high doses. Symptoms generally occur 2 weeks to 2 months after the completion of RT. The clinical outcome ranges from mild, reversible damage to death.61-63 The pathology of RILD is veno-occlusive disease, which is characterized by thrombosis within the central veins of the liver producing “post”-hepatic congestion.58 In the past, efforts to develop NTCP models to estimate the likelihood of developing RILD have been based primarily on the planned radiation dose distribution for the normal liver. The ability to predict RILD is improved by clinical factors.62,64-69 Although these models have permitted the safe delivery of far higher doses of radiation than were previously possible, they also suggest that there is a broad range of individual patient sensitivity that is not reflected by prediction made solely based on the physical dose distribution or general clinical features. Because the basic pathophysiology of RILD is venous occlusion, the early assessment of venous perfusion has the potential to select patients with preclinical signs of perfusion changes before the onset of symptomatic radiation-induced injury. A previous study investigated the portal venous perfusion changes during and after radiation therapy using DCE CT in patients treated with high-dose focal radiation therapy.70,71 It was found that the percentage changes in the regional portal venous perfusion during the course of RT and 1 month after RT were linearly related to the local doses accumulated up to the times of scanning. In addition to the dose dependency, the venous perfusion changes 1 month after RT also correlated with the changes measured after receiving ⬃45 Gy during the course of RT, indicating individual sensitivity to radiation (Fig. 2). This finding motivated an investigation of the individual portal venous perfusion-dose response function (ie, the relationship between the venous perfusion change and the local dose in individual patients).70 Interestingly, the livers of different patients responded differently to the same radiation dose and/or dose distribution, with critical biologically corrected doses (␣/␤ ⫽ 2.5) for undetectable portal venous perfusion ranging from 35.2 to 75.6 Gy with a median of 49.3 Gy. Most importantly, the estimated mean portal venous perfusion in normal liver parenchyma correlated with the overall liver function assessed by the clearance time of indocyanine green, suggesting that spatially resolved portal venous perfusion could be a marker for overall liver function, knowledge of which is required for RT planning and plan adaptation. Neurovasculature is also sensitive to radiation. Neurovascular injury might be a part of the dynamic, interactive process of neurotoxicity and may relate to late cognitive dysfunction. Because of the prolonged survival of some patients with brain tumors after treatment with radiation and the advance in MRI technologies, delayed or late microhemorrhage in the brain has been observed and appears to have an incidence higher than anticipated previously. The effects of dose and dose volume on neurovascular injury, tissue degeneration (white matter demyelination and necrosis), and cognitive dysfunction are not well understood.

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Figure 2 Portal vein perfusion maps (left) before RT and (right) after the tumor received 46.5 Gy color coded and overlaid on the liver axial CT. Note that the substantial reduction in venous perfusion in the region received more than 40 Gy (right). Both images were windowed identically. (Reprinted with permission from Cao et al.71)

Radiation-induced vascular alteration also poses difficulty to differentiate tumor recurrence from radiation necrosis.56,72 Several investigations attempt to shed light on these questions.56,57,73 A longitudinal investigation of neurovascular changes as well as cognitive function changes after conformal, whole-brain, or stereotactic radiation therapy could provide an insight into these questions. A prospective study of DCE MRI for the prediction of radiation-induced neurocognitive dysfunction found that CBVs and blood-brain barrier (BBB) permeability increase significantly in the high-dose regions during RT followed by a decrease after RT.57 Changes in both CBV and BBB permeability correlated with the doses accumulated at the time of scans at weeks 3 and 6 during the course of RT and 1 month after RT. The effect of the dose volume on the vascular volume was also observed. Finally, changes in verbal memory and learning scores 6 months after RT were significantly correlated with changes in CBV of left temporal and frontal lobes and changes in BBB permeability of left frontal lobes during RT, indicating the potential to use early changes in neurovasculature assessed by DCE MRI as a biomarker for late neurocognitive changes. Although other imaging modalities are also able to assess changes in cerebral blood flow and CBV after RT,74,75 DCE and DSc MRI are more suitable for repeated measures as required by a longitudinal study and also allow the assessment of CBV, CBF, and vascular permeability in a single scan that lasts just a few minutes. A further understanding of relationships of delayed microbleeding to late neurocognitive function change, to tissue degeneration, to early vascular changes, and to dose and dose volume could provide guidance on RT planning and improve quality of life.

DCE MRI for Radiation Target Selection and Delineation Because technological dose delivery has changed dramatically, target volume definition based on CT scanning is increasingly becoming an obvious limiting factor in advanced precision treatment. The role of functional imaging for target volume definition has been discussed by several authors.1,76

It has been suggested that a tumor target volume could be defined and segmented as multiple biological target subvolumes, which could be defined based on multiple functional imaging examinations, each of which could be a prognostic factor for radiation response.1 Dose sculpting and painting of multiple biological target subvolumes have been hypothesized.1,2 For such high-precision RT, it is important to understand the sensitivity and specificity of a functional imaging methodology for localization and delineation of a tumor or subvolume of tumor and how the discriminative capacity of these added biomarkers affects treatment planning. However, few studies have been able to address this question. To this regard, there is a scarcity of work that has been done for DCE imaging, with the exception of prostate cancer. Substantial investigations have been performed using DCE MRI for the localization of prostate cancer.77-80 In early studies, dynamic enhancement patterns and/or the parameters derived from DCE MRI were visually inspected and scored by radiologists and then compared with pathologic diagnoses.77,78,81 Overall, sensitivity achieved ranged from 74% to 93% and specificity from 79% to 96%, depending on the experience of reading radiologists, imaging acquisition protocol, and the parameters derived from DCE MRI as well as image processing approaches if used.77,78,82,83 Also, a lower rate of localization of cancer was observed in the transition zone than in the peripheral zone.78 In some studies, DCE MRI for the localization of prostate cancer has been compared with T2-weighted, diffusion, and spectroscopic MRI, and it is often reported that DCE MRI has superior performance81; a combined approach often shows a marginal improvement.84 A recent study85 showed the sensitivity (82%) and specificity (89%) of the vascular parameters derived from DCE MRI for the localization of prostate cancer in the left lobe, right lobe, or bilaterally are similar to that found in choline PET/CT.86 Interestingly, the same study found that magnetic resonance spectroscopy imaging has a lower sensitivity and specificity, with 55% to 68% for sensitivity and 62% to 67% for specificity, depending on spectroscopy pulse sequences and spatial resolution of spectroscopic images. To support delineating prostate gross cancer volume using

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Figure 3 Two large lesions were detected by DCE MRI. The lesion in the dorsal part of the right lobe of the prostate being smaller than 3 mm and containing ⬍30% of cancer cells was not detected by DCE-MRI. (Reprinted with permission from Schmuecking et al.85)

DCE MRI, further pathologic validation has been performed by a few studies.85,87 In 1 study,85 the parameters derived from DCE MRI were not able to detect lesions with a size ⬍3 mm and/or composed of ⬍30% tumor cells (Fig. 3), whereas magnetic resonance spectroscopy failed to detect lesions with a size of ⬍8 mm and/or containing 50% cancer cells. While comparing with the histologic volume in specimens, DCE MRI– defined volumes underestimated the histologic volumes, especially in cases in which the prostate cancer showed a diffuse tumor growth with a low density of prostate cancer cells. These findings may also depend on the imaging protocols used in the study.85 Nevertheless, the delineation of prostate cancer for radiation target remains a great challenge. Another recent study, including a small series of 5 patients, attempted to determine the accuracy of prostate gross tumor volume delineated based on the combination of diffusion and DCE MRI by a radiation oncology expert.87 Of 22 lesions delineated on prostatectomy specimens by a pathologist, 5 dominate cancers with a volume ⬎1 mL, and 4 other smaller ones with a minimum volume of 0.56 mL were detected by the experienced radiation oncologist based on MRI with a spatial resolution of 2.5 ⫻ 2.5 ⫻ 2.5 mm3. The gross tumor volumes of the 5 dominant cancers delineated on MRI covered 44% to 76% of the pathologically determined tumor volumes but had 62% to 174% of the pathologically determined tumor volumes. In addition to errors of image registration, the mismatched characteristics between diffusion

and vascular parameters (in 3 dominant lesions) and negative appearance on MRI (in 1 dominant lesion) contribute to the missed subvolumes of the tumor delineated by the radiation oncology expert. The investigators suggested the use of a 5-mm margin in the gross tumor volume delineated on the MRI to improve the tumor volume coverage. Using this expansion guideline, gross tumor volumes were 2.5 to 3 times as large as the pathologic tumor volumes. Whether this can be accepted or not for radiation target definition is subject to discussion. Finally, the development of multiparameter MRI and automated methods for detection of prostate cancer in the peripheral zone has been attempted.88-90 Although these tools are in the preliminary stage of development, they have the potential to overcome difficulties in comparison and replication of the results for localization and delineation of prostate cancer reported by previous studies.

