Room: AAPM ePoster Library
Purpose: in dose delivered during carbon radiotherapy is due largely to uncertainties in relative biological effectiveness (RBE), which is determined using one of several models that typically require extensive Monte Carlo calculations. While RBE by the Microdosimetric Kinetic Model (MKM) can be measured using microdosimeters, there exist no direct means of measuring RBE for other common models, including the Repair Misrepair Fixation (RMF) and Local Effect Model I (LEM). This study investigates a means of estimating RBE by common models with a uniform, microdosimetric measurement, to allow both measurement-based validation of model implementation and comparison of modeled RBE results within a common framework.
Methods: Carlo (Geant4) was used to simulate 165 monoenergetic and SOBP carbon beams incident on a water phantom. From these, input parameters for each RBE model (microdosimetric spectra, double strand break yield, kinetic energy spectra, dose fragment contributions) were calculated and input into the linear-quadratic formula to give RBE according to each model definition. To estimate RBE in a microdosimetric framework, these various input parameters were fit as a function of microdosimetric values. For LEM, a method of dividing the microdosimetric spectra by lineal energy was additionally devised to create an independent fit for each highly contributing ion species. The percent difference between RBE calculated using the true input parameters versus that calculated using the measurement-based fit was used to quantify estimation error.
Results: estimated by microdosimetric parameters was compared to the fully determined RBE using complete model inputs and found to be within 5% accuracy in 100%, 97%, and 93% of 26,000 data points assessed for MKM, RMF and LEM, respectively.
Conclusion: true RBE has extensive associated uncertainty, modeled RBE can be estimated with reasonable accuracy in a common, measurement-based framework. This system can be applied clinically to enhance model intercomparisons and verify institutional model implementation.