Benchmarking proton RBE models

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Abstract

To biologically optimise proton therapy, models which can accurately predict variations inproton RBE are essential. Current phenomenological models show large disagreements in RBEpredictions, due to different model assumptions and differences in the data to which they were fit. Inthis work, thirteen RBE models were benchmarked against a comprehensive proton RBE dataset toevaluate predictions when all models are fit using the same data and fitting techniques, and to assessthe statistical robustness of the models.Approach: Model performance was initially evaluated by fitting to the full dataset, and then a cross-validation approach was applied to assess model generalisability and robustness. The impact ofweighting the fit and the choice of biological endpoint (either single or multiple survival levels) wasalso evaluated.Results: Fitting the models to a common dataset reduced differences between their predictions,however significant disagreements remained due to different underlying assumptions. All modelsperformed poorly under cross-validation in the weighted fits, suggesting that some uncertainties onthe experimental data were significantly underestimated, resulting in over-fitting and poorperformance on unseen data. The simplest model, which depends linearly on the LET but has no tissueor dose dependence, performed best for a single survival level. However, when fitting to multiplesurvival levels simultaneously, more complex models with tissue dependence performed better. Allmodels had significant residual uncertainty in their predictions compared to experimental data.Significance: This analysis highlights that poor quality of error estimation on the dose responseparameters introduces substantial uncertainty in model fitting. The significant residual error presentin all approaches illustrates the challenges inherent in fitting to large, heterogeneous datasets and theimportance of robust statistical validation of RBE models.
Original languageEnglish
Article number085022
Number of pages15
JournalPhysics in Medicine and Biology
Volume69
Issue number8
DOIs
Publication statusPublished - 08 Apr 2024

Keywords

  • proton therapy
  • relative biological effectiveness
  • mathematical modelling

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