Issues Related to DCE Imaging in Radiation Therapy Issues related to the use of DCE imaging in radiation oncology perhaps depend on the attempted usage. There are some common issues related to all types of cancer therapy but others uniquely to radiation therapy. These common issues include the standardization of imaging protocols; the PK models; and quantitative metrics derived from the DCE im-

DCE imaging in radiation oncology aging data, quality control/assurance of imaging acquisition, and reproducibility and accuracy of the method as a whole. Currently, there are several national initiatives that aim to address some of these issues. These include the Reference Image Database to Evaluate Response to Therapy project of the National Cancer Institute’s Cancer Imaging Program, Quantitative Image Network of the National Cancer Institute (https://wiki.nci.nih.gov/display/CIP/QIN) and Quantitative Imaging Biomarker Alliance (http://qibawiki.rsna.org). One of the issues that has been focused on from such initiatives is the physical uncertainty resulting from image acquisition.91,92 The physical uncertainty of the quantitative metrics derived from DCE imaging can result from image acquisition as well as computation methods93 and can be random and systemic (biased). The physical uncertainty needs to be characterized to be powered into the clinical trials to assess a given size of biological effects of therapy.91 Currently, image-acquisition protocols and processing strategies are far from being standardized, which hinders cross-comparison of the data from different laboratories or trials as and the ability to conduct multicenter trials. The reproducibility of the DCE quantitative metrics, which can result from both physical uncertainty and biological uncertainty, determines the ability of a metric to measure therapy response in individual patients. The reproducibility of a metric may also be tumor site specific because of biological variation. A few technical aspects of the issues, such as the measurement of arterial input function due to acquisition parameters and its influence on the derived parameters,94-98 sensitivity of the derived parameters upon quantification software of the PK model, image quality and acquisition parameters additional to arterial input function,93 and repeatability of the parameters based on the PK models or non-PK models in the tumor and normal tissue, have been investigated.99-102 These efforts are works in progress, and few repeat image datasets are available to the scientific community for analysis. In addition, the lack of standards for image acquisition, parameter quantification methods, and statistical metrics (or methods)103 makes it difficult to interpret and use the limited published information. Uncertainty of image registration among the series of DCE images acquired over a period and to the treatment planning CT is also an issue. Although it is not unique for DCE imaging in radiation therapy assessment, misalignment between a pair of images acquired at different time points challenges the validity of voxel-level statistical analysis. Although deformable image registration can achieve better geometric accuracy than rigid body methods, the irregularity of the deformable field can propagate into the signal intensity interpolation in the parameter map104 and result in more errors in the signal intensities, in severe cases yielding a salt-and-pepper looking image, which is more problematic for a functional or physiological parameter image than a geometric image. In addition to the technical validation issues, the derived parameter has to have sufficient sensitivity and specificity for the clinical problem addressed (eg, prediction for tumor local control, time to disease survival, or overall survival).

153 There are further challenges for the use of DCE imaging in radiation therapy. RT and planning need to consider where the tumor is spatially, where the extent or margin of the tumor is, where tumor response is, and where the tumor at risk for failure is to delineate radiation targets and define boost volumes. In the consideration of tumor delineation, it is important to understand how sensitive DCE imaging is to the tumor size and tumor cell density and how specific it may be for distinguishing the tumor from other tissues. It is important to perform pathologic validation of DCE imaging for tumor delineation, which has been only pursued in a limited fashion and presents great challenges technically and clinically. Comparing image-derived tumor patterns of failure with the parameter map of DCE imaging could aid in assessing the value of DCE, particularly for organs in which a pathologic sample is hard to obtain (eg, in the brain). In summary, the wealth of information provided by perfusion estimation through DCE imaging is providing new insights into the prognosis and assessment of tumor and normal tissue responses to radiation and combined modality therapies. Such imaging may play a role in treatment modality selection, target definition, and therapy individualization although the evidence supporting these roles is still preliminary. To progress from preliminary studies to broad application, a number of validation steps, both technical as well as clinical/pathologic, are needed. National efforts at technical validation will support the optimal use of DCE as a biomarker, and ideally early-stage clinical trials will expand into broader investigations leading to establishment of this important methodology in routine clinical care.

References 1. Gregoire V, Haustermans K: Functional image-guided intensity modulated radiation therapy: Integration of the tumor microenvironment in treatment planning. Eur J Cancer 45:459-460, 2009 (suppl 1) 2. Ling CC, Humm J, Larson S, et al: Towards multidimensional radiotherapy (MD-CRT): Biological imaging and biological conformality. Int J Radiology and Oncology J Biol Phys 47:551-560, 2000 3. Hermans R, Meijerink M, Van den Bogaert W, et al: Tumor perfusion rate determined noninvasively by dynamic computed tomography predicts outcome in head-and-neck cancer after radiotherapy. Int J Radiat Oncol J Biol Phys 57:1351-1356, 2003 4. Cao Y, Sundgren PC, Tsien CI, et al: Physiological and metabolic magnetic resonance imaging in gliomas. J Clin Oncol 24:1228-1235, 2006 5. Law M, Young RJ, Babb JS, et al: Gliomas: Predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 247:490-498, 2008 6. Rosen BR, Belliveau JW, Aronen HJ, et al: Susceptibility contrast imaging of cerebral blood volume: human experience. Magn Reson Med 22:293-299, 1991 7. Ewing JR, Knight RA, Nagaraja TN, et al: Patlak plots of Gd-DTPA MRI data yield blood– brain transfer constants concordant with those of 14C-sucrose in areas of blood– brain opening. Magn Reson Med 50: 283-292, 2003 8. Roberts HC, Roberts TP, Brasch RC, et al: Quantitative measurement of microvascular permeability in human brain tumors achieved using dynamic contrast-enhanced MR imaging: correlation with histologic grade. AJNR Am J Neuroradiol 21:891-899, 2000 9. Provenzale JM, Wang GR, Brenner T, et al: Comparison of permeability in high-grade and low-grade brain tumors using dynamic suscep-

Y. Cao

154

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20. 21.

22.

23.

24.

25.

26.

27.

28.

29.

tibility contrast MR imaging. AJR Am J Roentgenol 178:711-716, 2002 Law M, Yang S, Babb JS, et al: Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 25:746-755, 2004 Aronen HJ, Gazit IE, Louis DN, et al: Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology 191:41-51, 1994 Sugahara T, Korogi Y, Kochii M, et al: Correlation of MR imagingdetermined cerebral blood volume maps with histologic and angiographic determination of vascularity of gliomas. AJR Am J Roentgenol 171:1479-1486, 1998 Ostergaard L, Sorensen AG, Kwong KK, et al: High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part II: Experimental comparison and preliminary results. Magn Reson Med 36:726-736, 1996 Tofts PS, Brix G, Buckley DE, et al: Estimating kinetic parameters from dynamic contrast-enhanced T(1) -weighted MRI of a diffusible tracer: Standardized quantities and symbols. J Magn Reson Imaging 10:223232, 1999 Cao Y, Nagesh V, Hamstra D, et al: The extent and severity of vascular leakage as evidence of tumor aggressiveness in high-grade gliomas. Cancer Res 66:8912-8917, 2006 Cao Y, Tsien CI, Nagesh V, et al: Clinical investigation survival prediction in high-grade gliomas by MRI perfusion before and during early stage of RT. Int J Radiat Oncol J Biol Phys 64:876-885, 2006 Knopp EA, Cha S, Johnson G, et al: Glial neoplasms: Dynamic contrast-enhanced T2*-weighted MR imaging. Radiology 211:791-798, 1999 Lev MH, Ozsunar Y, Henson JW, et al: Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: Confounding effect of elevated rCBV of oligodendrogliomas [corrected]. AJNR Am J Neuroradiol 25:214-221, 2004 Linderholm B, Grankvist K, Wilking N, et al: Correlation of vascular endothelial growth factor content with recurrences, survival, and first relapse site in primary node-positive breast carcinoma after adjuvant treatment. J Clin Oncol 18:1423-1431, 2000 Schneider BP, Miller KD: Angiogenesis of breast cancer. J Clin Oncol 23:1782-1790, 2005 Brizel DM, Dodge RK, Clough RW, et al: Oxygenation of head and neck cancer: Changes during radiotherapy and impact on treatment outcome. Radiother Oncol 53:113-117, 1999 Stadler P, Becker A, Feldmann HJ, et al: Influence of the hypoxic subvolume on the survival of patients with head and neck cancer. Int J Radiat Oncol J Biol Phys 44:749-754, 1999 Nordsmark M, Overgaard J: A confirmatory prognostic study on oxygenation status and loco-regional control in advanced head and neck squamous cell carcinoma treated by radiation therapy. Radiother Oncol 57:39-43, 2000 Nordsmark M, Overgaard M, Overgaard J: Pretreatment oxygenation predicts radiation response in advanced squamous cell carcinoma of the head and neck. Radiother Oncol 41:31-39, 1996 Cao Y, Popovtzer A, Li D, et al: Early prediction of outcome in advanced head-and-neck cancer based on tumor blood volume alterations during therapy: A prospective study. Int J Radiat Oncol J Biol Phys 72:1287-1290, 2008 Lehtio K, Eskola O, Viljanen T, et al: Imaging perfusion and hypoxia with PET to predict radiotherapy response in head-and-neck cancer. Int J Radiat Oncol J Biol Phys 59:971-982, 2004 Brandes AA, Franceschi E, Tosoni A, et al: MGMT promoter methylation status can predict the incidence and outcome of pseudoprogression after concomitant radiochemotherapy in newly diagnosed glioblastoma patients. J Clin Oncol 26:2192-2197, 2008 Kumar B, Cordell KG, Lee JS, et al: EGFR, p16, HPV Titer, Bcl-xL and p53, sex, and smoking as indicators of response to therapy and survival in oropharyngeal cancer. J Clin Oncol 26:3128-3137, 2008 Worden FP, Kumar B, Lee JS, et al: Chemoselection as a strategy for

30.

