Published on

El QUANTEC nos ayuda a los oncólogos radioterápicos a la hora de aprobar un tratamiento con sus tablas con "constraints" de los órganos de riesgo (los límites de dosis que pueden recibir los órganos sanos situados entorno al tumor que queremos tratar).
PD: Las tablas se encuentran en las páginas 15-17

Published in: Health & Medicine, Technology
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide


  1. 1. INTRODUCTORY PAPER GUEST EDITOR’S INTRODUCTION TO QUANTEC: A USERS GUIDE LAWRENCE B. MARKS, M.D.,* RANDALL K. TEN HAKEN, PH.D.,y GUEST EDITORS, AND MARY K. MARTEL, PH.D.,z ASSOCIATE GUEST EDITOR *University of North Carolina, Chapel Hill, North Carolina; y University of Michigan, Ann Arbor, Michigan; and z M. D. Anderson Cancer Center, Houston, Texas We are pleased to present this special issue of the International Journal of Radiation Oncology$Biology$Physics, dedicated to the Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC). This work is the result of the diligent ef- forts of numerous investigators, authors, reviewers, and support personnel. We are particularly indebted to the comembers of the QUANTEC Steering Committee1 , for their dedication. This is an exciting time in the field of radiation oncology. Sophisticated treatment-planning tools and delivery systems remarkably increase our ability to steer the dose where we want it. An increased knowledge of how dose distributions af- fect normal tissue outcomes is critically needed to know how best to exploit these new planning/delivery tools. In 1991, Emami et al. (1) published a comprehensive review of the available dose/volume/outcome data, along with expert opin- ion where data were lacking. Since the publication of the clas- sic paper by Emami et al. (1), there have been numerous additional studies providing dose/volume/outcome data. The QUANTEC reviews provide focused summaries of the dose/volume/outcome information for many organs. The re- views will be excellent resources to assist physicians and treatment planners in determining acceptable dose/volume constraints. In addition, the QUANTEC papers point out the shortcomings of current predictive models and suggest areas for future research. Despite the limitations of the data, the new information presented should be of substantial use in the treat- ment planning process. We are particularly pleased with the many summary tables and figures that, we hope, will adorn the walls of treatment planning areas. This special issue is organized into three sections. There are two introductory papers: the first paper is an overview/history with some scientific issues related to the QUANTEC effort, and the second paper contains suggestions on how to ratio- nally incorporate the QUANTEC metrics/models into clinical practice. The latter paper includes a large summary table of dose/volume/outcome data. The bulk of this issue is 16 organ- specific clinical papers. To assist the reader, each article is organized in a consistent format that includes 10 sections (Fig. 1). The organs discussed were selected because the authors believed that there were meaningful data to review, and a clinical need to better summarize the available dose/vol- ume data for these organs. We conclude with a series of vision papers outlining interesting issues that merit further study. Dr. Philip Rubin, at the University of Rochester, the found- ing Editor of this journal, was an early leader in the field of radiation-induced normal tissue injury. He conducted many of the classic studies of normal tissue response and provided some of the earliest summaries of normal tissue dose/volume/ outcome estimates. It is particularly fitting that an entire issue of the International Journal of Radiation Oncology$ Biology$Physics be devoted to a topic so very dear to our founding editor. QUANTEC represents an evolution from the early sum- mary tables presented by Dr. Rubin, to the more recent re- views by investigators such as Emami et al. (1). All those involved in the QUANTEC effort recognize that much work remains to be done. For example, most of the available data relate to conventionally fractionated conformal irradia- tion, i.e., not hypofractionated or intensity-modulated ap- proaches. We anticipate regular updates of the information and believe these will help our field continue to provide qual- ity care to our patients. We hope to be able to provide updated Reprint requests to: Dr. Lawrence B. Marks, M.D., University of North Carolina, Department of Radiation Oncology, CB 7512, Chapel Hill, NC 27514. Tel: (919) 966-0400; Fax: (919) 966- 7681; E-mail: marks@med.unc.edu The QUANTEC effort was made possible, in part, by generous financial support from the American Society for Radiation Oncol- ogy (ASTRO) and the American Association of Physicists in Medicine (AAPM). This special supplement to the Red Journal was supported by ASTRO. Acknowledgments—We thank the leaders of ASTRO’s Research Council and Health Services Research Committee (Drs. David Morris and Carol Hahn) and the AAPM Science Council. Special thanks to Beth Notzon and Deborah Williams at the International Journal of Radiation Oncology$Biology$Physics and Jessica Hubbs at University of North Carolina for oversight and patience with the review/editing process. Members of the 1 QUANTEC steering com- mittee: Drs. Søren M. Bentzen, Louis S. Constine, Joseph O. Deasy, Avi Eisbruch, Andrew Jackson, Lawrence B. Marks, Randy Ten Haken, and Ellen D. Yorke. Received Aug 27, 2009. Accepted for publication Aug 28, 2009. S1 Int. J. Radiation Oncology Biol. Phys., Vol. 76, No. 3, Supplement, pp. S1–S2, 2010 Copyright Ó 2010 Elsevier Inc. Printed in the USA. All rights reserved 0360-3016/10/$–see front matter doi:10.1016/j.ijrobp.2009.08.075
  2. 2. QUANTEC reviews on an ASTRO-sponsored web site, as well as perhaps on a bulletin board or blog where readers can provide comments/data for consideration for future re- views. Attempts to limit normal tissue risks should be taken in the context of the competing need to deliver a ‘‘therapeutic dose distribution.’’ Target coverage may trump normal tissue spar- ing: recurrent tumor can be morbid/lethal, and the normal tis- sue risks considered in the QUANTEC reviews are often not life threatening. Furthermore, QUANTEC’s focus on three-di- mensional dose/volume parameters reinforces the reliance on dose-volume histogram-based optimization systems to mini- mize normal tissue risk. It is important to remember that rela- tively simple measures (e.g., careful attention to patient positioning) can reduce normal tissue exposure and comple- ment our newer planning/delivery/optimization tools. It is humbling to have helped lead this QUANTEC effort, and it was a privilege to work with so many talented and ded- icated people. The information presented here was inspired by our mentors and teachers and relies almost entirely on the published work of others. We hope that current and future generations of investigators—physicians, physicists, biolo- gists, imagers, and others—will continue this area of study. Exploiting the rapidly evolving advances outlined in the vision papers (e.g., imaging, dose monitoring, genetics, and other biologic factors) will facilitate the development of bet- ter tools to understand and reduce the risks of radiation- induced normal tissue injury. REFERENCE 1. Emami B, Lyman J, Brown A, et al. Tolerance of normal tissue to therapeuticradiation.IntJRadiatOncolBiolPhys1991;21:109–122. Fig. 1. Outline of the issue: the first section consists of Introductory Papers; the second section consists of Organ-Specific Papers, each containing 10 topic sections; and the third section consists of Vision Papers. S2 I. J. Radiation Oncology d Biology d Physics Volume 76, Number 3, Supplement, 2010
  3. 3. INTRODUCTORY PAPER QUANTITATIVE ANALYSES OF NORMAL TISSUE EFFECTS IN THE CLINIC (QUANTEC): AN INTRODUCTION TO THE SCIENTIFIC ISSUES SØREN M. BENTZEN, PH.D., D.SC.,* LOUIS S. CONSTINE, M.D.,y JOSEPH O. DEASY, PH.D.,z AVI EISBRUCH, M.D.,x ANDREW JACKSON, PH.D.,k LAWRENCE B. MARKS, M.D.,{ RANDALL K. TEN HAKEN, PH.D.,x AND ELLEN D. YORKE, PH.D.k From the *Departments of Human Oncology, Medical Physics, Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI; y Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY; z Department of Radiation Oncology, Washington University, St. Louis, MO; x Department of Radiation Oncology, University of Michigan, Ann Arbor, MI; k Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY; { Department of Radiation Oncology, University of North Carolina at Chapel Hill, NC Advances in dose–volume/outcome (or normal tissue complication probability, NTCP) modeling since the seminal Emami paper from 1991 are reviewed. There has been some progress with an increasing number of studies on large patient samples with three-dimensional dosimetry. Nevertheless, NTCP models are not ideal. Issues related to the grading of side effects, selection of appropriate statistical methods, testing of internal and external model validity, and quantification of predictive power and statistical uncertainty, all limit the usefulness of much of the published literature. Synthesis (meta-analysis) of data from multiple studies is often impossible because of suboptimal pri- mary analysis, insufficient reporting and variations in the models and predictors analyzed. Clinical limitations to the current knowledge base include the need for more data on the effect of patient-related cofactors, interactions between dose distribution and cytotoxic or molecular targeted agents, and the effect of dose fractions and overall treatment time in relation to nonuniform dose distributions. Research priorities for the next 5–10 years are proposed. Ó 2010 Elsevier Inc. QUANTEC, Normal tissue complications, Overview, Modeling. WHY QUANTEC? Modern radiation therapy (RT) techniques generally yield nonuniform dose distributions in nontarget tissues. The intro- duction of external beam megavoltage RT in the 1950s shifted the most important side effects from the skin and sub- cutaneous tissues to the deeper seated tissues. The ensuing wide adoption of parallel opposing field techniques led to im- provements in target dose homogeneity, but typically led to whole or partial organ irradiation of the neighboring non-tar- get tissues: a fractional volume of an organ at risk would es- sentially receive the prescribed target dose. Because of the limited capabilities to image the tumor extent, most RT fields included liberal margins. Computed tomography–based diagnosis and RT planning in the 1980s and 1990s revolutionized target volume visual- ization and facilitated multiple-field and three-dimensional (3D) conformal RT. Conceptual and technological advances have led to new RT technologies (e.g., intensity-modulated radiation therapy, rotational or helical delivery, robotic delivery, and proton therapy). These technologies typically deliver near-uniform doses to the target volume. However, the dose distribution in the surrounding normal tissues is more variable. Therefore, these new technologies provide the treatment planner with increased flexibility in determining which re- gions of normal tissue are to be incidentally irradiated. The treatment planner needs information to predict the risk of a normal tissue injury for competing 3D dose distributions, such that the therapeutic ratio can be optimized. One of the goals of QUANTEC is to summarize the available 3D dose–volume/outcome data. At the same time, increasing use of combined modality therapy has often increased the burden of early and late tox- icities (1). Understanding the tradeoff between an expected decrease in toxicity resulting from an improved dose distribu- tion, and the possible increase in toxicity with systemic agents, is an increasingly pertinent, yet poorly researched, area. Reprint requests to: Søren M. Bentzen, Ph.D., D.Sc., University of Wisconsin School of Medicine and Public Health, Department of Human Oncology, K4/316 Clinical Science Center, 600 Highland Avenue, Madison, WI 53792. Tel: (608) 265-8572; Fax: (608) 263- 9947; E-mail: bentzen@humonc.wisc.edu Acknowledgment—This work was partially supported by NIH grants CA014520 (S.M.B.), CA85181 (J.O.D.), and CA69579 (L.B.M.), and a grant from the Lance Armstrong Foundation (L.B.M.). Received April 8, 2009, and in revised form Sept 1, 2009. Accepted for publication Sept 2, 2009. S3 Int. J. Radiation Oncology Biol. Phys., Vol. 76, No. 3, Supplement, pp. S3–S9, 2010 Copyright Ó 2010 Elsevier Inc. Printed in the USA. All rights reserved 0360-3016/10/$–see front matter doi:10.1016/j.ijrobp.2009.09.040
