An updated variable RBE model for proton therapy.

Objective.While a constant relative biological effectiveness (RBE) of 1.1 forms the basis for clinical proton therapy, variable RBE models are increasingly being used in plan evaluation. However, there is substantial variation across RBE models, and several newin vitrodatasets have not yet been included in the existing models. In this study, an updatedin vitroproton RBE database was collected and used to examine current RBE model assumptions, and to propose an up-to-date RBE model as a tool for evaluating RBE effects in clinical settings.Approach.A proton database (471 data points) was collected from the literature, almost twice the size of the previously largest model database. Each data point included linear-quadratic model parameters and linear energy transfer (LET). Statistical analyses were performed to test the validity of commonly applied assumptions of phenomenological RBE models, and new model functions were proposed forRBEmaxandRBEmin(RBE at the lower and upper dose limits). Previously published models were refitted to the database and compared to the new model in terms of model performance and RBE estimates.Main results.The statistical analysis indicated that the intercept of theRBEmaxfunction should be a free fitting parameter and RBE estimates were clearly higher for models with free intercept.RBEminincreased with increasing LET, while a dependency ofRBEminon the reference radiation fractionation sensitivity (α/ßx) did not significantly improve model performance. Evaluating the models, the new model gave overall lowest RMSE and highest R2 score. RBE estimates in the distal part of a spread-out-Bragg-peak in water (α/ßx= 2.1 Gy) were 1.24-1.51 for original models, 1.25-1.49 for refits and 1.42 for the new model.Significance.An updated RBE model based on the currently largest database among published phenomenological models was proposed. Overall, the new model showed better performance compared to refitted published RBE models.

An experimental validation of a filtering approach for prompt gamma prediction in a research proton treatment planning system.

Objective.Prompt gamma (PG) radiation generated from nuclear reactions between protons and tissue nuclei can be employed for range verification in proton therapy. A typical clinical workflow for PG range verification compares the detected PG profile with a predicted one. Recently, a novel analytical PG prediction algorithm based on the so-called filtering formalism has been proposed and implemented in a research version of RayStation (RaySearch Laboratories AB), which is a widely adopted treatment planning system. This work validates the performance of the filtering PG prediction approach.Approach.The said algorithm is validated against experimental data and benchmarked with another well-established PG prediction algorithm implemented in a MATLAB-based software REGGUI. Furthermore, a new workflow based on several PG profile quality criteria and analytical methods is proposed for data selection. The workflow also calculates sensitivity and specificity information, which can help practitioners to decide on irradiation course interruption during treatment and monitor spot selection at the treatment planning stage. With the proposed workflow, the comparison can be performed on a limited number of selected high-quality irradiation spots without neighbouring-spot aggregation.Main results.The mean shifts between the experimental data and the predicted PG detection (PGD) profiles (ΔPGD) by the two algorithms are estimated to be1.5±2.1mm and-0.6±2.2mm for the filtering and REGGUI prediction methods, respectively. The ΔPGD difference between two algorithms is observed to be consistent with the beam model difference within uncertainty. However, the filtering approach requires a much shorter computation time compared to the REGGUI approach.Significance.The novel filtering approach is successfully validated against experimental data and another widely used PG prediction algorithm. The workflow designed in this work selects spots with high-quality PGD shift calculation results, and performs sensitivity and specificity analyses to assist clinical decisions.