31.

32. 33.

34.

35.

36.

37.

38.

39.

40.

41.

42.

43.

44.

45.

46.

47.

48.

organ preservation in advanced oropharynx cancer: Response and survival positively associated with HPV-16 copy number. J Clin Oncol 26:3138-3146, 2008 Hafkamp HC, Manni JJ, Haesevoets A, et al: Marked differences in survival rate between smokers and nonsmokers with HPV-16-associated tonsillar carcinomas. Int Cancer 122:2656-2664, 2008 Fakhry C, Westra WH, Li S, et al: Improved survival of patients with human papillomavirus-positive head and neck squamous cell carcinoma in a prospective clinical trial. J Natl Cancer Inst 100:261-269, 2008 Chi AS, Sorensen AG, Jain RK, et al: Angiogenesis as a therapeutic target in malignant gliomas. Oncologist 14:621-36, 2009 Sorensen AG, Batchelor TT, Zhang WT, et al: A “vascular normalization index” as potential mechanistic biomarker to predict survival after a single dose of cediranib in recurrent glioblastoma patients. Cancer Res 69:5296-5300, 2009 Galban CJ, Chenevert TL, Meyer CR, et al: The parametric response map is an imaging biomarker for early cancer treatment outcome. Nat Med 15:572-576, 2009 Tsien C, Galban CJ, Chenevert TL, et al: Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma. J Clin Oncol 28:2293-2299, 2010 Essig M, Waschkies M, Wenz F, et al: Assessment of brain metastases with dynamic susceptibility-weighted contrast-enhanced MR imaging: initial results. Radiology 228:193-199, 2003 Weber MA, Thilmann C, Lichy MP, et al: Assessment of irradiated brain metastases by means of arterial spin-labeling and dynamic susceptibility-weighted contrast-enhanced perfusion MRI: initial results. Invest Radiol 39:277-287, 2004 Huber PE, Hawighorst H, Fuss M, et al: Transient enlargement of contrast uptake on MRI after linear accelerator (linac) stereotactic radiosurgery for brain metastases. Int J Radiat Oncol J Biol Phys 49: 1339-1349, 2001 Loncaster JA, Carrington BM, Sykes JR, et al: Prediction of radiotherapy outcome using dynamic contrast enhanced MRI of carcinoma of the cervix. Int J Radiat Oncol J Biol Phys 54:759-767, 2002 Mayr NA, Yuh WT, Magnotta VA, et al: Tumor perfusion studies using fast magnetic resonance imaging technique in advanced cervical cancer: A new noninvasive predictive assay. Int J Radiology and Oncology J Biol Phys 36:623-633, 1996 Mayr NA, Yuh WT, Arnholt JC, et al: Pixel analysis of MR perfusion imaging in predicting radiation therapy outcome in cervical cancer. J Magn Reson Imaging 12:1027-1033, 2000 Yamashita Y, Baba T, Baba Y, et al: Dynamic contrast-enhanced MR imaging of uterine cervical cancer: Pharmacokinetic analysis with histopathologic correlation and its importance in predicting the outcome of radiation therapy. Radiology 216:803-809, 2000 Mayr NA, Wang JZ, Zhang D, et al: Longitudinal changes in tumor perfusion pattern during the radiation therapy course and its clinical impact in cervical cancer. Int J Radiat Oncol Biol Physiol 77:502-508, 2010 Hermans R, Lambin P, Van den Bogaert W, et al: Non-invasive tumor perfusion measurement by dynamic CT: Preliminary results. Radiother Oncol 44:159-162, 1997 Hoskin PJ, Saunders MI, Goodchild K, et al: Dynamic contrast enhanced magnetic resonance scanning as a predictor of response to accelerated radiotherapy for advanced head and neck cancer. Br J Radiol 72:1093-1098, 1999 Gandhi D, Chepeha DB, Miller T, et al: Correlation between initial and early follow-up CT perfusion parameters with endoscopic tumor response in patients with advanced squamous cell carcinomas of the oropharynx treated with organ-preservation therapy. AJNR Am J Neuroradiol 27:101-106, 2006 Brun E, Kjellen E, Tennvall J, et al: FDG PET studies during treatment: Prediction of therapy outcome in head and neck squamous cell carcinoma. Head Neck 24:127-135, 2002 Zima A, Carlos R, Gandhi D, et al: Can pretreatment CT perfusion predict response of advanced squamous cell carcinoma of the upper

DCE imaging in radiation oncology

49.

50.

51.

52.

53.

54.

55.

56.

57.

58.

59.

60.

61.

62.

63. 64. 65.

66.

67.

68. 69.

70.

aerodigestive tract treated with induction chemotherapy? AJNR Am J Neuroradiol 28:328-334, 2007 Thorwarth D, Eschmann SM, Scheiderbauer J, et al: Kinetic analysis of dynamic 18F-fluoromisonidazole PET correlates with radiation treatment outcome in head-and-neck cancer. BMC Cancer 5:152, 2005 Jansen JF, Schoder H, Lee, NY, et al: Noninvasive assessment of tumor microenvironment using dynamic contrast-enhanced magnetic resonance imaging and 18F-fluoromisonidazole positron emission tomography imaging in neck nodal metastases. Int J Radiat Oncol J Biol Phys 77:1403-1410, 2010 Pena LA, Fuks Z, Kolesnick RN: Radiation-induced apoptosis of endothelial cells in the murine central nervous system: Protection by fibroblast growth factor and sphingomyelinase deficiency. Cancer Res 60:321-327, 2000 Ljubimova NV, Levitman MK, Plotnikova ED, et al: Endothelial cell population dynamics in rat brain after local irradiation. Br J Radiol 64:934-940, 1991 Santana P, Pena LA, Haimovitz-Friedman A, et al: Acid sphingomyelinase-deficient human lymphoblasts and mice are defective in radiation-induced apoptosis. Cell 86:189-199, 1996 Brown WR, Thore CR, Moody DM, et al: Vascular damage after fractionated whole-brain irradiation in rats. Radiat Res 164:662-668, 2005 Cao Y, Tsien CI, Shen Z, et al: Use of magnetic resonance imaging to assess blood– brain/blood-glioma barrier opening during conformal radiotherapy. J Clin Oncol 23:4127-4136, 2005 Sundgren PC, Cao Y: Brain irradiation: Effects on normal brain parenchyma and radiation injury. Neuroimaging Clin N Am 19:657-668, 2009 Cao Y, Tsien CI, Sundgren P, et al: DCE MRI as a biomarker for prediction of radiation-induced neurocognitive dysfunction. Clin Cancer Res 15:1747-1754, 2009 Lawrence TS, Robertson JM, Anscher MS, et al: Hepatic toxicity resulting from cancer treatment. Int J Radiat Oncol J Biol Phys 31:12371248, 1995 Michalski JM, Gay H, Jackson A, et al: Radiation dose-volume effects in radiation-induced rectal injury. Int J Radiat Oncol Biol Physiol 76:S123-S129, 2010 (3 suppl) Emami B, Lyman J, Brown A, et al: Tolerance of normal tissue to therapeutic irradiation. Int J Radiat Oncol J Biol Phys 21:109-122, 1991 Lawrence TS, Davis MA, Maybaum J, et al: The potential superiority of bromodeoxyuridine to iododeoxyuridine as a radiation sensitizer in the treatment of colorectal cancer. Cancer Res 52:3698-3704, 1992 Russell AH, Clyde C, Wasserman TH, et al: Accelerated hyperfractionated hepatic irradiation in the management of patients with liver metastases: Results of the RTOG dose escalating protocol. Int J Radiat Oncology J Biol Phys 27:117-123, 1993 Schacter L, Crum E, Spitzer T, et al: Fatal radiation hepatitis: A case report and review of the literature. Gynecol Oncol 24:373-380, 1986 Ingold DK, Reed, GB, Kaplan HS, et al: Radiation hepatitis. AJR Am J Roentgenol 93:200-208, 1965 Jackson A, Haken RK, Robertson JM, et al: Analysis of clinical complication data for radiation hepatitis using a parallel architecture model. Int J Radiat Oncol J Biol Phys 31:883-891, 1995 Dawson LA, Normolle D, Balter JM, et al: Analysis of radiation-induced liver disease using the Lyman NTCP model. Int J Radiat Oncol J Biol Phys 53:810-821, 2002 Ten Haken RK, Martel MK, Kessler ML, et al: Use of Veff and isoNTCP in the implementation of dose escalation protocols. Int J Radiat Oncol J Biol Phys 27:689-695, 1993 Poussin-Rosillo H, Nisce LZ, D’Angio GJ: Hepatic radiation tolerance in Hodgkin’s disease patients. Radiology 121:461-464, 1976 Austin-Seymour MM, Chen GT, Castro JR, et al: Dose volume histogram analysis of liver radiation tolerance. Int J Radiat Oncol J Biol Phys 12:31-35, 1986 Cao Y, Pan C, Balter JM, et al: Liver function after irradiation based on computed tomographic portal vein perfusion imaging. Int J Radiat Oncol J Biol Phys 70:154-160, 2008

