An ion-independent phenomenological relative biological effectiveness (RBE) model for proton therapy

TL;DR

This paper proposes a simple, ion-independent RBE model for proton therapy based on beam quantity Q, which predicts RBE effectively across various ions and reduces uncertainty in treatment planning.

Contribution

Introduces a linear, ion-independent RBE model using beam quantity Q, trained on diverse ion data, improving prediction and reducing biological uncertainty in proton therapy.

Findings

Q model's parameters are consistent across ions.

Q model predicts proton RBE comparable to existing models.

Using carbon ion data can enhance proton RBE predictions.

Abstract

Background: A relative biological effectiveness (RBE) of 1.1 is used for proton therapy though clinical evidence of varying RBE was raised. Clinical studies on RBE variability have been conducted for decades for carbon radiation, which could advance the understanding of the clinical proton RBE given an ion-independent RBE model. In this work, such a model, linear and simple, using the beam quantity Q = Z^2/E (Z = ion charge, E = kinetic energy per nucleon) was tested and compared to the commonly used, proton-specific and linear energy transfer (LET) based Wedenberg RBE model. Material and methods: The Wedenberg and Q models, both predicting RBEmax and RBEmin (i.e., RBE at vanishing and very high dose, respectively), are compared in terms of ion-dependence and prediction power. An experimental in-vitro data ensemble covering 115 publications for various ions was used as dataset. Results: The model parameter of the Q model was observed to be similar for different ions (in contrast to LET). The Q model was trained without any prior knowledge of proton data. For proton RBE, the differences between experimental data and corresponding predictions of the Wedenberg or the Q model were highly comparable. Conclusions: A simple linear RBE model using Q instead of LET was proposed and tested to be able to predict proton RBE using model parameter trained based on only RBE data of other particles in a clinical proton energy range for a large in-vitro dataset. Adding (pre)clinical knowledge from carbon ion therapy may, therefore, reduce the dominating biological uncertainty in proton RBE modelling. This would translate in reduced RBE related uncertainty in proton therapy treatment planning.