Variable-RBE-induced NTCP predictions for various side-effects following proton therapy for brain tumors - Identification of high-risk patients and risk mitigation.

Background And Purpose: Disregarding the increase of relative biological effectiveness (RBE) may raise the risk of acute and late adverse events after proton beam therapy (PBT). This study aims to explore the relationship between variable RBE (above 1.1)-induced normal tissue complication probabilities (NTCP) and patient-specific factors, identify patients at high risk of RBE-induced NTCP increase, and assess risk mitigation by incorporating RBE variability into treatment planning.

A deep-learning-based surrogate model for Monte-Carlo simulations of the linear energy transfer in primary brain tumor patients treated with proton-beam radiotherapy.

This study explores the use of neural networks (NNs) as surrogate models for Monte-Carlo (MC) simulations in predicting the dose-averaged linear energy transfer (LET) of protons in proton-beam therapy based on the planned dose distribution and patient anatomy in the form of computed tomography (CT) images. As LETis associated with variability in the relative biological effectiveness (RBE) of protons, we also evaluate the implications of using NN predictions for normal tissue complication probability (NTCP) models within a variable-RBE context.The predictive performance of three-dimensional NN architectures was evaluated using five-fold cross-validation on a cohort of brain tumor patients (= 151).