Linear energy transfer dependent variation in viability and proliferation along the Bragg peak curve in sarcoma and normal tissue cells.

OBJECTIVE: The energy deposition of photons and protons differs. It depends on the position in the proton Bragg peak (BP) and the linear energy transfer (LET) leading to a variable relative biological effectiveness (RBE). Here, we investigate LET dependent alterations on metabolic viability and proliferation of sarcoma and endothelium cell lines following proton irradiation in comparison to photon exposure. Approach: Using a multi-step range shifter (MSRS), each column of a 96-well plate was positioned in a different depth along four BP curves with increasing intensities. The high-throughput experimental setup covers dose, LET, and RBE changes seen in a treatment field. Photon irradiation was performed to calculate the RBE along the BP curve. Two biological information out of one experiment were extracted allowing a correlation between metabolic viability and proliferation of the cells. Main results: The metabolic viability and cellular proliferation were column-wise altered showing a depth-dose profile. Endothelium cell viability recovers within 96 h post BP irradiation while sarcoma cell viability remains reduced. Highest RBE values were observed at the BP distal fall-off regarding proliferation of the sarcoma and endothelial cells. Significance: The high-throughput experimental setup introduced here I) covers dose, LET, and RBE changes seen in a treatment field, II) measures short-term effects within 48 h to 96 h post irradiation, and III) can additionally be transferred to various cell types without time consuming experimental adaptations. Traditionally, RBE values are calculated from clonogenic cell survival. Measured RBE profiles strongly depend on physical characteristics such as dose and LET and biological characteristics for example cell type and time point. Metabolic viability and proliferation proofed to be in a similar effect range compared to clonogenic survival results. Based on limited data of combined irradiation with doxorubicin, future experiments will test combined treatment with systemic therapies applied in clinics e.g. cyclin-dependent inhibitors. .

A deep-learning-based surrogate model for Monte-Carlo simulations of the linear energy transfer in primary brain tumor patients treated with proton-beam radiotherapy.

Objective.This study explores the use of neural networks (NNs) as surrogate models for Monte-Carlo (MC) simulations in predicting the dose-averaged linear energy transfer (LETd) of protons in proton-beam therapy based on the planned dose distribution and patient anatomy in the form of computed tomography (CT) images. As LETdis associated with variability in the relative biological effectiveness (RBE) of protons, we also evaluate the implications of using NN predictions for normal tissue complication probability (NTCP) models within a variable-RBE context.Approach.The predictive performance of three-dimensional NN architectures was evaluated using five-fold cross-validation on a cohort of brain tumor patients (n= 151). The best-performing model was identified and externally validated on patients from a different center (n= 107). LETdpredictions were compared to MC-simulated results in clinically relevant regions of interest. We assessed the impact on NTCP models by leveraging LETdpredictions to derive RBE-weighted doses, using the Wedenberg RBE model.Main results.We found NNs based solely on the planned dose distribution, i.e. without additional usage of CT images, can approximate MC-based LETddistributions. Root mean squared errors (RMSE) for the median LETdwithin the brain, brainstem, CTV, chiasm, lacrimal glands (ipsilateral/contralateral) and optic nerves (ipsilateral/contralateral) were 0.36, 0.87, 0.31, 0.73, 0.68, 1.04, 0.69 and 1.24 keV µm-1, respectively. Although model predictions showed statistically significant differences from MC outputs, these did not result in substantial changes in NTCP predictions, with RMSEs of at most 3.2 percentage points.Significance.The ability of NNs to predict LETdbased solely on planned dose distributions suggests a viable alternative to compute-intensive MC simulations in a variable-RBE setting. This is particularly useful in scenarios where MC simulation data are unavailable, facilitating resource-constrained proton therapy treatment planning, retrospective patient data analysis and further investigations on the variability of proton RBE.

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.