  4. 4. ANALYZING RT-RELATED TOXICITY Cancer survivorship issues have been gaining prominence, partly because of the increasing number of cancer survivors; a tripling in the United States (2) between 1970 and 2001. This increase is the result of early diagnosis, screening ef- forts, improved treatments, and an increased incidence of many cancers. Radiation oncologists have pioneered record- ing and analysis of late treatment sequelae and the available literature on late effects is much richer for this modality than for cytotoxic or surgical treatments. However, toxicity is of- ten underreported, and probably underrecorded, even in the more rigorous framework of prospective clinical trials (3– 5). Clearly, this is a special concern in NTCP (normal tissue complication probability) modeling studies where the data analyzed often are retrospectively extracted from charts or databases. The US National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v3.0 is a comprehen- sive dictionary for recording and grading of side effects of all major cancer therapies (6). Widespread adoption of a com- mon grading system for adverse events, such as CTCAE, would improve between-study comparability and is encour- aged. However, CTCAE still combines multiple signs and symptoms into a single grade. Although this may be conve- nient for routine studies and comparisons of therapies across studies, it is associated with a loss of specificity in toxicity- specific studies (7). For such studies, including NTCP mod- eling studies, grades should be atomized (i.e., broken down to specific signs and symptoms that are likely to reflect specific radiation pathophysiologies). The SOMA (Subjective, Ob- jective, Management, Analytic) scale explicitly distinguishes between objective signs and subjective symptoms. For toxic- ity-specific studies, a ‘‘SOMAtized’’ scale—that is, a scale where these components of toxicity are kept separate—is preferable. Grouping several specific toxicities into a single composite endpoint is likely associated with a loss of statis- tical resolution (3, 8). THE EMAMI PAPER AND EARLY NTCP MODELING The paper by Emami et al. (9) is the most frequently cited paper ever published in the International Journal of Radia- tion Oncology Biology Physics, with 1,062 citations accord- ing to the ISI Web of Science (accessed February 3, 2009). This paper published the tolerance doses for irradiation of one third, two thirds, or the whole of various organs. Because high-quality clinical data were scarce, the task force took the bold approach to establish these doses by a simple consensus of clinical experience or opinions. In an accompanying paper, Burman et al. (10) fitted a Lyman model (11) to the Emami consensus dose–volume data thereby facilitating the use of Emami’s constraints for an arbitrary fraction of a whole organ uniformly irradiated. Further, Kutcher et al. (12) proposed a method, a so-called dose–volume histogram (DVH) reduc- tion algorithm, for reducing an arbitrary nonuniform dose distribution into a partial volume receiving the maximum dose, effectively allowing the extrapolation of Emami’s con- straints to any dose distribution. The mathematical method amounted to a common formula for taking a ‘‘generalized mean,’’ although this was not recognized at the time. This Lyman-Kutcher-Burman model, combining Lyman’s model with the Kutcher-Burman DVH reduction scheme, remains the most widely used NTCP model. Although the model claims no deep mechanistic validity, its mathematical form is sufficiently flexible to allow representation of various dose–volume dependencies. Within the structural resolution of current datasets, the Lyman-Kutcher-Burman model can typically not be rejected as a good fit of the data, although it is not always the best model considered. Probabilistic models, studied in groundbreaking papers in the 1980s by Schultheiss (13) and Withers (14), introduced concepts like serial and parallel tissue organization and functional sub- units and became conceptually influential but have played a relatively modest role in actual data analyses except for The Relative Seriality Model (15), that has found some use in analyzing clinical data. SMALL ANIMAL MODELS AND LIMITATIONS TO A DVH-BASED APPROACH DVH-based analyses inherently assume that organ func- tion is uniformly distributed within an organ. Experimental animal studies of the volume effect have produced important proof-of-principle insights that question this assumption. However, these have had relatively little impact on clinical NTCP modeling so far. In 1995, Travis et al. (16, 17) re- ported that partial organ irradiation of a volume of the mouse lung base was more likely to cause radiation pneumonitis than irradiating an identical volume of the apex or, even more pronounced, the middle regions of the lung. Because the histological damage in the lung did not vary with loca- tion, this finding has been interpreted as a result of variation in the functional importance of different lung regions. How- ever, some of the demonstrated effect may have also resulted from inadvertent inclusion of the central airways/vessels within the computed tomography–defined lung. Attempts at modeling location effects in human lung have only been tried relatively recently, with mixed results (see the paper by Marks et al. in this issue). Location effects have also been demonstrated in partial volume irradiation of the parotid gland (18), probably reflecting damage to the excretory ducts, blood vessels, and nerves. Another example where DVH- based analysis for the organ at risk may not be adequate is lung, where irradiation of the heart in addition to the lung has been shown in experimental animals to affect the risk of radiation induced pneumonitis as assessed by respiratory rate (19). Hopewell and Trott (20) analyzed experimental dose–vol- ume data and concluded that ‘‘Volume, as such, is not the rel- evant criterion, since critical, radiosensitive structures are not homogeneously distributed within organs.’’ Work by Trott et al. (21) in 1995 documented a volume effect for functional damage after irradiation of the rat rectum but found no S4 I. J. Radiation Oncology d Biology d Physics Volume 76, Number 3, Supplement, 2010
  5. 5. significant influence of volume on structural damage to the rectal wall. The theme of different radiation pathogenesis for different rectal side effects, and therefore varying radiobi- ological properties, has only relatively recently been system- atically analyzed in patients by the group at the Netherlands Kanker Instituut (22). Extensive studies by van der Kogel in the late 1980s show- ing that the probabilistic model did not correctly predict the probability of spinal cord injury after irradiation of two geo- metrically separated 4-mm segments of rat cervical spinal cord undoubtedly discouraged further exploration of this model in the analysis of clinical datasets (23). Van der Ko- gel’s studies were subsequently expanded into an elegant, systematic study of dose–volume effects in the rat spinal cord, ending with the sobering conclusion that not any of the 14 mathematical models, tried by the authors, could fit all the data (24). PROGRESS ON ALL FRONTS SINCE 1991 Much has changed since 1991 (Table 1). Many, mainly ret- rospective, clinical studies have been published on dose–vol- ume-outcome analysis of clinical data. The QUANTEC review identified >70 papers on radiation pneumonitis alone. Some of these studies are very large (e.g., a study of rectal ef- fects in 1,132 patients by Fiorini et al.) (25). There are quan- titative analyses of dose–volume-outcome relationships for >30 organs and tissues. More than a dozen mathematical dose volume models have been proposed. One class of NTCP models reduces the 3D dose matrix to a scalar, often thought of as an effective volume or an effec- tive dose received by a defined reference volume. This scalar is subsequently related to the incidence or risk of normal tis- sue toxicity through a sigmoid link function, typically a logis- tic or probit relationship. This model building strategy is similar to the one used originally by Lyman (11) and it may be reasonable classifying these as generalized Lyman models. The push from cell-killing based models towards heuristic models has been strengthened by novel insights into radiation pathogenesis of late effects (26) and an in- creased appreciation of the role of anatomical and physiolog- ical factors in normal tissue dysfunction. Other modeling approaches have been used such as princi- pal component analysis (27), contiguous (or cluster) damage model (28), and data mining to build multivariate models (29). Further approaches include the use of artificial neural networks (30) and support vector machines (31) as classifiers of patients with respect to the development of side effects. These methods are complementary to more traditional mod- eling and will undoubtedly be further explored in the coming years. THE QUANTEC INITIATIVE It was on this background that the QUANTEC Steering Committee was formed. Stimulated by a proposal from the Science Council of the American Association of Physicists in Medicine to revise and update the Emami guidelines, the QUANTEC group was formed from a loose network of re- searchers with a longstanding interest in dose–volume mod- eling. The Steering Committee defined three aims for QUANTEC. (1) To provide a critical overview of the current state of knowledge on quantitative dose–response and dose–vol- ume relationships for clinically relevant normal-tissue endpoints (2) To produce practical guidance allowing the clinician to reasonably (though not necessarily precisely) categorize toxicity risk based on dose–volume parameters or model results (3) To identify future research avenues that would help im- prove risk estimation or mitigation of early and late side effects of radiation therapy A kickoff workshop with 57 invited participants from North America and Europe was held in Madison, Wisconsin, in October 2007 with generous financial support from the American Association of Physicists in Medicine and the Board of the American Society for Therapeutic Radiation Oncology. The main deliverable from the workshop was the formation of a number of working groups charged with producing organ site-specific overviews of quantitative dose–volume relationships as well as groups producing vision papers on future research avenues in the field. The re- sults of these efforts are partly presented in this issue of the International Journal of Radiation Biology and Physics, again made possible with generous support from American Society for Therapeutic Radiation Oncology. Although overall progress has been real and substantial, research in the past two decades has also defined limitations to our current methods and the resulting knowledge. One of the main lessons from the literature overviews is that more uniform and comprehensive reporting would be a huge help when trying to combine data from multiple studies (see the paper by Jackson in this issue). Current best esti- mates of dose–volume parameters can in many situations be based on empirical data, in contrast to the consensus values proposed by Emami et al. However, there is still a lack of proper estimation of the uncertainty in these param- eters in most cases. Clinically, the literature on patient-related risk factors is scattered and often inconsistent from one study to the next. When patient- or treatment-related risk factors pa- rameters are not listed as significant in a given paper, it is of- ten not clear whether the factor has been tested or not. Therapeutically, RT is combined with drugs in more and more indications. Although calculating the risk associated with the RT dose distribution alone may provide some guid- ance, it cannot generally be assumed that giving a drug to- gether with radiation will even preserve the ranking of competing radiotherapy RT plans (32). The increased use of hypofractionation, and the use of an increasing number of beam orientations (e.g., rotational delivery), results in a rel- atively large volume of normal tissue receiving a low total dose and dose per fraction. The available dose–volume/out- come data may not be applicable in this setting. There has QUANTEC: scientific issues d S. M. BENTZEN et al. S5
