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 TCP (tumor control probability) and NTCP (normal tissue complication probability) 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 the Lyman and the Critical Volume NTCP models, a Poisson TCP model, and a TCP model incorporating linear-quadratic cell kill and a formalism recently developed by Zaider and Minerbo to account for repopulation. A number of different sets of parameters 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: 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; 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.