155 71. Cao Y, Platt JF, Francis IR, et al: The prediction of radiation-induced liver dysfunction using a local dose and regional venous perfusion model. Med Phys 34:604-612, 2007 72. Jain R, Scarpace L, Ellika S, et al: First-pass perfusion computed tomography: initial experience in differentiating recurrent brain tumors from radiation effects and radiation necrosis. Neurosurgery 61:778786, 2007 73. Lee MI, Cha S, Chang SM, et al: Dynamic susceptibility contrast perfusion imaging of radiation effects in normal-appearing brain tissue: Changes in the first-pass and recirculation phases. J Magn Reson Imaging 21:683-693, 2005 74. Taki S, Higashi K, Oguchi M, et al: Changes in regional cerebral blood flow in irradiated regions and normal brain after stereotactic radiosurgery. Ann Nucl Med 16:273-277, 2002 75. Hahn CA, Zhou SM, Raynor R, et al: Dose-dependent effects of radiation therapy on cerebral blood flow, metabolism, and neurocognitive dysfunction. Int J Radiat Oncol J Biol Phys 73:1082-1087, 2009 76. Grosu AL, Nestle U, Weber WA: How to use functional imaging information for radiotherapy planning. Eur J Cancer 45:461-463, 2009 (suppl 1) 77. Jager GJ, Ruijter ET, van de Kaa CA, et al: Dynamic TurboFLASH subtraction technique for contrast-enhanced MR imaging of the prostate: correlation with histopathologic results. Radiology 203:645-652, 1997 78. Ogura K, Maekawa S, Okubo K, et al: Dynamic endorectal magnetic resonance imaging for local staging and detection of neurovascular bundle involvement of prostate cancer: Correlation with histopathologic results. Urology 57:721-726, 2001 79. Noworolski SM, Henry RG, Vigneron DB, et al: Dynamic contrastenhanced MRI in normal and abnormal prostate tissues as defined by biopsy, MRI, and 3D MRSI. Magn Reson Med 53:249-255, 2005 80. Kiessling F, Lichy M, Grobholz R, et al: Simple models improve the discrimination of prostate cancers from the peripheral gland by T1weighted dynamic MRI. Eur Radiol 14:1793-1801, 2004 81. Futterer JJ, Heijmink SW, Scheenen TW, et al: Prostate cancer localization with dynamic contrast-enhanced MR imaging and proton MR spectroscopic imaging. Radiology 241:449-458, 2006 82. Hara N, Okuizumi M, Koike H, et al: DCE-MRI is a useful modality for the precise detection and staging of early prostate cancer. Prostate 62:140-147, 2005 83. Puech P, Potiron E, Lemaitre L, et al: Dynamic contrast-enhancedmagnetic resonance imaging evaluation of intraprostatic prostate cancer: Correlation with radical prostatectomy specimens. Urology 74: 1094-1099, 2009 84. Kozlowski P, Chang SD, Jones EC, et al: Combined diffusionweighted and dynamic contrast-enhanced MRI for prostate cancer diagnosis—Correlation with biopsy and histopathology. J Magn Reson Imaging 24:108-113, 2006 85. Schmuecking M, Boltze C, Geyer H, et al: Dynamic MRI and CAD vs. choline MRS: where is the detection level for a lesion characterization in prostate cancer? Int J Radiat Biol 85:814-824, 2009 86. Reske SN, Blumstein NM, Neumaier B, et al: Imaging prostate cancer with 11C-choline PET/CT. J Nucl Med 47:1249-1254, 2006 87. Groenendaal G, Moman MR, Korporaal JG, et al: Validation of functional imaging with pathology for tumor delineation in the prostate. Radiother Oncol 94:145-150, 2010 88. Vos PC, Hambrock T, Hulsbergen-van de Kaa CA, et al: Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI. Med Physiol 35:888-899, 2008 89. Langer DL, van der Kwast TH, Evans AJ, et al: Prostate cancer detection with multiparametric MRI: Logistic regression analysis of quantitative T2, diffusion-weighted imaging, and dynamic contrast-enhanced MRI. J Magn Reson Imaging 30:327-334, 2009 90. Puech P, Betrouni N, Makni N, et al: Computer-assisted diagnosis of prostate cancer using DCE-MRI data: Design, implementation and preliminary results. Int J Comput Assist Radiol Surg 4:1-10, 2009 91. Clarke LP, Croft BS, Nordstrom R, et al: Quantitative imaging for evaluation of response to cancer therapy. Transl Oncol 2:195-197, 2009 92. Jackson EF, Barboriak DP, Bidaut LM, et al: Magnetic resonance as-

Y. Cao

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93.

94.

95.

96.

97.

98.

sessment of response to therapy: tumor change measurement, truth data and error sources. Transl Oncol 2:211-215, 2009 Cao Y, Li D, Shen Z, et al: Sensitivity of quantitative metrics derived from DCE MRI and a pharmacokinetic model to image quality and acquisition parameters. Acad Radiol 17:468-478, 2010 Henderson E, Rutt BK, Lee TY: Temporal sampling requirements for the tracer kinetics modeling of breast disease. Magn Reson Imaging 16:1057-1073, 1998 Calamante F, Gadian DG, Connelly A: Delay and dispersion effects in dynamic susceptibility contrast MRI: Simulations using singular value decomposition. Magn Reson Med 44:466-473, 2000 Cheng HL: Investigation and optimization of parameter accuracy in dynamic contrast-enhanced MRI. J Magn Reson Imaging 28:736-743, 2008 Peeters F, Annet L, Hermoye L, et al: Inflow correction of hepatic perfusion measurements using T1-weighted, fast gradient-echo, contrast-enhanced MRI. Magn Reson Med 51:710-717, 2004 Singh A, Rathore RK, Haris M, et al: Improved bolus arrival time and arterial input function estimation for tracer kinetic analysis in DCEMRI. J Magn Reson Imaging 29:166-176, 2009

99. Jackson A, Jayson GC, Li KL, et al: Reproducibility of quantitative dynamic contrast-enhanced MRI in newly presenting glioma. Br J Radiol 76:153-162, 2003 100. Galbraith SM, Lodge, MA, Taylor NJ, et al: Reproducibility of dynamic contrast-enhanced MRI in human muscle and tumors: comparison of quantitative and semiquantitative analysis. NMR Biomed 15:132-42, 2002 101. Padhani AR, Hayes C, Landau S, et al: Reproducibility of quantitative dynamic MRI of normal human tissues. NMR Biomed 15:143-53, 2002 102. Yang C, Karczmar GS, Medved M, et al: Reproducibility assessment of a multiple reference tissue method for quantitative dynamic contrast enhanced-MRI analysis. Magn Reson Med 61:851-859, 2009 103. Barnhart HX, Barboriak DP: Applications of the repeatability of quantitative imaging biomarkers: A review of statistical analysis of repeat data sets. Transl Oncol 2:231-235, 2009 104. Yin LS, Tang L, Hamarneh G, et al: Complexity and accuracy of image registration methods in SPECT-guided radiation therapy. Phys Med Biol 55:237-246, 2010

Image-Based Biomarkers in Clinical Practice John E. Bayouth, PhD,* Thomas L. Casavant, PhD,† Michael M. Graham, MD, PhD,‡ Milan Sonka, PhD,§ Manickam Muruganandham, PhD,* and John M. Buatti, MD* The growth of functional and metabolically informative imaging is eclipsing anatomic imaging alone in clinical practice. The recognition that magnetic resonance (MR) and positron emission tomography (PET)-based treatment planning and response assessment are essential components of clinical practice and furthermore offer the potential of quantitative analysis being important. Extracting the greatest benefit from these imaging techniques will require refining the best combinations of multimodality imaging through welldesigned clinical trials that use robust image-analysis tools and require substantial computer based infrastructure. Through these changes and enhancements, image-based biomarkers will enhance clinical decision making and accelerate the progress that is made through clinical trial research. Semin Radiat Oncol 21:157-166 Published by Elsevier Inc.

