49th AAPM Annual Meeting, Minneapolis, 22-26 July 2007
USING CONFORMAL RADIOBIOLOGY TO FIND THE ‘BEST’ TREATMENT PLAN
Alan E. Nahum PhD
Physics Department
Clatterbridge Centre for Oncology
Bebington, Wirral
Merseyside CH63 4JY UK
Email: alan.nahum@ccotrust.nhs.uk
Preface
Physicists working in radiotherapy spend a lot of their time measuring doses in phantoms and then
calculating the dose distributions in patients due to a particular arrangement of beams. This is
because, according to the present state-of-the-art practice, the radiation oncologist prescribes the
treatment in terms of a (uniform) dose to the target volume accompanied by some sort of constraint on
the dose to one or more organs-at-risk. Well, this is not quite true, as today IMRT/‘inverse planning’ is
forcing a change of this practice, but the philosophy has not changed i.e. prescription is done in terms
of doses and volumes.
However, it can be argued that the endpoints in radiotherapy that are truly of relevance are not dose
distributions but the probability of (local) tumour control, often known as the Tumour Control
Probability (TCP) and the Probability of Normal-Tissue Complications (NTCP). PART TWO of this
course deals with the modelling of TCP and NTCP and how they can be actively used in treatmentplan optimisation with the emphasis on the spatial distribution of the absorbed dose within the target
volume, though not forgetting that fractionation is an indispensible ‘degree of freedom’ in the search
for optimal radiotherapy.
Some of the reasons why models for TCP and NTCP might be useful follow (the references cited are
not intended to be exhaustive):
†
Dose distributions in three dimension (3D) are inherently very complex and some way of
assimilating this vast amount of information is needed (Mauro 1989; Goitein 1992).
†
Biological models enable estimates to be made of the effect of uncertainties in dose and
patient position on therapy outcome (Boyer and Schultheiss 1988; Mijnheer et al. 1989;
Mackay et al. 1999; Zavgorodni 2004).
†
The effect of non-uniformities in the tumour dose distribution can be approximately quantified
(Brahme 1984; Sanchez-Nieto and Nahum 1999; Tome´ and Fowler 2000).
†
The values for α (especially important for tumours) and α/β from, for example, clonogenic
assays can be both extracted from and fed into such models (Deacon et al. 1984; Peters et
al. 1989; Mauro et al. 1989; West 1995; Bentzen 1997; Fenwick 1998; Sanchez-Nieto et al.
2001a; Buffa et al. 2001b; Levegrün et al. 2001, 2002; Wang et al. 2003a; Xiong et al. 2005;
Carlone et al. 2006).
†
Estimates can be made of the effects on local tumour control of hypoxia and other information
derived from functional imaging (Poppel et al. 2002; Nahum et al. 2003; Ruggieri 2004;
Nioutsikou et al. 2005; Ruggieri and Nahum 2006).
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49th AAPM Annual Meeting, Minneapolis, 22-26 July 2007
†
The clinical effect of improvements in dose distributions through the use of new beam-delivery
technology (e.g. MLCs, IMRT), 3D treatment planning systems, and new radiation modalities
(brachytherapy, protons, light ions) can be approximately quantified (Webb 1993; Lee et al.
1994; Isacsson 1998; Gagliardi 1998, 2001; King et al. 2000; De Meerleer et al. 2000; Nahum
and Glimelius 2001; Nutting et al. 2002).
†
Optimisation/inverse planning is beginning to be done in terms of biological criteria such as
highest TCP for a fixed low value of NTCP, equivalent uniform dose (EUD) etc. (Källman
1992; Nahum and Tait 1992; Mohan et al. 1992; Brahme 1999, 2001; De Gersem et al. 1999;
Engelsman et al. 2001; Iori 2001; Sanchez-Nieto et al. 2001a; Schwarz et al. 2003;
Peñagarıcano et al. 2005; Kim and Tomé 2006; Hoffmann et al. 2006; Chen et al. 2007).
†
Models for TCP and NTCP can serve as an aid to clarity of thought about radiotherapy
(Dutreix et al. 1988).