  6. 6. been little discussion—and no consensus—on how models or dose–volume constraints should be adjusted if the fraction- ation scheme changes significantly. One study did adjust the individual bins in the dose–volume histogram for dose per fraction (33), but the fits obtained with a/b = 3 Gy, 10 Gy, or infinity ( = physical dose) were not statistically differ- ent for that given treatment fractionation scheme. However, the model may not be valid without correction if a signifi- cantly different fractionation scheme is used. MODEL VALIDATION AND DATA ANALYSIS On the model side, there is a need for improved data ana- lytical methods and a more critical appraisal of the various di- mensions of model validity. Face validity The first screen when judging a model fit to a set of data is face validity. Is the probability of a side effect a nondecreas- ing function of dose, dose per fraction, and volume, given that two of these three variables are held constant? If the model includes patient characteristics, such as age, smoking history, or comorbidity, is the effect estimated using the model consistent with published clinical data? Are confi- dence intervals or standard errors of the estimates reasonable in view of the analyzed sample size and the number of events actually recorded? Internal validity Internal validity relates to whether the model actually pro- vides a reasonable representation of the data to which it is fit- ted. To this end, a graphical representation of the fit to the data may be informative. This may be supplemented with a formal goodness of fit statistics, such as the chi-square test. The null hypothesis being tested is that the discrepancy between the observed toxicity incidence data and the data ex- pected under the fitted model can be explained by chance alone. A test p value <0.05 means that the null hypothesis can be rejected at the 5% significance level (i.e., the model ‘‘does not fit the data’’). A nonsignificant p value, however, may not be very informative as typical NTCP model fits to clinical data sets yield a relatively low statistical power of goodness of fit statistics. In other words, two alternative mathematical models may be quite divergent without either one of them being rejected based on the goodness of fit test. The log-likelihood may also be used for comparing the fit of competing models to a data set; again, studies have shown that competing models tend to produce very similar log-like- lihood values for a given data set (34). For nested models (i.e., models that differ by the inclusion of one additional pa- rameter), the difference in log-likelihood forms the basis for the likelihood ratio test, a robust test for the statistical signif- icance of adding this parameter. For non-nested models the Akaike Information Criterion has been used by some authors, see for example Tucker (34). Some authors look at NTCP models as classifiers (i.e., as a way to separate patients who do or do not develop a given toxicity). This leads to a standard predictive testing frame- work, where sensitivity, specificity, and negative and posi- tive predictive values can be estimated. The area under the curve of the receiver operating characteristic curve can be used as a figure of merit for comparing alternative models. Note, however, that a model reliably identifying subgroups of patients with, say, a 10% and a 40% risk of toxicity would Table 1. Dose-volume relationships ca. 1990 and 2009+ ca. 1990 2009+ Treatment usually with parallel opposing fields or ‘‘box’’ techniques—three-dimensional conformal radiation therapy gaining ground clinically in some centers Widespread use of conformal techniques, including intensity- modulated radiation therapy, often resulting in highly nonuniform dose distribution in organs at risk with large volumes receiving low doses Radiation therapy typically delivered as single modality— spectrum of toxicities relatively well-characterized Many curative cases receiving combined modality therapy—many regimens are very toxic leading to problems with compliance Conventional fractionation dominates—clinical trials of hyperfractionation and accelerated fractionation Conventional fractionation dominates—clinical trials of hypofractionation in progress Authors search for a ‘‘safe’’ dose–volume constraint Increasing appreciation of the risk-benefit tradeoff in an individual patient—a monotonic increase in toxicity risk with increasing dose/increasing volume Early interest in normal tissue complication probability modeling— Lyman model most widely used Change from ‘‘more models’’ to ‘‘more data’’—Lyman model still widely used, but new modeling strategies are being pursued Analysis often based on groups of patients Analysis of individual patient level data Lack of consistency in contouring organs at risk among investigators Lack of consistency in contouring organs at risk among investigators Models often applied with parameters from the literature—no adjustment for patient or treatment characteristics Statistical estimation of model parameters—often with adjustment for significant patient or treatment characteristics Toxicity underscored and underreported in most studies Toxicity underscored and underreported in most studies—despite attempts to define dictionaries for toxicity reporting such as Common Terminology Criteria for Adverse Events A lack of quantitative, evidence-based dose–volume constraints— Emami et al. develops a ground-breaking set of consensus constraints for partial organ irradiation A lack of quantitative, evidence-based dose-volume constraints— the QUANTEC group initiates a series of systematic literature reviews S6 I. J. Radiation Oncology d Biology d Physics Volume 76, Number 3, Supplement, 2010
  7. 7. most likely be clinically useful, but if the latter group is la- beled as ‘‘responders’’ there would still be a 60% false-pos- itive rate. In this case a binned comparison of observed and expected toxicity may be more informative (35). Cross-vali- dation techniques have been suggested for NTCP modeling (29), but have so far not been widely applied. External validity External validity addresses how well the model explains the variability in response seen in an independent dataset, preferably from another institution. Multivariate NTCP models are often overfitted in the sense that they include too many parameters relative to the number of events ana- lyzed. This may result in strongly correlated parameter esti- mates and, although such a model may pass the test for internal validity with flying colors, it often has poor external validity. Differences between institutions in the scoring of re- actions, in patient demographics, in the burden of comorbid- ities as well as in treatment characteristics may all contribute to a reduced predictive power of a model when tested in an independent dataset. Relatively little research has been per- formed on external validity of NTCP models. Bradley et al. (36) applied a radiation pneumonitis model fitted to data from 219 Washington University patients to an independent series of radiation pneumonitis data from 129 patients en- rolled in the Radiation Therapy Oncology Group 93-11 trial and concluded that the model ‘‘performed poorly’’ in the new dataset. A model fitted to the two datasets combined was found to give an odds ratio of approximately two between the 33% of all patients with the riskiest plans and the 33% of patients with the safest plans, but much of the variability is still unexplained. Similar problems with generalizabilty are seen in studies applying different models on the same da- taset: as an example, Tsougos et al. (37) found that six pub- lished models predicted an incidence of Grade 3+ radiation pneumonitis ranging from 4% to 21% in a group of 47 pa- tients. One issue is that various dose–volume metrics often are strongly correlated within a given dataset (38). This may lead to problems with multicollinearity, which, although it may not affect the internal validity of the model, can lead to reduced generalizability. This becomes particularly rele- vant for extrapolation in dose–volume space (i.e., if a model derived on basis of ‘‘similar’’ dose plans is applied to a very different dose distribution) (39). Clinical utility Dose–volume constraints are used in routine dose planning as an integral part of the informal optimization of therapeutic ratio that inverse planning entails. Acceptable dose distribu- tions are identified from a assessment of the risk:benefit ratio in an individual patient—often on the basis of clinical expe- rience rather than on numerical estimates from dose–volume models. Population constraints are very important in this con- text but can obviously not stand alone. Careful consideration should be given not only to the numerical value of these con- straints but also to their statistical uncertainty. Using these values directly in dose–plan optimization should be done with great caution. The fact that dose–volume constraints or NTCP models are used in clinical practice does not in itself prove that they im- prove cancer care from an evidence-based medicine perspec- tive. Ultimately, the clinical utility of NTCP modeling should be tested in randomized controlled trials. Phase I/II dose es- calation trials in patients with non–small-cell lung cancer, where the individual patient is assigned a dose based on an NTCP estimate (40), have been completed or are in progress for example at University of Wisconsin (41), University of Michigan (42), and the Maastricht Radiation Oncology clinic in the Netherlands (43). The goal is to test these strategies in randomized Phase III trials. This could potentially provide an evidence base for risk adaptive radiotherapy for non–small- cell lung cancer based on NTCP modeling. RESEARCH PRIORITIES: BEYOND QUANTEC Important research priorities, identified above as well as in the QUANTEC thematic and organ-site reviews, include the following. A. Development of tools and strategies for prospective recording of specific pathologies after RT alone or com- bined with drugs B. Wider application of methods adjusting for censoring when analyzing late effects C. Quantification of the influence of physiologic factors and comorbidities on the expression of toxicities D. The continued development of robust normal tissue end- points including patient reported outcomes to further our understanding of the relationship between toxicity and quality of life E. Development of methods for synthesizing results across studies with appropriate estimation of prediction uncer- tainty F. Establishment of large continually growing data bases with full access to the 3D dose matrix and linkage with biomarkers and clinical outcome G. Prospective testing of model performance in independent datasets, preferably from clinical trials H. Improved understanding of the interaction between dose distribution on one hand and dose per fraction or admin- istration of other modalities on the other I. Developing strategies for testing the clinical utility of NTCP models. J. Development of methods for recording actual delivered dose in an individual patient after fractionated radiother- apy. K. Additional studies that use molecular and functional im- aging as an intermediary between local damage and organ-level signs and symptoms. Adjustment for dose distribution remains a major chal- lenge in clinical radiation research. A systematic effort, capa- ble of winning competitive research funding, is required to take this field to the next stage. QUANTEC: scientific issues d S. M. BENTZEN et al. S7