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epartments of radiation oncology were recently wellrecognized divisions within radiology departments. Because of increasing complexity, substantial separation of residency training requirements, and its unique focus on therapeutic endeavors for cancer, as opposed to diagnostics, the vast majority of radiation oncology divisions are now independent departments. Although this is a positive development in the recognition and development of our field, we paradoxically find ourselves increasingly engaged in the application and evaluation of imaging for staging (and hence decision making), treating, targeting, and determining the response to therapy. The rapid proliferation of functional and metabolically informative imaging agents goes far beyond the common standards of fluoroscopic simulation that comprised the imagebased training of many active radiation oncologists. The adoption of computed tomography (CT) scan– based and megavoltage portal image planning in the 1990s has given way to the common recognition that magnetic resonance (MR)-based planning is an advantage for many sites of disease (the brain for example), and most recently that positron emission tomography (PET)-based imaging and functional MR may have an even greater impact. This, along with the

*Department of Radiation Oncology, University of Iowa, Iowa City, IA. †Biomedical Engineering, University of Iowa, Iowa City, IA. ‡Radiology, University of Iowa, Iowa City, IA. §Electrical Engineering, University of Iowa, Iowa City, IA. Supported in part by NIH by 1U01 CA140206-01. Address reprint requests to John E. Bayouth, PhD, Department of Radiation Oncology, University of Iowa, Hospitals and Clinics, 200 Hawkins Dr, Iowa City, IA 52242. E-mail: [email protected]

1053-4296/11/$-see front matter Published by Elsevier Inc. doi:10.1016/j.semradonc.2010.11.003

growth of both functionally relevant MR techniques and new molecular imaging agents makes imaging an essential component of clinical practices. The potential of imaging to directly impact our practical decision making for our patients has never been greater. This complexity requires enhanced training for the radiation oncologist and invites important paradigm shifting studies that use the potential of image-based therapy and decision making. Although clinical acumen remains vital, the impact of image-based staging, treatment targeting, and response assessment is becoming paramount to our future success.

Prognostics to Assess the Need For/Value of A Given Therapy PET Fluorodeoxyglucose (FDG)-PET imaging is commonly used to show the indication for radiation therapy (RT). Many patients receive a FDG-PET/CT scan before surgery and find evidence of previously unidentified metastatic disease, resulting in their being upstaged and no longer a surgical candidate. Other patients are identified as candidates for RT once PET/CT imaging shows they are not responding to chemotherapy. Scientific literature has many examples of several disease sites in which this is true, including non–small-cell lung cancer, small-cell lung cancer, tumors of the head and neck, esophageal cancer, and pancreatic cancer. This imaging modality has become the preferred method for staging and decision making in several disease sites. Patients who are being planned for RT can also benefit from molecular imaging; this is seen clearly in patients being 157

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158 treated for lymphoma. In a prospective multi-institutional study, Pommier et al1 considered the impact of pre-RT FDGPET in the management of lymphoma patients. Eight institutions collected images from a combination of 16 children and 108 adult patients and found significant modifications of the treatment strategy in nearly 1 of 6 patients, ranging from modifications to the plan (13% of patients) to cancellation of radiotherapy (5% of patients). Those patients cancelling RT did so predominately because of an inadequate response but also from patient refusal after complete response detected by a PET/CT scan. Furthermore, several recent trials make clinical decisions based on response to chemotherapy indexed by PET/CT scan or a single photon emission CT scan, for example, the Children’s Oncology Group protocol for Hodgkin Disease (COG 0532/HD). Initial PET volumes are identified as treatment targets in other patients. RT modifications included changes to the clinical target volume (CTV) by more than 25% (4% patients) or the number of CTVs (5% patients), beam configurations used to create the desired dose distribution (5% patients), and the total dose prescribed by more than 10% (9% patients). Robertson et al2 retrospectively reviewed the role of FDG-PET in 30 pediatric Hodgkin lymphoma patients treated over a nearly 5-year period and concluded that this functional imaging approach influenced the initial staging and treatment volume. Although most of the 546 regions analyzed were in agreement (86%), extranodal sites were found to be problematic. Regions that were discordant were most likely PET positive and CT negative. PET imaging resulted in a change in staging in 15 (50%) patients and modifications to in-field RT volumes in 21 (70%) patients (32 sites added and 15 excluded). Reviews by Belkacémi et al3 and Saleem et al4 succinctly summarize studies in which the role of PET imaging can indicate tumor radiosensitivity. Labeled amino acids (eg, FDOPA and 11C-methionine) can identify cell proliferation and protein synthesis. Labeled nucleosides directly test cell proliferation and DNA synthesis. Several agents now being studied show tissue hypoxia via either selective or reduced uptake in hypoxic cells (eg, F-misonidazole, Cu-ATSM, and HX-4). Hormones and epidermal growth factor receptors show cell proliferation and through either intracellular incor-

poration or cell fixation the presence of specific receptors and/or growth factors (eg, octreotide, prostate specific membrane antigen (PSA), and somatostatin analysis). Other agents directly measure necrosis and apoptosis. Although many remain promising, evaluation of the radiosensitivity of a tissue before radiotherapy exposure requires further research.

MRI Magnetic resonance spectroscopic imaging (MRSI) enables analysis at the metabolic level. Crehange et al5 in a retrospective review showed that MRSI can identify both the tumor volume as well as metabolic activity in prostate cancer. Metabolic imaging data from 72 men who were stratified by risk (low ⫽ 26, medium ⫽ 24, high ⫽ 22) were acquired at 3Tesla (T) using a phased array coil, obviating the need for an endorectal coil that distorts patient anatomy. They found that MRSI-based tumor volumes were significantly different from anatomic volumes (P ⫽ .03) and correlated with increased PSA levels (P ⫽ .012). MRSI is also an effective prognostic tool for evaluating gliomas. In a study of 72 patients with glioma, Li et al6 found that a high choline to creatine ratio, a high choline to N-acetyl aspartate (NAA) ratio, a high lactate and lipid concentration, and a low creatine to NAA ratio were all associated with poor prognosis when evaluated with proton MRSI. In pediatric brain tumors, Warren et al7 found that a low choline to NAA ratio was predictive of a good prognosis (P ⬍ .0067). They also showed 31P MRSI to be a useful prognostic tool for bone sarcoma; 20 patients showed that a high nucleoside triphosphate to inorganic phosphate ratio conferred with a good prognosis (P ⬍ .003). In addition to being used as a prognostic tool, MRSI may be useful in revealing metabolically active tumors that extend beyond the morphologic mass typically identified on a T1- or even T2-weighted image. In a retrospective review of 15 glioma patients, Muruganandham et al8 found significant differences between morphologically defined tumor volumes (T1 and T2) and metabolic tumor volumes identified by MRSI. An example is shown in Figure 1 where the cholineto-NAA ratio of 2.0 is shown as a thermal color wash super-

Figure 1 Comparison of conventional CT and MR-based GTV and CTV with MRSI– derived choline-to-NAA maps. MRSI fused with (A) CT scan, (B) T1-contrast enhanced MRI, and T2-MRI of rim enhancing GBM lesion (pt 3). Contours: purple, Cho: NAA ⫽ 2.0; red, GTV; green, CTV. Note a significant portion of metabolically active tumor falls outside both GTV and CTV.

Image-based biomarkers in clinical RT imposed on the treatment planning CT scan, T1-contrast enhanced MRI, and T2-weighted 2-dimensional turbo spin echocardiographic MRI. The MRSI data were acquired at 3T (Siemens Trio TIM, Erlangen, Germany) using point resolved spectroscopy. This example is representative of all patients within the study in which 2 common characteristics were seen: (1) metabolic tumor volumes were consistently smaller than those defined by morphologic imaging, and (2) metabolically active volumes typically extended beyond both the gross tumor volume and even the clinical target volume. The implications for successful RT are clear. Several investigators have used dynamic contrast-enhanced (DCE) MRI to predict prognosis but have had limited success because of insufficient patient numbers and followup. Nevertheless, a few reports have associated certain quantitative MRI criteria with tumor characteristics having prognostic value. Alonzi and Hoskin9 provide a review article that summarizes the role of functional MRI in several cancer types including central nervous system, prostate, lung, breast, kidney, cervix, rectum, and metastatic disease. In breast cancer, DCE is being used to differentiate between fibrocystic disease, ductal carcinoma in situ, and invasive cancer. DCE MRI was used to differentiate between high- and low-grade lesions in a study of 39 patients10 and to successfully predict the presence of axillary lymph node metastases (P ⬍ .01).11 In cancer of the cervix12 and prostate,13 DCE MRI has been used to identify angiogenesis, which correlates with outcome. In addition to DCE and MRSI, blood-oxygen level– dependent (BOLD) MRI is a functional imaging technique that indirectly measures the oxygenated state of the blood. BOLD MRI has been used in the prostate to identify extracapsular spread.14 BOLD MRI is being evaluated as a tool to assess hypoxia,15 which reduces the sensitivity of tumors to RT. O’Connor et al16 used BOLD to measure induced changes in tissue oxygenation concentration in a series of 10 patients with cervical, hepatocellular, and metastases from ovarian and colorectal carcinoma. While competing with many PET agents to understand hypoxia, BOLD MRI is a nonradiographic means that may provide new momentum into studying the temporal and spatial maps of hypoxia within tumors. Challenges to BOLD MRI include the relative nature of the measurement and its dependence on blood volume, flow, and pH. Diffusion-weighted imaging is a method of measuring the Brownian motion of water molecules, which tends to differ in tumors because of a loss of tissue structure, disrupted cell membranes, and a reduction in interstitial pressure. The flow of water molecules between tissue compartments is quantified by the apparent diffusion coefficient (ADC). This technique has been used to differentiate between malignant and benign tumors in the parotid17 and prostate18 and has been used to distinguish between recurrent gliomas and necrosis.19 Finally, ADC maps were found to be predictive of response in a series of 14 patients with colorectal cancer receiving chemoradiation.20