Philosophy
Radiobiology has ‘classically’ concerned itself with the properties of cell-survival curves. The linearquadratic model (e.g. Steel 2002, Chapman 2003) represents one of its triumphs in not only throwing a
lot of light on the mechanisms of cell-killing by radiation, but in explaining so much of what was
vaguely already known in an empirical sense, without much if any theoretical insight. Thus the
principles of fractionation itself, the distinction between ‘early’ and ‘late’ reactions in terms of their
dependence on the fractionation scheme, the calculation of iso-effective regimens with different
fraction sizes (the Withers’ formula), the reduction in cell-killing at very low doserate and its
implications for brachytherapy, all of these aspects and more are today immeasurably better
understood as a result of the L-Q model.
But knowing how many/what fraction of cells are killed by (uniform) doses, or how to change the
fraction size safely, doesn’t get one nearly far enough in this age of Conformal Therapy. What
Conformal Therapy also needs is a RADIOBIOLOGY of NORMAL TISSUES which applies to dose
distributions and can tell us, therefore, what happens to the tolerance of an organ if we reduce the
irradiated volume, or more precisely, change the dose distribution (and also, naturally, how
fractionation and dose distributions are related). Normal tissues, organs-at-risk (OARs) or whatever
one calls them, are as good as NEVER irradiated by uniform doses (even though tumours might be,
though even here in the IMRT/stereotactic era this is less and less the case). Models, whether one
calls them ‘biological’, ‘biophysical’, ‘biomathematical’ or anything else, which can quanitfy with
reasonable certainty (and with quantifiable uncertainty/confidence limits)
i)
and
ii)
the probability of (local) tumour control - TCP
the probability of a given type of complication is as a function of the dose distribution and
fraction sizes - NTCP
usher in the age of Conformal Radiobiology.
NTCP models
Underlying all NTCP models is the volume effect, which is central to how normal organs behave in
response to inhomogeneous distributions of radiation and which therefore, has important implications
for the design of treatment strategies. In organs with a small volume effect, it may be advantageous to
use many fields in order to distribute the dose over a larger volume and thereby reduce the peak dose
in the organ. Conversely, in parallel organs, it would be better to use a small number of fields to
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49th AAPM Annual Meeting, Minneapolis, 22-26 July 2007
keep fractional damage below the organ’s functional reserve. Intensity modulation may prove useful in
adjusting the volumetric distribution to normal organs while maintaining the intended dose to the large
volume. In the future, we may find that organ architecture will play an important role in designing, in
addition to evaluating treatment plans (Kutcher et al. 1994).
In spite of their importance, NTCP values should be used with caution when evaluating treatment
plans, since the models suffer from a number of problems (Glatstein 2001). To begin with, they are
generally quite crude and hardly even try to represent the multiple and interrelated toxicities observed
in clinical practice (Deasy et al. 2002). In this respect, modelling of NTCP is much more complex than
TCP because of the large and complex taxonomy of treatment toxicities (Niemierko and Goitein
1993b; Withers and Taylor 1993; Glatstein 2001; Schultheiss 2001; Travis 2001; Yorke 2001; Deasy et
al. 2002). And even when clinical responses are reduced to the barest essentials, perhaps to one or a
few critical endpoints, the paucity of clinical data and their large uncertainties makes it difficult to rely
on the calculated complication probabilities (Deasy et al. 2001; Glatstein 2001). These uncertainties
can lead not only to large variations in the absolute values of the calculated NTCPs (Lebesque et al.
1995) but can also affect the relative ranking of candidate treatment plans. However, this situation is
changing as subsequent lectures will make clear; the increased use of 3D planning by many
institutions is beginning to provide large databases of three-dimensional dose distributions potentially
correlated with clinical endpoints. Although the endpoints characterizing clinical complications, as is
well known, are difficult to define and the data are painstaking to collect, there is nevertheless a
continuing accumulation of such data. There are also indications that the pooling of clinical data and
the ability to share dose distributions between institutions electronically will provide databases that are
more robust where models can be refined (see the lecture by John Fenwick and also Kwa et al.
1998b; Deasy et al. 2003; Rancati et al. 2003; El Naqa et al. 2006). Some consensus may then be
reached on the models and their parameters, at least for some important organs and endpoints, e.g.
Seppenwoolde et al. (2003) in the case of radiation pneumonitis in lung and Rancati et al. (2004) in the
case of proctitis in the rectum. As a complement to the lectures which follow by Ellen Yorke and Marco
Schwarz, excellent summaries of the status of the NTCP models themselves and their application to a
number of organs/endpoints can be found in Ten Haken (2001) and in Cattaneo et al. (2001). In the
meantime, tools (such as BIOPLAN - Sanchez-Nieto and Nahum 2000 and that described in Warketin
et al. 2004) are available which simplify the evaluation of the existing models (of both NTCP and TCP).
