A TCP-NTCP estimation module using DVHs and known radiobiological models and parameter sets

Abstract

Radiotherapy treatment plan evaluation relies on an implicit estimation of the tumor control probability (TCP) and normal tissue complication probability (NTCP) arising from a given dose distribution. A potential application of radiobiological modeling to radiotherapy is the ranking of treatment plans via a more explicit determination of TCP and NTCP values. Although the limited predictive capabilities of current radiobiological models prevent their use as a primary evaluative tool, radiobiological modeling predictions may still be a valuable complement to clinical experience. A convenient computational module has been developed for estimating the TCP and the NTCP arising from a dose distribution calculated by a treatment planning system, and characterized by differential (frequency) dose-volume histograms (DDVHs). The radiobiological models included in the module are sigmoidal dose response and Critical Volume NTCP models, a Poisson TCP model, and a TCP model incorporating radiobiological parameters describing linear-quadratic cell kill and repopulation. A number of sets of parameter values for the different models have been gathered in databases. The estimated parameters characterize the radiation response of several different normal tissues and tumor types. The system also allows input and storage of parameters by the user, which is particularly useful because of the rapidly increasing number of parameter estimates available in the literature. Potential applications of the system include the following: comparing radiobiological predictions of outcome for different treatment plans or types of treatment; comparing the number of observed outcomes for a cohort of patient DVHs to the predicted number of outcomes based on different models/parameter sets; and testing of the sensitivity of model predictions to uncertainties in the parameter values. The module thus helps to amalgamate and make more accessible current radiobiological modeling knowledge, and may serve as a useful aid in the prospective and retrospective analysis of radiotherapy treatment plans.

Figure 1

Example output from the program for a case where the user has chosen to display and analyze DDVHs for the bladder (DVH #2), rectum (DVH #3), spinal cord (DVH #4), and prostate tumor volumes (DVH #1).

Figure 2

Program output after analysis of lung DVHs generated from the retrospective treatment planning of a cohort of 16 breast cancer patients using two different treatment techniques. DVH #1 (blue line) is the cumulative DVH (averaged over the 16 patients) for a “5‐field” technique, while DVH #2 (red line) is the corresponding DVH for a “wide‐tangent” technique. For each set of DVHs, radiobiological model predictions of the mean NTCP are displayed for a number of different parameter sets available in the literature.

Figure 3

Program output displaying the distribution of the number of complications predicted from the DVHs for the mandible for a group of 10 patients based on two sets of radiobiological predictions based on the Emami et al. ⁽¹⁶⁾ data. (a) SDR model, Burman et al. ⁽¹⁷⁾ parameters; (b) “population” CV model, Stavrev et al. ⁽⁷⁾ parameters.

Figure 4

Program output displaying TCP predictions for the same DDVH (i.e., DVHs #1 to #4 are the same) for four sets of user‐specified parameters: (i) $\alpha =0.30\hspace{0.17em}{\text{Gy}}^{-1},\beta =0.03\hspace{0.17em}{\text{Gy}}^{-2},N={10}^6,\lambda =0.05\hspace{0.17em}{\text{days}}^{-1},n=25$ fractions; (ii) same as (i), but with slightly decreased cellular radiosensitivity, $\alpha =0.27\hspace{0.17em}{\text{Gy}}^{-1},\beta =0.027\hspace{0.17em}{\text{Gy}}^{-2}$; (iii) same as (i), but with the rate of repopulation doubled, $\lambda =0.10\hspace{0.17em}{\text{days}}^{-1}$; (iv) $\alpha =0.17\hspace{0.17em}{\text{Gy}}^{-1},\beta =0.017\hspace{0.17em}{\text{Gy}}^{-2},N={10}^3,\lambda =0.05\hspace{0.17em}{\text{days}}^{-1},n=25$ fractions.