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Early Assessments to Stop or Alter the Course of a Given Therapy Functional and molecular imaging techniques can be used to evaluate the response of either the tumor or the normal tissues during the course of therapy. This information could be used to adapt treatments as the tumors and normal tissues change during therapy, alter the course of treatment when no response occurs, or potentially reduce toxicity by withholding additional dose once complete response has occurred. As such, many investigators are looking to functional and molecular imaging to provide metrics that can be correlated with outcomes. In a prospective study of 20 cervical cancer patients, Harry et al21 showed diffusion-weighted MRI as an early response indicator. Diffusion-weighted imaging was performed before chemoradiation, repeated after 2 weeks of therapy, and again at the conclusion of therapy. Both the ADC values and their change after 2 weeks of therapy showed a significant correlation with eventual MR response (P ⬍ .05) and clinical response (P ⬍ .03). Retrospectively, Mayr et al22 used dynamic contrast enhancement to show that longitudinal tumor perfusion changes during RT correlate with ultimate treatment outcome. When analyzing DCE MRI in 98 patients, they found that persistently low perfusion (before RT, after 2 weeks of RT, and after 5 weeks at 10 Gy/wk) indicated a high risk of treatment failure, whereas those with initially high perfusion or, more importantly, substantial improvement had favorable outcomes (P ⫽ .003). FDG-PET imaging has also been used to show treatment response in cervical cancer. Schwarz et al23 found complete metabolic response during RT for 6 of 36 patients studied, whereas 26 had a partial metabolic response and 4 had stable or increased tumor metabolic activity. The median time to complete response was 29.5 days for those having a complete response. Menda et al24 reported using fluorothymidine-PET to assess tumor response after the first week of chemoradiotherapy. An image of 1 subject is shown in Figure 2. These images show that an index of proliferation can be used to assess outcome both pretherapy and at early time points. Preliminary data suggest that initial low proliferation may be an indicator of ultimate failure to control disease chemoradiotherapy. Muruganandham et al25 have shown a positive correlation between MRSI changes in tumor metabolic activity at 3 weeks of treatment and disease control in glioma patients. Data from 1 subject are presented in Figure 3. These data suggest that the initial change in a choline-NAA ratio after 3 weeks predicts early failure versus more sustained local control. This combined with previously discussed implications on targeting makes new approaches to therapy based on MRSI intriguing. Ding et al26 used 4DCT to compute pulmonary function changes. An example is shown in Figure 4 in which the subject’s pulmonary function was determined from a 4DCT scan acquired before therapy and again once the subject had received 13 of 37 prescribed fractions (23.4 Gy of 66.6 Gy

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Figure 2 Fluorothymidine (FLT)-PET to assess tumor response after the first week of chemoradiotherapy. Proliferation within the tumor is shown to be reduced at an early time point and may be used to assess outcome and/or modify therapy.

prescribed). The first and second columns show a color-wash display of pulmonary function, whereas the third column shows the difference between the two. The final column shows the planed radiation dose distribution after 13 fractions of therapy. During the third week of RT, changes in pulmonary function were observed. Analysis for correlation to rethink the outcome is pending. Such studies show that even 4DCT may be a means of obtaining important regional lung function information that could also be used for treatment planning (ie, deposit more radiation through less functional areas of lung to avoid long-term sequelae).

Posttreatment Assessment to Gauge Treatment Effectiveness FDG has been proven to be a useful tool in the assessment of RT treatment effectiveness in head and neck cancer. Yao et al27 showed the accuracy and prognostic significance of posttreatment FDG-PET in squamous cell carcinoma after radiotherapy in a retrospective study of 188 head and neck patients. Based on a median follow-up after RT of 32.6 months, they found patients with positive post–RT PET findings had a significantly worse 3-year overall survival (42.5% vs 70.5%, P ⬍ .001) and disease-free survival (57% vs 73.6%, P ⫽ .005). Patients were imaged at 15 weeks after RT (median). They reported the sensitivity, specificity, positive predictive value, and negative predictive value for FDG-PET in the assessment of the treatment response in the neck/ primary site to be 86%/86%, 97%/86%, 71%/32.4%, and 99%/98.7%, respectively. The greatest challenge was interpreting the primary site where 2 false-negative findings were

reported in 151 patients, and 25 false-positive were reported in 37 patients with positive FDG-PET findings. In an earlier retrospective study of 53 head and neck patients spanning 5 years, Yao et al28 showed the ability of FDG-PET to indicate the need for post–RT neck dissection. Of the 42 patients who had no evidence of residual lymphadenopathy and a negative FDG-PET scan 12 weeks after definitive radiation, the negative predictive value of PET was 100%. They concluded in these cases that neck dissection can be safely withheld, even in cases in which residual lymphadenopathy remains. This has substantially altered the general approach to manage the neck after chemotherapy. Kamel et al29 showed that for patients with hepatocellular carcinoma receiving transcatheter arterial chemoembolization, tumors decreased in size and enhancement with increasing ADC values. Marcus et al30 provided an overview of DCE-MRI, DCE-CT, and DCE ultrasonography as techniques for evaluating and monitoring treatment responses. Price et al31 investigated the ability of functional imaging to quantify the effects of RT damage to normal brain tissue.

Infrastructure Needs to Support These Efforts Quality Assurance Few institutions perform quality assurance (QA) on their imaging systems with the intent of using the images for quantitative analysis; rather, the focus is on reading images for diagnosis. In this regard, several protocols have been established that identify QA procedures, test frequencies, and ac-

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Figure 3 Metabolite ratio (Cho/NAA) color maps and corresponding MR spectral array from a representative case.

tion levels. Both NEMA NU 2-200132 and ACR technical Standards for PET/CT33 are outstanding documents for evaluating PET system performance in the context of diagnosis. Two American Association of Physicists in Medicine Task Groups (TGs) are performing work that will also contribute to these efforts. TG-12634 is charged with establishing acceptance testing and routine QA of PET/CT scanners, whereas TG-14535 will establish phantoms and test procedures for assuring consistent and quantitatively accurate metrics from PET/CT scanners. Recently, the International Atomic Energy Agency published a detailed guide of QA for PET and PET/CT (2009) that contains a comprehensive list of practical tests to be performed. Bergman et al36 followed the NEMA protocol to assess image quality variation between 15 scanners using the International Electrotechnical Commission body phantom containing 6 spheres of varying diameter and activity concentrations relative to background. They found considerable differences in image quality, calling into question the clinical interpretation of images and measurement of quantitative indexes, such as the standardized uptake value. Ultimately, the RFP specifications and QA appropriate for PET/CT in RT are dependent on how the system is to be used. A parallel example is provided by Nichols et al37 who described the QA process followed for PET/CT systems used in

cardiology. In RT, Xing38 described QA directed toward PET/CT where in addition to general system performance assessment and QA he considered issues surrounding image acquisition and data transfer, consistency of standard uptake value (SUV), multimodality image fusion, system integration, and respiratory motion management. The frequency and action levels of system testing have been investigated for many systems,39-41 with an emerging philosophic shift being introduced by TG 10042 of the American Association of Physicists in Medicine. The TG-100 approach uses “Failure Modes and Effects Analysis” to better implement QA programs based on institutional procedures. One could easily understand the implications of erroneous conclusions from insufficiently validated and monitored quantitative imaging propagating through the decision process of precision adaptive therapy and, thus, the need for such systems level failure analysis incorporating metabolic and physiological imaging uncertainties is evident.

Education Balter et al43 reported on the findings of an intersociety workshop discussing imaging opportunities in Radiation Oncology. A major theme of this workshop was the need for formal

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Figure 4 The pulmonary function change compared with the planned radiation dose distribution. The dose map, pulmonary function, and pulmonary function change are overlaid on the free-breathing CT scan. The first column is the pulmonary function before RT. The second column is the pulmonary function after RT. The third column is the pulmonary function change from the subtraction of the previous 2 images. The fourth column is the planned radiation dose distribution. In the third column, the red arrows show regions with decreased pulmonary function, and the blue arrows show regions with increased pulmonary function.

training requirements in diagnostic imaging for the radiation oncology trainee. Recognition of this need has existed for several years. In 2005, Coleman et al44 described the conclusions of a working group comprised of representatives from the American College of Radiology, the Society of Nuclear Medicine, and the Society of Computed Body Tomography and MR. They too identified the growing utilization of functional and molecular imaging to necessitate a greater review of image-acquisition protocols, supervision, interpretation, professional qualifications, and patient/staff safety. In 2007, Li and Hendee45 identified the lack of knowledge and/or training by radiation oncology physicists to successfully integrate the emerging use of medical imaging into radiation oncology. They encouraged enhancement of knowledge through self-learning, on-the-job training, interactions with diagnostic radiology and nuclear medicine physicists and physicians, and educational opportunities made available by professional societies and vendors. Shortt et al46 provided the perspective of the International Atomic Energy Agency, who recognized the necessary coupling between medical radiation imaging and radiotherapy, especially in the context of advanced technologies being implemented in current radiation oncology departments.

