An ion-independent phenomenological relative biological effectiveness (RBE) model for proton therapy. (September 2022)

• Record Type: Journal Article
• Authors: Tian, Liheng; Hahn, Christian; Lühr, Armin
• Is Part Of: Radiotherapy and oncology. Volume 174(2022)
• Page Start: 69
• Page End: 76
• Publication Date: 2022-09
• Subjects: Proton therapy -- RBE modeling -- Ion therapy -- Radiotherapy; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2022.06.023
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: Beam quality Q has a higher correlation to in vitro RBE data than LET. Q = Z 2 /E is a simple quantity (Z and E being ion charge and kinetic energy per nucleon). Proposed RBE model is simple, linear in Q and observed to be ion independent. Q model predicts measured proton RBE without any pre-knowledge of proton data. Q may support transfer of variable RBE data from heavy-ion to proton therapy. 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.