Verification of the Method Used in Clinical Estimation of Alpha/beta Ratio, Using a Model Simulation Including Proliferation and Hypoxic Effects
J Jeong*, J O Deasy, Memorial Sloan Kettering Cancer Center, New York, NYSU-D-137-7 Sunday 2:05PM - 3:00PM Room: 137
Purpose: We hypothesize that the interplay between cell kill, proliferation, and hypoxia make the traditional interpretation of clinically-derived α/β ratios problematic. In this study, we simulate resulting α/β ratios given a recently developed tumor-response simulation model that includes the interplay between hypoxia and proliferation, as well as the resulting tumor reoxygenation during a course of radiotherapy.
Methods: Using a state-driven tumor response model, various typical fractionation schemes were simulated until the same level of tumor control probability was achieved (50%). Depending on the inclusion of effects, four different cases were evaluated: (1) neither proliferation nor hypoxia; (2) proliferation only; (3) hypoxia only; and (4) both proliferation and hypoxia. We judge the full model to be most realistic. The resulting α/β ratio was estimated based on the Withers isoeffect equation (as a negative slope of linear regression line in the plot of TD₅₀ vs. TD₅₀xd) and compared with the ratio in model input, α/β(model) (=6.63).
Results: Without any other effects (case 1), the model estimated an α/β ratio, α/β(est), that equals to the model assigned value. Including proliferation (case 2), smaller fractional doses become less effective due to increased repopulation, resulting in a reduced α/β(est). For case 3, including hypoxia only, longer schedules with smaller fraction sizes become more effective due to reoxygenation and resulted in unrealistic negative α/β(est). Interestingly, in case 4, the two effects, proliferation and hypoxia, approximately cancel each other and unmask the underlying cell-kill characteristics, resulting in an α/β(est) comparable to the model input value α/β(model).
Conclusion: Simulations show that the estimated α/β ratio is dependent on underlying radiobiological effects (including proliferation and hypoxia), and the resulting model estimation may not be an accurate reflection of cell-kill sensitivity alone, which implies the clinical estimation might be biased if other effects are not properly controlled.
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