  8. 8. REFERENCES 1. Bentzen SM, Rosenthal DI, Weymuller E. Increasing toxicity in non-operative head and neck cancer treatment: Investigations and interventions. Int J Radiat Oncol Biol Phys 2007;69(2 Suppl):S79–82. 2. Center for Disease Control (USA). Cancer survivorship— United States, 1971–2001. Available at http://www.cdc.gov/ mmwr/preview/mmwrhtml/mm5324a3.htm. Accessed Decem- ber 1, 2005. 3. Bentzen SM, Trotti A. Evaluation of early and late toxicities in chemoradiation trials. J Clin Oncol 2007;25:4096–4103. 4. Trotti A, Bentzen SM. The need for adverse effects reporting standards in oncology clinical trials. J Clin Oncol 2004;22: 19–22. 5. Papanikolaou PN, Ioannidis JP. Availability of large-scale evi- dence on specific harms from systematic reviews of randomized trials. Am J Med 2004;117:582–589. 6. National Cancer Institute. Common Terminology Criteria for Adverse Events v3.0. Available at http://ctep.cancer.gov/ protocolDevelopment/electronic_applications/docs/ctcaev3.pdf. Accessed February 9, 2009. 7. Bentzen SM, Dorr W, Anscher MS, et al. Normal tissue effects: Reporting and analysis. Semin Radiat Oncol 2003;13:189–202. 8. Peeters ST, Lebesque JV, Heemsbergen WD, et al. Localized volume effects for late rectal and anal toxicity after radiotherapy for prostate cancer. Int J Radiat Oncol Biol Phys 2006;64:1151– 1161. 9. Emami B, Lyman J, Brown A, et al. Tolerance of normal tissue to therapeutic irradiation. Int J Radiat Oncol Biol Phys 1991;21: 109–122. 10. Burman C, Kutcher GJ, Emami B, et al. Fitting of normal tissue tolerance data to an analytic function. Int J Radiat Oncol Biol Phys 1991;21:123–135. 11. Lyman JT. Complication probability as assessed from dose-vol- ume histograms. Rad Res 1985;104: S-13–S-19. 12. Kutcher GJ, Burman C, Brewster L, et al. Histogram reduction method for calculating complication probabilities for three-di- mensional treatment planning evaluations. Int J Radiat Oncol Biol Phys 1991;21:137–146. 13. Schultheiss TE, Orton CG, Peck RA. Models in radiotherapy: Volume effects. Med Phys 1983;10:410–415. 14. Withers HR, Taylor JM, Maciejewski B. Treatment volume and tissue tolerance. Int J Radiat Oncol Biol Phys 1988;14:751– 759. 15. Ka¨llman P, Aagren A, Brahme A. Tumour and normal tissue re- sponses to fractionated non-uniform dose delivery. Int J Radiat Biol 1992;62:249–262. 16. Liao ZX, Travis EL, Tucker SL. Damage and morbidity from pneumonitis after irradiation of partial volumes of mouse lung [see comments]. Int J Radiat Oncol Biol Phys 1995;32:1359– 1370. 17. Tucker SL, Liao ZX, Travis EL. Estimation of the spatial distri- bution of target cells for radiation pneumonitis in mouse lung. Int J Radiat Oncol Biol Phys 1997;38:1055–1066. 18. Konings AW, Faber H, Cotteleer F, et al. Secondary radiation damage as the main cause for unexpected volume effects: A his- topathologic study of the parotid gland. Int J Radiat Oncol Biol Phys 2006;64:98–105. 19. van Luijk P, Faber H, Meertens H, et al. The impact of heart ir- radiation on dose-volume effects in the rat lung. Int J Radiat On- col Biol Phys 2007;69:552–559. 20. Hopewell JW, Trott KR. Volume effects in radiobiology as ap- plied to radiotherapy. Radiother Oncol 2000;56:283–288. 21. Trott KR, Tamou S, Sassy T, et al. The effect of irradiated vol- ume on the chronic radiation damage of the rat large bowel. Strahlenther Onkol 1995;171:326–331. 22. Peeters ST, Hoogeman MS, Heemsbergen WD, et al. Rectal bleeding, fecal incontinence, and high stool frequency after con- formal radiotherapy for prostate cancer: Normal tissue compli- cation probability modeling. Int J Radiat Oncol Biol Phys 2006; 66:11–19. 23. Stewart FA, van der Kogel AJ. Volume effects in normal tis- sues. In: Steel GG, editor. Basic clinical radiobiology. 3rd ed. London: Arnold; 2002. p. 42–51. 24. van Luijk P, Bijl HP, Konings AW, et al. Data on dose-volume effects in the rat spinal cord do not support existing NTCP models. Int J Radiat Oncol Biol Phys 2005;61:892–900. 25. Fiorino C, Fellin G, Rancati T, et al. Clinical and dosimetric predictors of late rectal syndrome after 3D-CRT for localized prostate cancer: Preliminary results of a multicenter prospective study. Int J Radiat Oncol Biol Phys 2008;70:1130–1137. 26. Bentzen SM. Preventing or reducing late side effects of radia- tion therapy: Radiobiology meets molecular pathology. Nat Rev Cancer 2006;6:702–813. 27. Dawson LA, Biersack M, Lockwood G, et al. Use of principal component analysis to evaluate the partial organ tolerance of normal tissues to radiation. Int J Radiat Oncol Biol Phys 2005;62:829–837. 28. Stavreva N, Niemierko A, Stavrev P, et al. Modelling the dose- volume response of the spinal cord, based on the idea of damage to contiguous functional subunits. Int J Radiat Biol 2001;77: 695–702. 29. El Naqa I, Bradley J, Blanco AI, et al. Multivariable modeling of radiotherapy outcomes, including dose-volume and clinical factors. Int J Radiat Oncol Biol Phys 2006;64:1275–1286. 30. Gulliford SL, Webb S, Rowbottom CG, et al. Use of artificial neural networks to predict biological outcomes for patients re- ceiving radical radiotherapy of the prostate. Radiother Oncol 2004;71:3–12. 31. Chen S, Zhou S, Yin FF, et al. Investigation of the support vec- tor machine algorithm to predict lung radiation-induced pneu- monitis. Med Phys 2007;34:3808–3814. 32. Khuntia D, Harris J, Bentzen SM, et al. Increased oral mucositis after IMRT versus non-IMRT when combined with cetuximab and cisplatin or docetaxel for head and neck cancer: Preliminary results of RTOG 0234 [abstract]. Int J Radiat Oncol Biol Phys 2008;72:S33. 33. Tucker SL, Liu HH, Wang S, et al. Dose-volume modeling of the risk of postoperative pulmonary complications among esophageal cancer patients treated with concurrent chemoradio- therapy followed by surgery. Int J Radiat Oncol Biol Phys 2006; 66:754–761. 34. Tucker SL, Dong L, Cheung R, et al. Comparison of rectal dose-wall histogram versus dose-volume histogram for model- ing the incidence of late rectal bleeding after radiotherapy. Int J Radiat Oncol Biol Phys 2004;60:1589–1601. 35. De Ruysscher D, Dehing C, Bremer RH, et al. Maximal neutro- penia during chemotherapy and radiotherapy is significantly as- sociated with the development of acute radiation-induced dysphagia in lung cancer patients. Ann Oncol 2007;18:909– 916. 36. Bradley JD, Hope A, El N, et al. A nomogram to predict radia- tion pneumonitis, derived from a combined analysis of RTOG 93-11 and institutional data. Int J Radiat Oncol Biol Phys 2007;69:985–992. 37. Tsougos I, Nilsson P, Theodorou K, et al. NTCP modelling and pulmonary function tests evaluation for the prediction of radia- tion induced pneumonitis in non-small-cell lung cancer radio- therapy. Phys Med Biol 2007;52:1055–1073. 38. Yorke ED, Kutcher GJ, Jackson A, et al. Probability of radia- tion-induced complications in normal tissues with parallel S8 I. J. Radiation Oncology d Biology d Physics Volume 76, Number 3, Supplement, 2010
  9. 9. architecture under conditions of uniform whole or partial organ irradiation. Radiother Oncol 1993;26:226–237. 39. Deasy JO, Niemierko A, Herbert D, et al. Methodological is- sues in radiation dose-volume outcome analyses: summary of a joint AAPM/NIH workshop. Med Phys 2002;29:2109–2127. 40. Lawrence TS, Kessler ML, Robertson JM. 3-D conformal radi- ation therapy in upper gastrointestinal cancer. The University of Michigan experience. Front Radiat Ther Oncol 1996;29:221– 228. 41. Adkison JB, Khuntia D, Bentzen SM, et al. Dose escalated, hypofractionated radiotherapy using helical tomotherapy for inoperable non-small cell lung cancer: Preliminary results of a risk-stratified phase I dose escalation study. Technol Cancer Res Treat 2008;7:441–448. 42. Kong FM, Hayman JA, Griffith KA, et al. Final toxicity results of a radiation-dose escalation study in patients with non-small- cell lung cancer (NSCLC): Predictors for radiation pneumonitis and fibrosis. Int J Radiat Oncol Biol Phys 2006;65:1075–1086. 43. van Baardwijk A, Bosmans G, Boersma L, et al. Individualized radical radiotherapy of non-small-cell lung cancer based on nor- mal tissue dose constraints: A feasibility study. Int J Radiat Oncol Biol Phys 2008;71:1394–1401. QUANTEC: scientific issues d S. M. BENTZEN et al. S9
  10. 10. INTRODUCTORY PAPER USE OF NORMAL TISSUE COMPLICATION PROBABILITY MODELS IN THE CLINIC LAWRENCE B. MARKS, M.D.,* ELLEN D. YORKE, PH.D.,y ANDREW JACKSON, PH.D.,y RANDALL K. TEN HAKEN, PH.D.,z LOUIS S. CONSTINE, M.D.,x AVRAHAM EISBRUCH, M.D.,z SØREN M. BENTZEN, PH.D.,k JIHO NAM, M.D.,* AND JOSEPH O. DEASY, PH.D.{ *Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC; y Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY; z Department of Radiation Oncology, University of Michigan, Ann Arbor, MI; x Department of Radiation Oncology, University of Rochester Cancer Center, Rochester, NY; k Department of Human Oncology, University of Wisconsin School of Medicine, Madison, WI; and { Department of Radiation Oncology, Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO The Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) review summarizes the currently available three-dimensional dose/volume/outcome data to update and refine the normal tissue dose/volume toler- ance guidelines provided by the classic Emami et al. paper published in 1991. A ‘‘clinician’s view’’ on using the QUANTEC information in a responsible manner is presented along with a description of the most commonly used normal tissue complication probability (NTCP) models. A summary of organ-specific dose/volume/outcome data, based on the QUANTEC reviews, is included. Ó 2010 Elsevier Inc. QUANTEC, NTCP. INTRODUCTION Historically, radiation therapy (RT) fields/doses were selected empirically, based largely on experience. Physicians relied on clinical intuition to select field sizes/doses. They understood that these empiric guidelines were imprecise and did not fully reflect the underlying anatomy, physiology, and dosimetry. A great promise of three-dimensional (3D) treatment plan- ning was quantitative correlates of doses/volumes with clin- ical outcomes. This promise was partly delivered. When 3D dosimetric information became widely available, guidelines were needed to help physicians predict the relative safety of proposed treatment plans, although only limited data were available. In 1991, investigators pooled their clinical experience, judgment, and information regarding partial or- gan tolerance doses, and produced the ‘‘Emami paper’’ (1). While ‘‘Emami’’ is often criticized, the paper clearly stated the uncertainties and limitations in its recommendations, and it is widely admired for addressing a clinical need. During the last 18 years, numerous studies reported asso- ciations between dosimetric parameters and normal tissue outcomes. The QUANTEC (quantitative analysis of normal tissue effects in the clinic) articles summarize the available data to update/refine the estimates provided by Emami et al. A central goal of QUANTEC is to summarize this infor- mation in a clinically useful manner. We hope the information will improve patient care by pro- viding clinicians and treatment planners with tools to esti- mate ‘‘optimal/acceptable’’ 3D dose distributions. We hope that at least some of the summary tables, graphs, and models presented will be reproduced and posted in resident work- rooms, dosimetry planning areas, and physician offices, as is currently done with the Emami et al. tables. The information provided by QUANTEC is not ideal, and care must be taken to apply it correctly in the clinic. We herein present a ‘‘clinician’s view’’ on using the QUANTEC information in a responsible manner, highlighting the diverse type of limitations of the presented data. LIMITATIONS INHERENT IN EXTRACTING DATA FROM THE LITERATURE The information presented is largely extracted from publi- cations. Because different investigators often present infor- mation differently (e.g., actuarial vs. crude complication rates), pooling data from multiple studies may be inaccurate. Reprint requests to: Lawrence B. Marks, M.D., Department of Radiation Oncology, Box 7512 University of North Carolina, Chapel Hill, NC 27514. Tel: (919) 966-0400; Fax: (919) 966- 7681; E-mail: marks@med.unc.edu Conflict of interest: none. Acknowledgments—The authors express special thanks to Jessica Hubbs and Janet Bailey for their assistance in the preparation of this manuscript. Partially supported by National Institutes of Health grants CA85181 (J.O.D.) and CA69579 (L.B.M.) and by a grant from the Lance Armstrong Foundation (L.B.M.). Received Jan 6, 2009, and in revised form July 1, 2009. Accepted for publication July 2, 2009. S10 Int. J. Radiation Oncology Biol. Phys., Vol. 76, No. 3, Supplement, pp. S10–S19, 2010 Copyright Ó 2010 Elsevier Inc. Printed in the USA. All rights reserved 0360-3016/10/$–see front matter doi:10.1016/j.ijrobp.2009.07.1754
