A Monte Carlo based radiation response modelling framework to assess variability of clinical RBE in proton therapy.

The clinical implementation of a variable relative biological effectiveness (RBE) in proton therapy is currently controversially discussed. Initial clinical evidence indicates a variable proton RBE, which needs to be verified. In this study, a radiation response modelling framework for assessing clinical RBE variability is established. It was applied to four selected glioma patients (grade III) treated with adjuvant radio(chemo)therapy and who developed late morphological image changes on T1-weighted contrast-enhanced (T1w-CE) magnetic resonance (MR) images within approximately two years of recurrence-free follow-up. The image changes were correlated voxelwise with dose and linear energy transfer (LET) values using univariable and multivariable logistic regression analysis. The regression models were evaluated by the area-under-the-curve (AUC) method performing a leave-one-out cross validation. The tolerance dose TD at which 50% of patient voxels experienced toxicity was interpolated from the models. A Monte Carlo (MC) model was developed to simulate dose and LET distributions, which includes variance reduction (VR) techniques to decrease computation time. Its reliability and accuracy were evaluated based on dose calculations of the clinical treatment planning system (TPS) as well as absolute dose measurements performed in the patient specific quality assurance. Morphological image changes were related to a combination of dose and LET. The multivariable models revealed cross-validated AUC values of up to 0.88. The interpolated TD curves decreased with increasing LET indicating an increase in biological effectiveness. The MC model reliably predicted average TPS dose within the clinical target volume as well as absolute water phantom dose measurements within 2% accuracy using dedicated VR settings. The observed correlation of dose and LET with late brain tissue damage suggests considering RBE variability for predicting chronic radiation-induced brain toxicities. The MC model simulates radiation fields in patients precisely and time-efficiently. Hence, this study encourages and enables in-depth patient evaluation to assess the variability of clinical proton RBE.