Clinical Applications of RBE in Carbon Ion Radiation Therapy: Current Status and Consensus Statements.
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- Record sourced from PubMed, PMID 42526654.
- Also identified by DOI 10.1016/j.ijrobp.2026.07.023.
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Abstract
The exploitation of the high relative biological effectiveness (RBE) of carbon ions is one of the major rationales for their use as a radiation therapy modality. As the RBE depends on many physical and biological factors, biophysical models are used to compute it for the complex radiation fields used in clinical settings. However, the models currently applied in clinic or used to interpret clinical results make different RBE predictions. This creates difficulties for direct comparability of RBE-weighted doses delivered and reported within different approaches. Additional conventions on how these models are applied also differ and further complicate the comparison. Consequently, it is crucial to understand the impact of RBE modelling on the delivered absorbed doses and the reported RBE-weighted doses. Translation concepts between dose prescription systems, i.e. the models and the context in which they are used, are needed to exchange treatment protocols between centers with different planning methods and to establish joint clinical studies or meta studies. While many of these problems are solved for specific cases, a broad perspective is lacking on how to transparently proceed with multiple RBE models and corresponding concepts of RBE-weighted dose. This publication aims to (i) raise awareness of the problem, (ii) demonstrate the impact of different models used for RBE predictions, (iii) provide information on how RBE currently is accounted for, and (iv) give an overview of approaches towards the translation of doses. Along this route, we provide a number of expert consensus statements agreed on by all authors, which give insights on the complexity in understanding and comparing different dose prescription systems. Despite this complexity, transforming treatment plans between any two systems is feasible, opening up to novel planning strategies considering multiple models and paving the way towards multi-institutional clinical studies.