  11. 11. Summary tables are often included to help the reader better understand the primary data. LIMITATIONS OF PREDICTIVE MODELS Some studies use models to estimate the complication risk. Care should be taken when applying models, especially when clinical dose/volume parameters are beyond the range of data used to generate the model/parameters. Models and dose/vol- ume recommendations are only as good as the data available. Typically, they are based on dose–volume histograms (DVHs). DVHs are not ideal representations of the 3D doses as they discard all organ-specific spatial information (and hence assume all regions are of equal functional importance), and often do not consider fraction size variations. They are usually based on a single planning computed tomography (CT) scan that does not account for anatomic variations during therapy (Fig. 1). Interinstitutional/physician differences in image segmentation, dose calculation, patient populations, and preferred beam arrangements may limit model exportabil- ity. Before introducing a predictive model into a clinical prac- tice, it is prudent to assess if its predictions ‘‘make sense’’ in regard to that clinic’s treatment plans and experience. EVOLVING FRACTIONATION SCHEDULES RT-induced normal tissue responses are fraction size de- pendent. Throughout the QUANTEC reviews, this variable is acknowledged and, where possible, considered by making adjustments for fraction size based on the linear quadratic (LQ) model. Nevertheless, a/b ratios are uncertain. Particular care must be taken when QUANTEC information is applied to stereotactic RT, where the fraction size is much different than that in the cited literature. For very novel fractionations, even the validity of the LQ model is questioned (2). Even when the prescribed tumor dose is ‘‘conventionally’’ fractionated, the fraction size seen by the normal tissue may have varied over time. When ‘‘Emami’’ was published, most external RT was delivered with opposing fields, and shrink- ing field techniques—the normal tissue was irradiated with a fairly uniform fraction size. Modern techniques often use multiple beams (with or without concurrent boosts); the vol- ume of normal tissue exposed to low doses is often increased and the dose is delivered at fraction sizes ranging from z0 to the prescribed fraction size. COMBINED MODALITY THERAPY Use of sequential/concurrent chemotherapy/RT is increas- ing for many tumors. Concurrent chemotherapy is typically believed to exacerbate the severity of normal tissue reactions, but data quantifying this is often lacking. Even when such data are available, the chemotherapy doses, schedules and agents—which may influence outcomes—are in evolution. HOST FACTORS Host factors (e.g., chronic liver disease, genetic, lifestyle) may affect dose–response relationships and are partly respon- sible for the shallowness of these relationships in the patient population. It is likely that incorporating these factors, when they are known, will produce better models/correlations/pre- dictors of toxicity. BALANCING THE RISKS TO DIFFERENT ORGANS Different morbidities vary in their clinical significance. Grade 2 toxicity has a different clinical meaning for the esophagus than for the rectum. Furthermore, different pa- tients may have different levels of acceptance for injuries. When comparing competing treatment plans, there is usually a tradeoff; for example, should we accept a certain dose to the lung or to the esophagus? For most cases, modern treatments will redistribute, not eliminate, the dose to normal tissue. The fundamental problem of treatment planning is how to balance exposure of one organ against that of another. Unfortunately, there is no objective way to do this. Investigators have considered the risks to multiple organs, and computed the probability of uncomplicated tumor con- trol (3–5). Others have attempted to incorporate the relative importance of different toxicities by considering their impact on patients’ quality of life. This approach generates a global figure of merit such as the ‘‘quality of life adjusted tumor con- trol probability’’ (6, 7). The utility of this approach, although conceptually attractive, is not clear. FOLLOW-UP DURATION If dose–effect relationships for a late complication are de- rived from a patient population with very poor prognosis, they may be limited by lack of long-term follow-up, and not applicable to patients with a better prognosis (e.g., apply- ing brain toxicity from patients with high-grade glioma to pa- tients with low-grade tumors). The risk of normal tissue complication occurs in the con- text of a patient’s expected longevity. Radiation therapy is an effective anti-cancer therapy and can provide good Fig. 1. A three-dimensional dose distribution is reduced to a two-di- mensional (2D) dose–volume histogram (DVH) by discarding all spatial, anatomic and physiologic data. The 2D graph is then further reduced to a single value of merit, such as the mean dose, the percent of the organ receiving $20 Gy (V20), or a model-based normal tis- sue complication probability (NTCP). Use of NTCP models in the clinic d L. B. MARKS et al. S11
  12. 12. palliation for patients with recurrent/metastatic/incurable dis- ease. In these settings, concern for late normal tissue reac- tions often should not limit the application of RT. For example, reirradiation of the whole brain for recurrent brain metastases to cumulative doses well above tolerance can pro- vide palliation for these challenging cases (8–10) for which concern about late toxicity may be unnecessary. Similarly, RT for locally advanced lung cancers may routinely exceed the normal dose limits for lung and heart. In these instances, there typically are no good alternative therapies available. Withholding thoracic RT because of the risk of pericarditis or pneumonitis may not be therapeutically rational. These concerns are most applicable to recently trained ra- diation oncologists who are accustomed to using 3D dosimet- ric information for most of their clinical decision making. They may be uncomfortable in clinical settings where large RT fields need to be applied without 3D dosimetry in order to provide palliative effect. It is the physician’s responsibility to tell dosimetrists/physicists when it is appropriate to pro- ceed with treatment without a formal 3D dose/volume assess- ment and/or suspend the conventional departmental dose/ volume guidelines. RELATING ‘‘WHOLE TREATMENT’’ DVHS WITH ACUTE TOXICITIES For some organs, a relevant acute toxicity may occur dur- ing the course of RT (i.e., before the delivery of the entire RT course). Relating the incidence of such acute events to a DVH that reflects the whole treatment course may be somewhat il- logical. It might be preferable to try to relate acute events to the dose delivered before symptom onset (or even to doses received a number of days before symptom onset, if there is a known latency time). If a consistent set of treatment fields is used throughout the entirecourseoftreatment(e.g.,nofieldreductions),the‘‘whole course’’ DVH might bea reasonable surrogate for the 3D doses delivered before acute symptom onset. Therefore, in these sit- uations, it still might be a reasonable to relate the risk of acute events to a DVH that reflects the whole treatment course. However, field arrangements often change during therapy, thus altering the dose/volume parameters for the target organ (e.g., initial AP-PA fields plus a subsequent off-cord boost). In these situations, the ‘‘whole-course’’ DVH is less likely to be a reasonable surrogate for the 3D doses delivered prior to the acute toxicity. Thus, some dose/volume/outcomes analy- ses for acute endpoints that consider the so-called whole- course DVH may be suspect. Further complicating the issue is the fact that the duration of symptoms (that may also influence the scoring of toxicity), may be affected by RT dose delivered after the onset of symptoms. In this regard, the whole-course DVH may indeed be reasonable to consider in dose/volume/outcomes analyses. A similar concern may apply for analyses of late effects. If a late toxicity results from a severe acute toxicity occurring during the course of RT, relating that late event to the whole-course DVH may also be suboptimal. TUMOR COVERAGE VS. NORMAL TISSUE RISK For most curative patients, a marginal miss is more serious than a normal tissue complication. For many tumors, recur- rences are difficult to manage, cause severe morbidity, and usually result in mortality. Target coverage should generally not be compromised to reduce the normal tissue risks. This is exemplified by the experience from Israel in treating orbital lymphomas. In 24 tumors treated in 23 patients, intraorbital recurrence was seen in four of 12 (33%) of the tumors treated with conformal fields (including the radiographically defined gross tumor with margin), vs. none of the 12 tumors treated with conventional whole-orbit techniques (11). Similarly, in- vestigators at Washington University noted a higher relapse rate in lung cancers closer to the spinal cord; perhaps reflect- ing compromised GTV coverage because of spinal cord pro- tection (Ref. 12 and personal communication from J. Deasy, 2008). Engels et al. noted a reduction in 5-year biochemical disease-free survival rate (from 91% to 58%) in patients irra- diated for prostate cancer with the addition of implanted seeds for localization, and tighter ‘‘PTV margins,’’ with the intent of reducing exposure to surrounding normal tissues (13). The use of improved diagnostic imaging, and improved immobi- lization and image guidance during RT, may facilitate a more realistic PTV margin to be applied safely. APPLICABILITY TO CHILDREN In the young, a mosaic of tissues develop at different rates and temporal sequences. In adults, the same tissues are in a steady state with relatively slow cell renewal kinetics. The vulnerability of tissues to RT typically increases during the periods of rapid proliferation. Consequently, generalizing data from adult to pediatric populations is problematic and re- quires caution. Ideally, specific data from investigations on children should be used to predict risks in this population. UNDERSTANDING THE BASICS OF NTCP MODELS Despite these caveats, model-based risk estimates are a re- ality. Physicians routinely use models, in their broadest sense, to make treatment decisions. Use of metrics such as the mean lung dose, and cord maximum dose to estimate risks are models, albeit simple ones. We present a primer re- garding the basic principles of NTCP models. Generally, NTCP models attempt to reduce complicated dosimetric and anatomic information to a single risk measure. Most models fall into one of three categories: DVH-reduction models, tissue architecture models, and multiple-metric (i.e. multimetric) models. DVH-reduction models Although most applications of DVH reduction models are to nonuniform dose distributions, they are based on estimated complication probability under uniform irradiation. A dose- response for uniform irradiation is described by a mathemat- ical function with at least two parameters: for example TD50, which denotes the dose for 50% complication probability, and m, which is inversely proportional to the slope at the S12 I. J. Radiation Oncology d Biology d Physics Volume 76, Number 3, Supplement, 2010
