QUANTEC

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

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

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
doi:10.1016/j.ijrobp.2009.09.040

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

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

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

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.

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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
doi:10.1016/j.ijrobp.2009.07.1754

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

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

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.

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.

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
D1–10 cck
#59 5
Whole organ 3D-CRT Permanent cranial
Dmax 64 5 Point dose 1 cc
Whole organ SRS (single fraction) Permanent cranial
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
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

Table 1. QUANTEC Summary: Approximate Dose/Volume/Outcome Data for Several Organs Following Conventional Fractionation (Unless Otherwise Noted)* (Continued)
Organ
Volume
segmented
Irradiation type
otherwise stated)y
Endpoint
Dose (Gy), or
dose/volume
parametersy
Rate (%)
Notes on
Bilateral whole
parotid glands
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
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

Organ
Volume
segmented
Irradiation type
otherwise stated)y
Endpoint
Dose (Gy), or
dose/volume
parametersy
Rate (%)
Notes on
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

Organ
Volume
segmented
Irradiation type
otherwise stated)y
Endpoint
Dose (Gy), or
dose/volume
parametersy
Rate (%)
Notes on
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,
V60 35% 15
10
V65 25% 15
10
V70 20% 15
10
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

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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
doi:10.1016/j.ijrobp.2009.02.091

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

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
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
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
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.

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