Requirements for Broad Adoption of Molecular and Functional Imaging in the Community Setting Efforts must be made to establish reimbursement for functional imaging. Ideally, results from the clinical trials would

provide information needed to construct business plans that allow radiation oncology and radiology departments to afford the equipment and imaging time required for molecular and functional imaging. Once departments can be reimbursed, they can hire the professional and technical staff trained with the required skills to perform these studies in routine clinical practice. A literature review reveals thousands of peer-reviewed publications showing the utility of molecular and functional imaging. Almost 10 years ago, this very journal dedicated an entire volume (volume 11, number 1, 2001) to “Functional Imaging and Its Application to Radiation Oncology,” yet 1 decade later we have made little progress in adopting these approaches in the community setting. Why is there a disconnect? Academic institutions must show efficacy in well-defined prospective clinical trials, leading to multi-institutional clinical trials after rigorous and prescriptive QA. One effort to standardize quantitative imaging is being developed by the National Cancer Institute under a contract with multiple institutions known as the Quantitative Imaging Network (QIN). The ability to use functional imaging quantitatively should enable the objective and mathematically robust determination of treatment targets for therapy and for treatment response assessment. Developing tools and methods to address these needs is substantial. Medical imaging analysis has been thwarted by a lack of standardization. Image processing software is primarily written by scanner vendors, third-party developers, and university researchers. Algorithms vary, interfaces vary, and even calculation methodologies vary. Software is often proprietary and prohibitively expensive. This problem makes it difficult to compare response assessment data in a multicenter clinical trial.

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Figure 5 Informatics system architecture as related to the QIN.

Another obstacle is the reliability of input data. As such, the QIN is also addressing the lack of uniformity and consistency in data acquisition. The imaging data to be analyzed is subject to uncertainties introduced during the data-collection process. These variations may manifest from many different sources, including the scanning system, image-acquisition and postprocessing protocols, and/or physiological or biochemical patient changes. Consistent protocols must be established and followed to make results from quantitative imaging studies reproducible. With robust imaging data for analysis, quantitative imageassessment tools must have interoperability across platforms. Specifically, the tools must be (1) developed to operate on standardized platforms (Digital Imaging and Communications in Medicine [DICOM] WG-23 compliant) so that they are accessible in an open environment, (2) validated by evaluation against large databases of patient data in which ground truth is known, (3) shared directly with the scientific community for utilization in the community setting, and (4) open to further development by investigators. Establishing a framework to enable quantitative imaging for tumor response assessment will require coordinating an interdisciplinary team of oncologists, imaging clinicians, imaging and therapy physicists, computer scientists, bioinformatics experts, and corporate scientists. A major initiative of

the QIN is to link together large imaging databases with outcomes databases. Once automated and semiautomated quantitative imaging tools are developed to identify tumors and quantify their changes in subsequent imaging studies, the imaging/outcomes database will serve as the ground truth for evaluating the quantitative tools. This requires a substantial effort in bioinformatics. Figure 5 shows the software architecture being developed within the University of Iowa as a part of the QIN. A key enabling element is the Cancer Biomedical Informatics Grid (caBIG) with its underlying network architecture and platform caGRID, which is the result of 5 years of investment by the NCI in building this community-driven enterprise, and represents the “digital backbone” for caGRID protocols and developer tools. To harmonize imaging protocols, data acquisition, tool development and validation, imaging analysis and QA requires interconnection of the multiple nodes of the QIN itself and establishment of an open-source, open access fabric that enables all parties and stakeholders (including industrial partners) to contribute tools and enhancements. One example of resources becoming available within the QIN includes a dataset of all head and neck cancer patient PET/CT studies acquired at the University of Iowa since 2003 (U of Iowa IRB-01 200,503,706). The dataset has already proven to be a useful resource, leading to multiple retrospec-

J.E. Bayouth et al

164 tive studies that have contributed significantly to the utilization of PET/CT as a highly predictive tool for patients with head and neck cancer and significant nodal disease. Using an SUV ⬎3, we found that a PET/CT 3 months postradiation had a negative predictive value of 97% and a positive predictive value of 32% to 57%. Such studies show the potential of molecular imaging to affect clinical decision making because we now routinely defer surgery in patients with even initially large lymph nodes when their PET/CT is negative using this criteria 3 months after chemoradiotherapy. In contrast, positive scans are frequently false-positives, outlining the need for more rigorous quantitative analysis to improve and then be further tested. This extensively studied dataset of more than 250 patients’ imaging and clinical outcomes is an ideal test bed for tool development and sharing. All new quantitative image-analysis tools and decision support tools developed for the QIN are intended to be shared with the cancer treatment and research communities via an open source approach in addition to the deployment of tools, such as caGRID-enabled applications. Applications developed within this environment will most likely be built on the extensible imaging platform (XIP). The XIP is an opensource framework and platform in which common infrastructure functions used by most applications (such as data management) are separated from analysis/processing chores (which are typically task specific). It is designed for rapidly developing medical imaging applications that are “plug-andplay” across multiple computing environments. XIP was developed out of the caBIG initiative and is the application hosting the initiative of DICOM WG-23. XNAT will serve as a conduit for uploading all QIN-Iowa imaging data into the National Biomedical Imaging Archive as shown in Figure 5. The National Biomedical Imaging Archive will be the primary repository allowing for open sharing of all data resources to the cancer community. Validation of the quantitative imaging tools (for semiautomated tumor identification and quantitative metric calculation) developed by imaging researchers requires merging longitudinal imaging data with annotation and bioinformatics. The algorithm validation toolkit project brings the new annotation and image markup standard into the XIP development environment, allowing statistical assessment tools developed through the algorithm validation toolkit and collaboration with statistical, bioinformatics, and machine learning researchers. Once the standalone modules have been assessed and validated, they can readily be integrated within any DICOM WG-23 compliant host; ideally, applications could be distributed and run using a web browser.

Variance of Measurement Equipment A common source of variation in functional and metabolic imaging is found within the imaging device itself. The performance of a given scanner can be verified by executing consistent QA. Figure 6 shows a slice-by-slice evaluation of the mean SUV within a cylindrical liquid phantom filled with known concentrations of FDG. Data were acquired monthly; each data point represents the slice-specific SUV averaged

Figure 6 Mean SUV within a cylindrical liquid phantom filled with known concentrations of FDG. Data were acquired monthly; each data point represents the slice-specific SUV averaged over 14 months of data collection.

over 14 months. Although all slices should identify an SUV of unity, substantial daily variation occurs. Furthermore, variations can be seen when comparing data between slices. Finally, a systematic shift of 2.7% is clearly present, which is likely caused by inaccurate knowledge of the true activity within the phantom used to for daily cross-calibration of counts into bequerels. Additional sources of uncertainty are the dose calibrator (⫾0.5%-1%), the concentration of the injected solutions within the dose calibrator (⫾1%), and the variation by slice (⫾2%). When summed in quadrature, this produces nearly 4% uncertainty, simply because of the imaging equipment. Temporal variance of a functional metric within a given human subject is also a substantial issue for evaluating the response to therapy. Biological variability within a given subject adds further to the overall uncertainty of the measurement, having a further negative influence on the power of a trial to identify changes caused by therapy. The sensitivity of the response assessment approach to physical/biological variation can be determined from experiments in which imaging is repeated in a very short interval under the assumption that no change has occurred. These “coffee break” studies allow investigators to quantify combined sources of uncertainty.

Conclusions In conclusion, image-based biomarkers offer critical and objective decision-making tools that will change our practice. Refining the best combination of multimodality imaging remains a significant challenge. Multi-institutional efforts are underway to design tools that operate across many platforms, provide an infrastructure for image analysis and tool validation against large clinical databases with longitudinal studies and complete annotation, and establish prospective clinical trails. In combination, the broad adoption of advanced imaging into clinical practice will further require enhancing the imaging skills typically accessed by the radiation oncology community. The promise of mathematically defined image-

Image-based biomarkers in clinical RT based targets from well-qualified and harmonized imaging protocols is profound in that it finally enables true consistency in target definition and response assessment. The impact on our ability to perform high-quality clinical trials will be dramatic.