  13. 13. steepest part of the response curve. For a patient cohort with diverse radiosensitivities, the response curve is shallower (larger m) than for a biologically similar population receiving the same treatment (14). Various S-shaped functions are used to fit dose-response data, including the probit function used by Lyman (below). To account for the dose heterogeneity typical of parallel opposed beam irradiation (partial organ uniform irradiation), Jolles (15) described tissue tolerance as a power law of the fractional volume irradiated: DðVirradiatedÞ ¼ D À Vreference Á À Vreference Á ðVirradiatedÞ n ; [1] Here Vreference isthereferencevolumeandVirradiatedistheuni- formly irradiated volume. D is the corresponding tolerance doses, representing a chosen level on the dose–response curve, such as TD50. The parameter n controls the volume effect. Lyman (16) used this power law model to define the risks associated with partial organ volume uniform irradiation. From Eq. 1, decreasing the irradiated volume fraction shifts the dose–response curve (TD50) to higher doses by a factor of the irradiated fractional volume raised to the power ‘‘neg- ative n’’. The effect of different n values on tolerance dose is shown in Fig. 2. For example, if the n parameter equals 1, then the TD50 of irradiation for one half of the volume is expected to increase by a factor of 2, whereas if the n parameter is 0.5, TD50 for irradiation of one half the volume would increase by a factor of the square root of 2. To generalize this to clinically realistic, heterogeneous dose distributions, a summary statistic—the generalized equivalent uniform dose (gEUD)—is often introduced (17, 18). The gEUD is the dose that, if given uniformly to the en- tire organ, is believed to yield the same complication rate as the true dose distribution. The gEUD is computed by sum- ming over all voxels in the organ: gEUD ¼ 1 Nvoxels ðd 1=n 1 þ d 1=n 2 þ . þ d 1=n NVoxels Þ !n : [2] Here NVoxelsis the number of equi-volume voxels, and di is the dose to the ith voxel. The gEUD equation is consistent with the power-law assumption. Together, the gEUD equa- tion and the Lyman assumptions are often referred to as the Lyman-Kutcher-Burman (LKB) model (16, 18–20). Note that some analyses use the parameter n, and some use the parameter a, equal to 1/n. Both are shown in Fig. 2. When n is small (and a is large), changes in irradiated volume make only a modest change in relative tolerance whereas, as n gets larger (and a gets smaller), the tolerance dose depends strongly on the irradiated volume fraction. Serial vs. parallel complication endpoints There have been efforts to devise mechanistic models that ascribe the volume dependence of some complications to dis- ruption of the organ’s functional architecture by RT (21–23). In so-called parallel complications, subvolumes of the or- gan function relatively independently. Sufficiently small por- tions of the organ can be damaged without clinical effect; the complication is observed only after more than a critical vol- ume is damaged. Parallel complications have large volume effects, and for this reason they are often likened to LKB models with n z 1 (as is found in analyses of liver, lung, and kidney complications). More detailed models exist, in- cluding models employing the concept of a functional re- serve, representing a hypothesized fraction of organ function that can be lost before a complication is likely (23). In contrast, serial complications occur when even a small portion of the organ suffers damage. Here, n is small (e.g., z 0.1 for late rectal bleeding). Serial complications are most affected by the hottest portion of the DVH. More de- tailed models exist for this type of endpoint as well, including models that make explicit the size of small subunits, all of which need to be preserved to avoid a complication (23). Figure 3 shows how different parts of an example DVH contributetotheoverall gEUDas nvaries. Notethat: (a) thelow- est value of n results in the highest gEUD corresponding to the hottest point on the DVH (more appropriate for serial-like end- points), and (b) the lower dose bins contribute more when n ap- proaches 1 (more appropriate for parallel-like endpoints). Multimetric models Clinicians frequently estimate complication risk via a sin- gle DVH point based on a statistically significant dose/vol- ume cut-point reported in one or more studies. An example is the often-used V20 (percentage of lung receiving 20 Gy) as a predictor of radiation pneumonitis (24). However, such single ‘‘volume threshold’’ rules are overly simple, and often easily manipulated by the treatment planner, or by the optimization software. Optimizing based on such a threshold may introduce a ‘kink’ in one part of the DVH to achieve a desired ‘‘threshold value,’’ while inadequately constraining the rest. An infinite number of very different dose distributions (some likely with very different risks) can have the same V20. The same is true for any Fig. 2. As the (idealized) irradiated organ fraction decreases, the tol- erance dose (D) increases, more so for larger values of n or smaller values of a (=1/n). VReferencerepresents the reference volume (usually the full organ volume), and VIrradiated represents the volume irradiated. Use of NTCP models in the clinic d L. B. MARKS et al. S13
  14. 14. DVH-reduction scheme, including the LKB models; mark- edly different-looking DVHs can yield the same NTCP. However, models that consider a larger fraction of the DVH are less easily manipulated (and may be more radiobi- ologically logical) than are the threshold models that consider only one point on the DVH. Nonetheless, reports correlating single DVH point thresholds to toxicity are common and are often included in the QUANTEC reviews. The more robust multimetric approach selects several uni- variate-significant dosimetric features of the dose distribution (e.g., multiple Vdose values) as well as medical variables and use multivariate analysis together with sophisticated statisti- cal methods or ‘‘machine learning’’ algorithms to pick out the most significant combinations (25). In-depth discussions of this topic can be found in reviews elsewhere (26–29). SUMMARY A major goal of this issue of the Journal is to provide prac- tical clinical guidance for physicians and treatment planners. The information presented is not perfect, as evidenced by the multiple caveats above. The lack of good predictors is some- what unsettling. Nevertheless, the QUANTEC papers present a valuable review. Over time, with the help of new studies guided by new physical, statistical and biological technolo- gies, we hope to be able to update this information so that pa- tient care can be continually improved. With the multiple caveats outlined above in mind, a limited summary of available organ-specific dose/volume/outcome data is provided in Table 1. This is not meant to replace the detailed information provided in the individual organ-spe- cific reviews. Treatment planners and physicians are encour- aged to read the individual papers to understand the origin, certainty, and nuances that apply to the dose/volume/out- come data provided in the summary table. In general, the dose/volume/outcome data provided in the summary table are associated with generally-regarded clinically acceptable rates of injury; for example, low rates for severe injury (e.g., brain necrosis), and higher rates of less severe end- points (e.g., erectile dysfunction). Thus, these are dose/vol- ume parameters that might be widely applied in clinical practice. Obviously many clinical situations require treat- ments that exceed the dose/volume values shown. Where practical, some dose response data are included as well. Fur- thermore, most of the data in the table is based on convention- ally fractionated radiation using conventional techniques, and may or may not be applicable in other settings. Fig. 3. Volume–effect parameter. The effect of changing the n parameter (= 1/a) in the Lyman model with the generalized equivalent uniform dose equation to compute normal tissue complication probability (NTCP) is shown. Starting with a (real) rectal dose–volume histogram (DVH) computed for an intensity-modulated radiation therapy (IMRT) prostate pa- tient plan (upper left), the DVH is first transformed into a single number by the generalized equivalent uniform dose (gEUD) equation that weights dose values exponentially. Lower figure shows the cumulative contribution of each part of the DVH to the overall gEUD for all bins below the given dose value. As one can see, if a is set to 1 (rightmost curve), gEUD would equal the mean dose (e.g., for parallel organs), and many voxels with doses as low as 20 to 30 Gy contribute significantly to the gEUD and therefore may increase the final NTCP value (although contributions are proportional to dose, so higher dose still does contribute more for the same volume). As n decreases, the value of gEUD is a determined mainly by the highest dose voxels (e.g., for series organs). Typical clinical values for late rectal bleeding are n z 0.1. Un- fortunately, investigators sometimes report a (especially when discussing the gEUD) and other-times use n, where n =1/a. S14 I. J. Radiation Oncology d Biology d Physics Volume 76, Number 3, Supplement, 2010
  15. 15. Table 1. QUANTEC Summary: Approximate Dose/Volume/Outcome Data for Several Organs Following Conventional Fractionation (Unless Otherwise Noted)* Organ Volume segmented Irradiation type (partial organ unless otherwise stated)y Endpoint Dose (Gy), or dose/volume parametersy Rate (%) Notes on dose/volume parameters Brain Whole organ 3D-CRT Symptomatic necrosis Dmax 60 3 Data at 72 and 90 Gy, extrapolated from BED modelsWhole organ 3D-CRT Symptomatic necrosis Dmax = 72 5 Whole organ 3D-CRT Symptomatic necrosis Dmax = 90 10 Whole organ SRS (single fraction) Symptomatic necrosis V12 5–10 cc 20 Rapid rise when V12 5–10 cc Brain stem Whole organ Whole organ Permanent cranial neuropathy or necrosis Dmax 54 5 Whole organ 3D-CRT Permanent cranial neuropathy or necrosis D1–10 cck #59 5 Whole organ 3D-CRT Permanent cranial neuropathy or necrosis Dmax 64 5 Point dose 1 cc Whole organ SRS (single fraction) Permanent cranial neuropathy or necrosis Dmax 12.5 5 For patients with acoustic tumors Optic nerve / chiasm Whole organ 3D-CRT Optic neuropathy Dmax 55 3 Given the small size, 3D CRT is often whole organzz Whole organ 3D-CRT Optic neuropathy Dmax 55–60 3–7 Whole organ 3D-CRT Optic neuropathy Dmax 60 7-20 Whole organ SRS (single fraction) Optic neuropathy Dmax 12 10 Spinal cord Partial organ 3D-CRT Myelopathy Dmax = 50 0.2 Including full cord cross-section Partial organ 3D-CRT Myelopathy Dmax = 60 6 Partial organ 3D-CRT Myelopathy Dmax = 69 50 Partial organ SRS (single fraction) Myelopathy Dmax = 13 1 Partial cord cross-section irradiated Partial organ SRS (hypofraction) Myelopathy Dmax = 20 1 3 fractions, partial cord cross-section irradiated Cochlea Whole organ 3D-CRT Sensory neural hearing loss Mean dose #45 30 Mean dose to cochlear, hearing at 4 kHz Whole organ SRS (single fraction) Sensory neural hearing loss Prescription dose #14 25 Serviceable hearing Parotid Bilateral whole parotid glands 3D-CRT Long term parotid salivary function reduced to 25% of pre-RT level Mean dose 25 20 For combined parotid glands{ Unilateral whole parotid gland 3D-CRT Long term parotid salivary function reduced to 25% of pre-RT level Mean dose 20 20 For single parotid gland. At least one parotid gland spared to 20 Gy{ (Continued) UseofNTCPmodelsintheclinicdL.B.MARKSetal.S15