The development of further NTCP models is probably unnecessary. However, the generation of more
dose-volume-complication data for the organs and endpoints of most interest is essential in order to
derive more reliable values of the parameters for the currently established models such as the L–K–B
(see lectures by Ellen Yorke) and Relative Seriality (Källman et al 1992) formalisms. It is now
imperative to start using the models and their associated parameters to make predictions of
complications and to compare these predictions with the observed complication rates (e.g. Gagliardi et
al. 2000). Only in this way will NTCP models come to be used with confidence in the clinic (see the
lecture by Philip Mayles). Furthermore, for those organs/endpoints for which a reasonable amount of
reliable dose-volume-complication data already exists (e.g. radiation pneumonitis in the lung; proctitis
in the rectum; RILD in the liver), one can start optimizing treatment plans based on the calculated
NTCP values. Two of the forms that this active use of NTCP modelling could take are
1.
Start with a relative-dose treatment plan arrived at using, for example, dose-based criteria
(e.g. PTV within 95–105% of Dpresc, V90% of OAR < 80% of Dpresc) and then adjust Dpresc
until NTCPOAR is equal to a value specified in the local clinical protocol, e.g. NTCPproctitis = 3%
(Nahum and Tait 1992; Ten Haken et al. 1993; McGinn et al. 1998; Sanchez-Nieto et al.
2001a).
2.
Use NTCP and TCP as part of the objective function in the (computer) optimisation/inverseplanning process, thus allowing the mathematical and radiobiological properties of the models
to drive the search for the optimum plan (e.g. Peñagarícano et al. 2005; Hoffmann et al.
2006).
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49th AAPM Annual Meeting, Minneapolis, 22-26 July 2007
Furthermore, it should not be forgotten that other variables such as fraction size, clonogen proliferation
rate and the patient’s performance status should ideally be incorporated into both the biological
models and the optimisation process (Glatstein 2001; Bentzen 2004; Fowler et al. 2004; Nahum and
Bentzen 2004).
Low-dose hypersensitivity (LDHRS) may also play an important role in our understanding of how
certain complications depend on dose and volume. One consequence of factoring LDHRS into NTCP
models, via a modification of the L-Q expression below around 0.6 Gy, will be an increased
contribution to overall NTCP from those volumes of normal tissue receiving doses well below the
tumour dose, i.e. at the 20% and lower isodoses. A significant increase in such volumes through the
use of rotational techniques such as tomotherapy or many-field IMRT may be undesirable for certain
organs and endpoints in which the killing of cells exhibiting LDHRS play a significant role in causing
the complication.
The question of cancer induction by radiotherapy (see the lecture by Geoff Lawrence) should also be
mentioned. Concerns have been raised (Glatstein 2002; Hall and Wuu 2003) that certain modern
conformal techniques such as (many-field) IMRT, IMAT and tomotherapy may increase cancer
induction due to the increase in volumes irradiated at low doses compared to non-IMRT few-field
techniques and especially compared to the use of proton beams. Hitherto cancer induction has not
been classed as a complication due to the presumed low or extremely low frequency of occurrence.
Models of (second) cancer induction probability (SCIP) which take into account details of the dose
distribution in different organs and also the patient’s age and general prognosis (Lindsay et al. 2001)
would be desirable additions to the collection of tools for predicting the clinical effect of radiotherapy.
Sachs and Brenner (2005) have produced a very useful analysis from which such SCIP models can
now be developed.
ACKNOWLEDGEMENTS
I should like to acknowledge fruitful collaborations on radiobiological modelling with John
Fenwick, Philip Mayles, Alison Scott, Zafar Malik, Chinnamani Eswar, Isabel Syndikus and
John Littler at CCO and also Mauro Iori (Reggio Emilia), Giovanna Gagliardi (Stockholm),
Beatriz Sanchez-Nieto (Santiago, Chile), Francesca Buffa (Gray lab, Northwood and Oxford,
UK), Don Chapman (formerly Fox Chase Cancer Center, Philadelphia) and colleagues at
RaySearch Laboratories AB (Stockholm).
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