Quantifying Sensitivity of Carbon RBE Models to Reference Parameter Variations.

Abstract

Models used to calculate the relative biological effectiveness (RBE) of carbon-ion radiotherapy include the microdosimetric kinetic model (MKM), stochastic MKM (SMKM), repair-misrepair-fixation (RMF) model, and local effect model I (LEM). We compared the sensitivities of these models to variations in input biological and reference parameters. We used Monte Carlo simulations of clinically realistic carbon-ion beams incident on a phantom and scored input parameters for RBE models (kinetic energy, microdosimetric spectra, double-strand break yield, and physical dose). We combined data with cell- and model-specific parameters to calculate the linear (α) and quadratic (β) components of the carbon-ion beam, which were used along with the reference α and β values and dose to calculate RBE. Model sensitivity to parameters was quantified by statistically introducing uncertainty into independent parameters and sampling the resultant RBE. To assess histological differences contributing to variations in the RBE, we also used various reference cell lines. We recalculated the RBE using different reported datasets within individual cell lines to compare inter- and intra-cell line variability. The variability introduced by inherent measurement and estimation uncertainty was typically 26% for the microdosimetric models, 25% for the RMF model, and 30% for the LEM at the 1-σ level. The variability across cell lines, which averaged 27% for the microdosimetric models and 2.5% for the RMF model, was similar to the intra-cell line variability in the RBE as calculated with unique datasets for an individual cell line. While the focus is largely on comparing models, the results of this study indicate that the variation in RBE within each model, based solely on reference parameters, is substantial. Our findings indicate that the selection of input parameters is of comparable importance to the choice of cell line and even the RBE model. This study provides insight into model robustness and emphasizes the need for continued computational and in-vitro RBE research.