  16. 16. Table 1. QUANTEC Summary: Approximate Dose/Volume/Outcome Data for Several Organs Following Conventional Fractionation (Unless Otherwise Noted)* (Continued) Organ Volume segmented Irradiation type (partial organ unless otherwise stated)y Endpoint Dose (Gy), or dose/volume parametersy Rate (%) Notes on dose/volume parameters Bilateral whole parotid glands 3D-CRT Long term parotid salivary function reduced to 25% of pre-RT level Mean dose 39 50 For combined parotid glands (per Fig. 3 in paper) { Pharynx Pharyngeal constrictors Whole organ Symptomatic dysphagia and aspiration Mean dose 50 20 Based on Section B4 in paper Larynx Whole organ 3D-CRT Vocal dysfunction Dmax 66 20 With chemotherapy, based on single study (see Section A4.2 in paper) Whole organ 3D-CRT Aspiration Mean dose 50 30 With chemotherapy, based on single study (see Fig. 1 in paper) Whole organ 3D-CRT Edema Mean dose 44 20 Without chemotherapy, based on single study in patients without larynx cancer**Whole organ 3D-CRT Edema V50 27% 20 Lung Whole organ 3D-CRT Symptomatic pneumonitis V20 # 30% 20 For combined lung. Gradual dose response Whole organ 3D-CRT Symptomatic pneumonitis Mean dose = 7 5 Excludes purposeful whole lung irradiationWhole organ 3D-CRT Symptomatic pneumonitis Mean dose = 13 10 Whole organ 3D-CRT Symptomatic pneumonitis Mean dose = 20 20 Whole organ 3D-CRT Symptomatic pneumonitis Mean dose = 24 30 Whole organ 3D-CRT Symptomatic pneumonitis Mean dose = 27 40 Esophagus Whole organ 3D-CRT Grade $3 acute esophagitis Mean dose 34 5–20 Based on RTOG and several studies Whole organ 3D-CRT Grade $2 acute esophagitis V35 50% 30 A variety of alternate threshold doses have been implicated. Appears to be a dose/volume responseWhole organ 3D-CRT Grade $2 acute esophagitis V50 40% 30 Whole organ 3D-CRT Grade $2 acute esophagitis V70 20% 30 Heart Pericardium 3D-CRT Pericarditis Mean dose 26 15 Based on single study Pericardium 3D-CRT Pericarditis V30 46% 15 Whole organ 3D-CRT Long-term cardiac mortality V25 10% 1 Overly safe risk estimate based on model predictions (Continued) S16I.J.RadiationOncologydBiologydPhysicsVolume76,Number3,Supplement,2010
  17. 17. Table 1. QUANTEC Summary: Approximate Dose/Volume/Outcome Data for Several Organs Following Conventional Fractionation (Unless Otherwise Noted)* (Continued) Organ Volume segmented Irradiation type (partial organ unless otherwise stated)y Endpoint Dose (Gy), or dose/volume parametersy Rate (%) Notes on dose/volume parameters Liver Whole liver – GTV 3D-CRT or Whole organ Classic RILDyy Mean dose 30-32 5 Excluding patients with pre-existing liver disease or hepatocellular carcinoma, as tolerance doses are lower in these patients Whole liver – GTV 3D-CRT Classic RILD Mean dose 42 50 Whole liver – GTV 3D-CRT or Whole organ Classic RILD Mean dose 28 5 In patients with Child-Pugh A preexisting liver disease or hepatocellular carcinoma, excluding hepatitis B reactivation as an endpointWhole liver – GTV 3D-CRT Classic RILD Mean dose 36 50 Whole liver –GTV SBRT (hypofraction) Classic RILD Mean dose 13 18 5 5 3 fractions, for primary liver cancer 6 fractions, for primary liver cancer Whole liver – GTV SBRT (hypofraction) Classic RILD Mean dose 15 20 5 5 3 fractions, for liver metastases 6 fractions, for liver metastases 700 cc of normal liver SBRT (hypofraction) Classic RILD Dmax 15 5 Critical volume based, in 3–5 fractions Kidney Bilateral whole kidneyz Bilateral whole organ or 3D-CRT Clinically relevant renal dysfunction Mean dose 15–18 5 Bilateral whole kidneyz Bilateral whole organ Clinically relevant renal dysfunction Mean dose 28 50 Bilateral whole kidneyz 3D-CRT Clinically relevant renal dysfuntction V12 55% 5 For combined kidney V20 32% V23 30% V28 20% Stomach Whole organ Whole organ Ulceration D100k 45 7 Small bowel Individual small bowel loops 3D-CRT Grade $ 3 acute toxicityx V15 120 cc 10 Volume based on segmentation of the individual loops of bowel, not the entire potential peritoneal space Entire potential space within peritoneal cavity 3D-CRT Grade $ 3 acute toxicityx V45 195 cc 10 Volume based on the entire potential space within the peritoneal cavity (Continued) UseofNTCPmodelsintheclinicdL.B.MARKSetal.S17
  18. 18. Table 1. QUANTEC Summary: Approximate Dose/Volume/Outcome Data for Several Organs Following Conventional Fractionation (Unless Otherwise Noted)* (Continued) Organ Volume segmented Irradiation type (partial organ unless otherwise stated)y Endpoint Dose (Gy), or dose/volume parametersy Rate (%) Notes on dose/volume parameters Rectum Whole organ 3D-CRT Grade $ 2 late rectal toxicity, Grade $ 3 late rectal toxicity V50 50% 15 10 Prostate cancer treatment Whole organ 3D-CRT Grade $ 2 late rectal toxicity, Grade $ 3 late rectal toxicity V60 35% 15 10 Whole organ 3D-CRT Grade $ 2 late rectal toxicity, Grade $ 3 late rectal toxicity V65 25% 15 10 Whole organ 3D-CRT Grade $ 2 late rectal toxicity, Grade $ 3 late rectal toxicity V70 20% 15 10 Whole organ 3D-CRT Grade $ 2 late rectal toxicity, Grade $ 3 late rectal toxicity V75 15% 15 10 Bladder Whole organ 3D-CRT Grade $ 3 late RTOG Dmax 65 6 Bladder cancer treatment. Variations in bladder size/shape/ location during RT hamper ability to generate accurate data Whole organ 3D-CRT Grade $3 late RTOG V65 #50 % Prostate cancer treatment Based on current RTOG 0415 recommendation V70 #35 % V75 #25 % V80 #15 % Penile bulb Whole organ 3D-CRT Severe erectile dysfunction Mean dose to 95% of gland 50 35 Whole organ 3D-CRT Severe erectile dysfunction D90k 50 35 Whole organ 3D-CRT Severe erectile dysfunction D60-70 70 55 Abbreviations: 3D-CRT = 3-dimensional conformal radiotherapy, SRS = stereotactic radiosurgery, BED = Biologically effective dose, SBRT = stereotactic body radiotherapy, RILD = radi- ation-induced liver disease, RTOG = Radiation Therapy Oncology Group. * All data are estimated from the literature summarized in the QUANTEC reviews unless otherwise noted. Clinically, these data should be applied with caution. Clinicians are strongly advised to use the individual QUANTEC articles to check the applicability of these limits to the clinical situation at hand. They largely do not reflect modern IMRT. y All at standard fractionation (i.e., 1.8–2.0 Gy per daily fraction) unless otherwise noted. Vx is the volume of the organ receiving $ x Gy. Dmax = Maximum radiation dose. z Non-TBI. x With combined chemotherapy. k Dx = minimum dose received by the ‘‘hottest’’ x% (or x cc’s) of the organ. { Severe xerostomia is related to additional factors including the doses to the submandibular glands. ** Estimated by Dr. Eisbruch. yy Classic Radiation induced liver disease (RILD) involves anicteric hepatomegaly and ascites, typically occurring between 2 weeks and 3 months after therapy. Classic RILD also involves elevated alkaline phosphatase (more than twice the upper limit of normal or baseline value). zz For optic nerve, the cases of neuropathy in the 55 to 60 Gy range received z59 Gy (see optic nerve paper for details). Excludes patients with pituitary tumors where the tolerance may be reduced. S18I.J.RadiationOncologydBiologydPhysicsVolume76,Number3,Supplement,2010
  19. 19. REFERENCES 1. Emami B, Lyman J, Brown A, et al. Tolerance of normal tissue to therapeutic irradiation. Int J Radiat Oncol Biol Phys 1991;21: 109–122. 2. Kirkpatrick J, Meyer J, Marks L. The linear-quadratic model is inappropriate to model high-dose per fraction effects. Semin Ra- diat Oncol 2008;18:240–243. 3. Trotti A, Colevas AD, Setser A, et al. CTCAE v3.0: Develop- ment of a comprehensive grading system for the adverse effects of cancer treatment. Semin Radiat Oncol 2003;13:176–181. 4. Langer M, Morrill SS, Lane R. A test of the claim that plan rank- ings are determined by relative complication and tumor-control probabilities. Int J Radiat Oncol Biol Phys 1998;41:451–457. 5. Agren A, Brahme A, Turesson I. Optimization of uncompli- cated control for head and neck tumors. Int J Radiat Oncol Biol Phys 1990;19:1077–1085. 6. MiftenMGO,PardaDS,ProsnitzRG,MarksLM.Usingqualityof life information to rationally incorporate normal tissue effects into treatment plan evaluation and scoring. In: Rubin PCL, Marks LB, Okunieff P, editors.Cured I—lent.Lateeffectsof cancer treatment on normal tissue. 1st ed. New York: Springer; 2007. 7. Amols HI, Zaider M, Hayes MK, et al. Physician/patient-driven risk assignment in radiation oncology: Reality or fancy? Int J Radiat Oncol Biol Phys 1997;38:455–461. 8. Morris DE. Clinical experience with retreatment for palliation. Semin Radiat Oncol 2000;10:210–221. 9. Sadikov E, Bezjak A, Yi QL, et al. Value of whole brain re-ir- radiation for brain metastases—single centre experience. Clin Oncol 2007;19:532–538. 10. Kurup P, Reddy S, Hendrickson FR. Results of re-irradiation for cerebral metastases. Cancer 1980;46:2587–2589. 11. Pfeffer MR, Rabin T, Tsvang L, et al. Orbital lymphoma: Is it necessary to treat the entire orbit? Int J Radiat Oncol Biol Phys 2004;60:527–530. 12. Hope AJ, Lindsay PE, El Naqa I, et al. Clinical, dosimetric, and location-related factors to predict local control in non-small cell lung cancer. Int J Radiat Oncol Biol Phys 2005;63. S231–S231. 13. Engels B, Soete G, Verellen D, et al. Conformal Arc Radiotherapy for Prostate Cancer: Increased Biochemical Failure in Patients With Distended Rectum on the Planning Computed Tomogram Despite Image Guidance by Implanted Markers. Int J Radiat Oncol Biol Phys 2009;74:388–391. 14. Zagars GK, Schultheiss TE, Peters LJ. Inter-tumor heterogene- ity and radiation dose-control curves. Radiother Oncol 1987;8: 353–361. 15. Jolles B. Area factor in skin reaction. Br Emp Cancer Campaign Rep 1939;16. 16. Lyman JT. Complication probability as assessed from dose-vol- ume histograms. Radiat Res Suppl 1985;8:S13–S19. 17. Mohan R, Mageras GS, Baldwin B, et al. Clinically relevant op- timization of 3-D conformal treatments. AAPM 1992;19:933– 944. 18. Niemierko A. A generalized concept of equivalent uniform dose (EUD). Med Phys 1999;26:1100. 19. Kutcher GJ, Burman C, Brewster L, et al. Histogram reduction method for calculating complication probabilities for three-di- mensional treatment planning evaluations. Int J Radiat Oncol Biol Phys 1991;21:137–146. 20. Burman C, Kutcher GJ, Emami B, et al. Fitting of normal tissue tolerance data to an analytic function. Int J Radiat Oncol Biol Phys 1991;21:123–135. 21. Withers HR, Tayor JMG, Maciejewski B. Treatment volume and tissue tolerance. Int J Radiat Oncol Biol Phys 1988;15: 751–759. 22. Jackson A, Ten Haken RK, Robertson JM, et al. Analysis of clinical complication data for radiation hepatitis using a paral- lel architecture model. Int J Radiat Oncol Biol Phys 1995;31: 883–891. 23. Niemierko A, Goitein M. Calculation of normal tissue compli- cation probability and dose-volume histogram reduction schemes for tissues with a critical element architecture. Radio- ther Oncol 1991;20:166–176. 24. Graham MV, Purdy JA, Emami B, et al. Clinical dose-volume histogram analysis for pneumonitis after 3D treatment for non- small cell lung cancer (NSCLC). Int J Radiat Oncol Biol Phys 1999;45:323–329. 25. El Naqa I, Bradley J, Blanco AI, et al. Multivariable modeling of radiotherapy outcomes including dose-volume and clinical factors. Int J Radiat Oncol Biol Phys 2006;64:1275–1286. 26. Jackson A, Yorke E. NTCP and TCP for treatment planning. In: Memorial Sloan-Kettering Cancer Center, editor. A practical guide to intensity-modulated radiation therapy. Madison, WI: Medical Physics Pubishers; 2003; pp. 185–220. 27. Deasy JO, Niemierko A, Herbert D, et al. Methodological is- sues in radiation dose-volume outcome analyses: Summary of a joint AAPM/NIH workshop. Med Phys 2002;29:2109–2127. 28. YorkeED.Modelingtheeffectsofinhomogeneous dose distribu- tions in normal tissues. Semin Radiat Oncol 2001;11:197–209. 29. Moiseenko J, Deasy O, Van Dyk J. Radiobiological modeling for treatment planning. In: Van Dyk J, editor. The modern tech- nology of radiation oncology: A compendium for medical phys- icists and radiation oncologists, vol. 2. Madison, WI: Medical Physics Publishing; 2005, pp. 185–220. Use of NTCP models in the clinic d L. B. MARKS et al. S19