References 1. Pommier P, Dussart S, Girinsky T, et al: Impact of 18F-fluoro-2-deoxyglucose positron emission tomography on treatment strategy and radiotherapy planning for stage I–II Hodgkin disease: A prospective multicenter study. Int J Radiat Oncol Biol Phys 2010[in press] 2. Robertson VL, Anderson CS, Keller FG, et al: Role of FDG-PET in the definition of involved-field radiation therapy and management for pediatric Hodgkin’s lymphoma. Int J Radiat Oncol Biol Phys 2010[in press] 3. Belkacémi Y, Tsoutsou P, Magné N, et al: Metabolic functional imaging for tumor radiosensitivity monitoring. Crit Rev Oncol Hematol 62:227239, 2007 4. Saleem A, Charnley N, Price P: Clinical molecular imaging with positron emission tomography. Eur J Cancer 42:1720-1727, 2006 5. Crehange G, Parfait S, Liegard M, et al: Tumor volume and metabolism of prostate cancer determined by proton magnetic resonance spectroscopic imaging at 3T without endorectal coil reveal potential clinical implications in the context of radiation oncology. Int J Radiat Oncol Biol Phys 2010[in press] 6. Li X, Jin H, Lu Y, et al: Identification of MRI and 1H MRSI parameters that may predict survival for patients with malignant gliomas. NMR Biomed 17:10-20, 2004 7. Warren KE, Frank JA, Black JL, et al: Proton magnetic resonance spectroscopic imaging in children with recurrent primary brain tumors. J Clin Oncol 18:1020-1026, 2000 8. Muruganandham M, Bayouth JE, Smith MC, et al: Impact of 3-Tesla MR spectroscopic imaging in the delineation of high grade glioma target volumes for radiation therapy planning. Int J Radiat Oncol Biol Phys 72:S210, 2008 9. Alonzi R, Hoskin P: Functional imaging in clinical oncology: Magnetic resonance imaging- and computerised tomography-based techniques. Clin Oncol 18:555-570, 2006 10. Oshida K, Nagashima T, Ueda T, et al: Pharmacokinetic analysis of ductal carcinoma in situ of the breast using dynamic MR mammography. Eur Radiol 15:1353-1360, 2005 11. Tuncbilek N, Karakas HM, Okten OO: Dynamic magnetic resonance imaging in determining histopathological prognostic factors of invasive breast cancers. Eur J Radiol 53:199-205, 2005 12. Hawighorst H, Weikel W, Knapstein PG, et al: Angiogenic activity of cervical carcinoma: Assessment by functional magnetic resonance imaging-based parameters and a histomorphological approach in correlation with disease outcome. Clin Cancer Res 4:2305-2312, 1998 13. Schlemmer HP, Merkle J, Grobholz R, et al: Can pre-operative contrastenhanced dynamic MR imaging for prostate cancer predict microvessel density in prostatectomy specimens? Eur Radiol 14:309-317, 2004 14. Diergarten T, Martirosian P, Kottke R, et al: Functional characterization of prostate cancer by integrated magnetic resonance imaging and oxygenation changes during carbogen breathing. Invest Radiol 40:102109, 2005 15. Taylor NJ, Baddeley H, Goodchild KA, et al: Of human tumor oxygenation during carbogen breathing. J Magn Reson Imaging 14:156-163, 2001 16. O’Connor JP, Naish JH, Parker GJ, et al: Preliminary study of oxygenenhanced longitudinal relaxation in MRI: A potential novel biomarker of oxygenation changes in solid tumors. Int J Radiat Oncol Biol Phys 75:1209-1215, 2009 17. Habermann CR, Gossrau P, Graessner J, et al: Diffusion-weighted echoplanar MRI: A valuable tool for differentiating primary parotid gland tumors? Rofo 177:940-945, 2005 18. Sato C, Naganawa S, Nakamura T, et al: Differentiation of noncancerous tissue and cancer lesions by apparent diffusion coefficient values in

165

19.

20. 21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32. 33. 34. 35. 36.

37. 38.

39. 40. 41. 42.

transition and peripheral zones of the prostate. J Magn Reson Imaging 21:258-262, 2005 Hein PA, Eskey CJ, Dunn JF, et al: Diffusion-weighted imaging in the follow-up of treated high-grade gliomas: Tumor recurrence versus radiation injury. AJNR Am J Neuroradiol 25:201-209, 2004 Dzik-Jurasz A, Domenig C, George M, et al: Diffusion MRI for prediction of response of rectal cancer to chemoradiation. Lancet 360:307-308, 2002 Harry VN, Semple SI, Gilbert FJ, et al: Diffusion-weighted magnetic resonance imaging in the early detection of response to chemoradiation in cervical cancer. Gynecol Oncol 111:213-220, 2008 Mayr NA, Wang JZ, Zhang D, et al: Longitudinal changes in tumor perfusion pattern during the radiation therapy course and its clinical impact in cervical cancer. Int J Radiat Oncol Biol Phys 77:502-508, 2010 Schwarz JK, Lin LL, Siegel BA, et al: 18-F-fluorodeoxyglucose-positron emission tomography evaluation of early metabolic response during radiation therapy for cervical cancer. Int J Radiat Oncol Biol Phys 72: 1502-1507, 2008 Menda Y, Ponto LL, Dornfeld KJ, et al: Investigation of the pharmacokinetics of 3=-deoxy-3=-[18F]fluorothymidine uptake in the bone marrow before and early after initiation of chemoradiation therapy in head and neck cancer. Nucl Med Biol 37:433-438, 2010 Muruganandham M, Bayouth JE, Anderson CM: 3D-MR spectroscopic imaging assessment of metabolic status of malignant gliomas during external beam radiation therapy—Preliminary results. Int J Radiat Oncol Biol Phys 75:S228-S2S9, 2009 Ding K, Bayouth JE, Buatti JM, et al: 4DCT-based measurement of changes in pulmonary function following a course of radiation therapy. Med Phys 37:1261-1272, 2010 Yao M, Smith RB, Hoffman HT, et al: Clinical significance of postradiotherapy [18F]-fluorodeoxyglucose positron emission tomography imaging in management of head-and-neck cancer: A long-term outcome report. Int J Radiat Oncol Biol Phys 74:9-14, 2009 Yao M, Smith RB, Graham MM, et al: The role of FDG PET in management of neck metastasis from head-and-neck cancer after definitive radiation treatment. Int J Radiat Oncol Biol Phys 63:991-999, 2005 Kamel IR, Bluemke DA, Eng J, et al: The role of functional MR imaging in the assessment of tumor response after chemoembolization in patients with hepatocellular carcinoma. J Vasc Interv Radiol 17:505-512, 2006 Marcus CD, Ladam-Marcus V, Cucu C, et al: Imaging techniques to evaluate the response to treatment in oncology: Current standards and perspectives. Crit Rev Oncol/Hematol 72:217-238, 2009 Price SJ, Jena R, Green HA, et al: Early radiotherapy dose response and lack of hypersensitivity effect in normal brain tissue: A sequential dynamic susceptibility imaging study of cerebral perfusion. Clin Oncol 19:577-587, 2007 NEMA: National Electrical Manufacturers Association NU 2-2001. 2001 Geise AC: Technical Standards for PET/CT 2008 Mawlawi OR: TG126 PET/CT Acceptance Testing and Quality Assurance Kinahan PE: TG145 quantitative imaging initiative: Quantitative PET/CT imaging Bergmann H, Dobrozemsky G, Minear G, et al: An inter-laboratory comparison study of image quality of PET scanners using the NEMA NU 2-2001 procedure for assessment of image quality. Phys Med Biol 50:2193, 2005 Nichols KJ, Bacharach SL, Bergmann SR, et al: Instrumentation quality assurance and performance. J Nucl Cardiol 13:e25-e41, 2006 Xing L: Quality Assurance of positron emission tomography/Computed Tomography for radiation therapy. Int J Radiat Oncol Biol Phys 71:S38S42, 2008 Schultheiss TE, Boyer AL, Horton JL, et al: Calibration frequency as determined by analysis of machine stability. Med Phys 16:84-87, 1989 Rozenfeld M, Jette D: Quality assurance of radiation dosage: Usefulness of redundancy. Radiology 150:241-244, 1984 Pawlicki T, Whitaker M, Boyer AL: Statistical process control for radiotherapy quality assurance. Med Phys 32:2777-2786, 2005 Method for Evaluating QA Needs in Radiation Therapy: Task Group

166 Report 100 American Association of Physicists in Medicine. New York, NY, American Institute of Physics (in press) 43. Balter JM, Haffty BG, Dunnick NR, et al: Imaging opportunities in radiation oncology. Int J Radiat Oncol Biol Phys 2010 Aug 26 [Epub ahead of print] 44. Coleman RE, Delbeke D, Guiberteau MJ, et al: Concurrent PET/CT with an integrated imaging system: Intersociety dialogue from the Joint Working Group of the American College of Radiology, the Society of

J.E. Bayouth et al Nuclear Medicine, and the Society of Computed Body Tomography and Magnetic Resonance. J Am Coll Radiol 2:568-584, 2005 45. Li XA, Hendee WR: Radiation oncology physicists will Need to better understand medical Imaging. J Am Coll Radiol 4:40-44, 2007 46. Shortt K, Davidsson L, Hendry J, et al: International perspectives on quality assurance and new Techniques in Radiation Medicine: Outcomes of an IAEA Conference. Int J Radiat Oncol Biol Phys 71:S80-S84, 2008