  20. 20. QUANTEC: ORGAN SPECIFIC PAPER Central Nervous System: Brain RADIATION DOSE–VOLUME EFFECTS IN THE BRAIN YAACOV RICHARD LAWRENCE, M.R.C.P.,* X. ALLEN LI, PH.D.,y ISSAM EL NAQA, PH.D.,z CAROL A. HAHN, M.D.,x LAWRENCE B. MARKS, M.D.,{ THOMAS E. MERCHANT, D.O. PH.D.,k AND ADAM P. DICKER, M.D. PH.D.* *Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA; y Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI; z Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO; x Department of Radiation Oncology, Duke University Medical Center, Durham, NC; { Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC; k Department of Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, TN We have reviewed the published data regarding radiotherapy (RT)-induced brain injury. Radiation necrosis ap- pears a median of 1–2 years after RT; however, cognitive decline develops over many years. The incidence and se- verity is dose and volume dependent and can also be increased by chemotherapy, age, diabetes, and spatial factors. For fractionated RTwith a fraction size of 2.5 Gy, an incidence of radiation necrosis of 5% and 10% is predicted to occur at a biologically effective dose of 120 Gy (range, 100–140) and 150 Gy (range, 140–170), respectively. For twice-daily fractionation, a steep increase in toxicity appears to occur when the biologically effective dose is 80 Gy. For large fraction sizes ($2.5 Gy), the incidence and severity of toxicity is unpredictable. For single fraction radiosurgery, a clear correlation has been demonstrated between the target size and the risk of adverse events. Sub- stantial variation among different centers’ reported outcomes have prevented us from making toxicity–risk predic- tions. Cognitive dysfunction in children is largely seen for whole brain doses of $18 Gy. No substantial evidence has shown that RT induces irreversible cognitive decline in adults within 4 years of RT. Ó 2010 Elsevier Inc. Radiotherapy, stereotactic radiosurgery, brain, tolerance, side effects. 1. CLINICAL SIGNIFICANCE Radiotherapy (RT) plays an important role in the curative and palliative treatment of patients with primary and metastatic brain tumors. Primary brain tumors account for 22% of tumors in those 18 years of age. Brain metastases occur in z30% of patients diagnosed with solid tumors, afflicting z170,000 Americans annually. The acute and late effects of RT on the brainarecommonandrepresentasignificantsourceofmorbid- ity. Such morbidity is particularly troubling in patients with baseline tumor-related dysfunction. In addition, the radiation fields used to treat the upper aerodigestive track (e.g., pharynx and nasal cavities) often include a portion of the brain. 2. ENDPOINTS The acute side effects of RT to the brain include nausea, vomiting, and headache; vertigo and seizures are less fre- quent. These symptoms are transient and generally respond to medication. The present report summarizes the dose–volume predictors for the principal late side effects of RT to the brain: radiation necrosis and cognitive deterioration. A biopsy is rarely per- formed to confirm suspected radiation necrosis. The working definition used by most of the studies listed in Tables 1 and 2 was ‘‘new symptoms with suggestive radiologic findings.’’ However, most investigators have reported their late toxic- ity rates as crude numbers according to the number of patients treated rather than the number at risk (i.e., the survivors). This method understates the risk, because some subjects will have died before the toxicity has had a chance to develop. The actuarial rates have rarely been discussed. Surgery, chemo- therapy, steroids, antiepileptic agents, and opioids impair neurologic and cognitive function, further confounding the interpretation of suspected RT toxicity. Reprint requests to: Yaacov Richard Lawrence, M.R.C.P., Department of Radiation Oncology, Jefferson Medical College of Thomas Jefferson University, Bodine Cancer Center, 111 S. 11th St., Philadelphia, PA 19107. Tel: (215) 955-6700; Fax: (215) 955- 0412; E-mail: richard.lawrence@jefferson.edu Y. R. Lawrence is supported by The ASCO Cancer Foundation Young Investigator Award. Any opinions, findings, and conclusions expressed in this material are those of the author(s) and do not nec- essarily reflect those of the American Society of Clinical Oncology or The ASCO Cancer Foundation. L. B. Marks is supported by NIH R01 69579 and the Lance Armstrong Foundation. A. P. Dicker is supported by National Institutes of Health Grant CA10663, Tobacco Research Settlement Fund (State of Pennsylva- nia), and the Christine Baxter Fund. Conflict of interest: none. Received Nov 26, 2008, and in revised form Feb 24, 2009. Accepted for publication Feb 27, 2009. S20 Int. J. Radiation Oncology Biol. Phys., Vol. 76, No. 3, Supplement, pp. S20–S27, 2010 Copyright Ó 2010 Elsevier Inc. Printed in the USA. All rights reserved 0360-3016/10/$–see front matter doi:10.1016/j.ijrobp.2009.02.091
  21. 21. 3. CHALLENGES DEFINING VOLUMES There is little disagreement regarding image segmentation of the entire brain, and little intra- or interfraction movement occurs. However, segmenting the brain subregions is challenging (e.g., the superior boundary of the brain stem). Currently the utility of subregion definition is unclear. 4. REVIEW OF DOSE–VOLUME DATA Radiation necrosis For radiosurgery, the incidence of necrosis depends on the dose, volume, and region irradiated (1–10) (Table 1 and Fig. 1). The Radiation Therapy Oncology Group conducted a dose-escalation study that sought to define the maximal dose for targets of different sizes; all subjects had previously undergone whole brain irradiation. The maximal tolerated dose for targets 31–40 mm in diameter was 15 Gy, and for targets 21–30 mm in diameter, it was 18 Gy. For targets 20 mm, the maximal tolerated dose was 24 Gy (11). The volume of brain receiving $12 Gy has been shown to corre- late with both the incidence of radiation necrosis and asymp- tomatic radiologic changes (Table 1). The large variation in absolute complication rates among studies (Fig. 1) is difficult to comprehend, but it might relate to differences in the definitions of the volume and toxicity, the avoidance of critical structures, and the type and length of clinical follow-up. For fractionated RT, the relationship between the radiation dose and radiation necrosis for partial brain irradiation is pre- sented in Table 2 (12–19) and Fig. 2, segregated by the frac- tionation scheme. Different fractionation schemes were compared using the biologically effective dose (BED) (20), with an a/b ratio of 3. For standard fractionation, a dose–re- sponse relationship exists, such that an incidence of side ef- fects of 5% and 10% occur at a BED of 120 Gy (range, 100–140) and 150 Gy (range, 140–170), respectively (corre- sponding to 72 Gy [range, 60–84] and 90 Gy [range, 84–102] in 2-Gy fractions). For twice-daily fractionation, a steep in- crease in toxicity appears to occur when the BED is 80 Gy. For daily large fraction sizes (2.5 Gy), the incidence Table 1. Dose–volume predictors of radiation necrosis after stereotactic radiosurgery Reference Diagnosis Technique Patients (n) Dmin* (Gy) RN incidence (%) Subgroup (cm3 ) RN incidence (%) Primary toxicity predictor Other risk factors 1 AVM GK 823 ? 5 Average dose in 20 cm3 2 Mixed LINAC 133 15.0 (7.0–25.0) 12.8 V10: 10 vs. 10 0 vs. 23.7 V10 Location 3 AVM GK 307 20.9 (12–30) 10.7 V12 Location 4 AVM LINAC 73 16 (10–22) 14 Tx volume: 1 1–3.9 4–13.9 14 0 15 14 27 Treatment volume Dose, previous brain insult 5 Mixed GK 243 20 (10–30) 7 V10 Repeated radiosurgery, Glioma 6 Mixed GK 749 18 (16–19)y ? Prescription volume: 0.05–0.66 0.67–3 3.1–8.6 8.7–95.1 0 3 7 9 Prescription volume 7 AVM Proton beam 1250 10.5 (4–65) 4.1 Dose and volume combined Older age, location 8 AVM ? 269 ? 4.7 V12 9 Brain metastases GK 137 16 (12–25) 11.4 Tx volume: 2 2 3.7 16 Volume 10 Tumor GK 129 17.3 (11–25) 30 V12: 0–5 5–10 10–15 15 23 20 54 57 V12 Location, previous WBRT, male Abbreviations: Dmin = minimal dose; RN = radiation necrosis; AVM = arteriovenous malformation; GK = gamma knife; LINAC = linear accelerator teletherapy machine; V10 = percentage of volume receiving $10 Gy; V12 = percentage of volume receiving $ 12 Gy; Tx = treat- ment; WBRT = whole brain radiotherapy. * Data presented as mean, with range in parentheses, unless otherwise noted. y Range refers to 25th to 75th quartile. Radiation dose–volume effects in brain d Y. R. LAWRENCE et al. S21
  22. 22. and severity of toxicity is unpredictable. The reader is cau- tioned against overinterpreting the data presented in Fig. 2, which was created from a heterogeneous data pool (i.e., dif- ferent target volumes, endpoints, sample sizes, and brain re- gions). No evidence has shown that children are especially at risk of radiation necrosis (21, 22). Table 2. Dose–volume predictors of radiation necrosis after fractionated radiotherapy Reference Patients (n) Disease Volume Fraction size* Prescribed dose (Gy) Fractions/ week* BED (Gy) RN incidence (%) Comment 12 141 NPC TL 2 66 5 110 0 5-y Actuarial rate 12 126 NPC TL 2.5 60 4 110 0 ’’ ’’ 12 89 NPC TL 2.5 60 5 110 1.4 ’’ ’’ 12 53 NPC TL 3.5 59.5 3 129 8.1 ’’ ’’ 12 218 NPC TL 2 62.5 5 108 1.5 ’’ ’’ 12 109 NPC TL 2 62.5 5 108 1.4 ’’ ’’ 12 212 NPC TL 2.5 61 4 119 0.6 ’’ ’’ 12 48 NPC TL 1.6 71.2 10 115 14 ’’ ’’ 13 56 NPC TL 3.8 45.6 2 103 4.8 10-y Actuarial rate 13 621 NPC TL 4.2 50.4 2 121 18.6 ’’ ’’ 13 320 NPC TL 2.5 60 2 110 4.6 ’’ ’’ 12 105 NPC TL 2 67 5 112 1 Data represent dose range and fractionation parameters; mean values given; time of evaluation not clearly stated 12 378 NPC TL 2 67 5 107 1.1 ’’ ’’ 12 86 NPC TL 2.1 54 5 92 1.2 ’’ ’’ 12 143 NPC TL 1.9 62 5 101 1.4 ’’ ’’ 12 152 NPC TL 3 60 5 120 3.3 ’’ ’’ 12 18 NPC TL 2.4 60 5 108 5.6 ’’ ’’ 12 82 NPC TL 2.5 60 5 110 19.5 Time of evaluation not clearly stated 12 23 NPC TL 1.6 67.2 10 103 34.8 ’’ ’’ 12 77 NPC TL 1.6 71.2 10 131 40.3 ’’ ’’ 14 60 HGG PB 1.6 51.2 10 79 1.6 Received nitrosourea; endpoint, possible RN on 18-mo imaging 14 66 HGG PB 1.2 68.4 10 96 6.1 ’’ ’’ 14 51 HGG PB 1.2 79.2 10 111 17.7 ’’ ’’ 15 291 HGG PB 2 ? 5 103 4 Assume a/b of 2, BED included initial and salvage RT; some patients received chemotherapy; range of fraction sizes used; time of evaluation not clearly stated 15 11 HGG PB 2 ? 5 138 9 ’’ ’’ 15 23 HGG PB 2 ? 5 173 17 ’’ ’’ 15 23 HGG PB 2 ? 5 208 22 ’’ ’’ 16 101 LGG PB 1.8 50.4 5 81 2.5 16 102 LGG PB 1.8 64.8 5 104 11 17 213 BM WB 3 30 5 60 0 Median survival only 6 mo; later events might have been missed; time of evaluation not clearly stated 17 216 BM WB+B 1.6 54.4 10 83 0.4 ’’ ’’ 18 63 BM WB+B 1.6 48 10 74 0.0 ’’ ’’ 18 121 BM WB+B 1.6 54.4 10 83.4 1.7 ’’ ’’ 18 105 BM WB+B 1.6 64 10 98.4 1.9 ’’ ’’ 18 56 BM WB+B 1.6 70.4 10 108 1.8 ’’ ’’ 19 11 NPC TL 1.6 64 10 98 27 Refers to dose received by temporal lobe; time of evaluation not clearly stated 19 70 NPC TL 1.2 70.8 10 99 0 ’’ ’’ Abbreviations: NPC = nasopharyngeal cancer; TL = temporal lobe; BM = brain metastases; LGG = low-grade glioma; HGG = high grade glioma; WB = whole brain; WB+B = whole brain 32 Gy plus boost; PB = partial brain; RN = radiation necrosis. * For most fractions. S22 I. J. Radiation Oncology d Biology d Physics Volume 76, Number 3, Supplement, 2010