Clinical consequences of relative biological effectiveness variations in proton radiotherapy of the prostate, brain and liver

Proton relative biological effectiveness (RBE) is known to depend on the (alpha/beta)(x) of irradiated tissues, with evidence of similar to 60% variation over (alpha/beta)(x) values from 1-10 Gy. The range of (alpha/beta)(x) values reported for prostate tumors (1.2-5.0 Gy), brain tumors (10-15 Gy) and liver tumors (13-17 Gy) imply that the proton RBE for these tissues could vary significantly compared to the commonly used generic value of 1.1. Our aim is to evaluate the impact of this uncertainty on the proton dose in Gy(RBE) absorbed in normal and tumor tissues. This evaluation was performed for standard and hypofractionated regimens. RBE-weighted total dose (RWTD) distributions for 15 patients (five prostate tumors, five brain tumors and five liver tumors) were calculated using an in-house developed RBE model as a function of dose, dose-averaged linear energy transfer (LETd) and (alpha/beta)(x). Variations of the dose-volume histograms (DVHs) for the gross tumor volume (GTV) and the organs at risk due to changes of (alpha/beta)(x) and fractionation regimen were calculated and the RWTD received by 10% and 90% of the organ volume reported. The goodness of the plan, bearing the uncertainties, was then evaluated compared to the delivered plan, which considers a constant RBE of 1.1. For standard fractionated regimens, the prostate tumors, liver tumors and all critical structures in the brain showed typically larger RBE values than 1.1. However, in hypofractionated regimens lower values of RBE than 1.1 were observed in most cases. Based on DVH analysis we found that the RBE variations were clinically significant in particular for the prostate GTV and the critical structures in the brain. Despite the uncertainties in the biological input parameters when estimating RBE values, the results show that the use of a variable RBE with dose, LETd and (alpha/beta)(x) could help to further optimize the target dose in proton treatment planning. Most importantly, this study shows that the consideration of RBE variations could influence the comparison of proton and photon treatments in clinical trials, in particular in the case of the prostate

Mechanistic Modelling of DNA Repair and Cellular Survival Following Radiation-Induced DNA Damage

Characterising and predicting the effects of ionising radiation on cells remains challenging, with the lack of robust models of the underlying mechanism of radiation responses providing a significant limitation to the development of personalised radiotherapy. In this paper we present a mechanistic model of cellular response to radiation that incorporates the kinetics of different DNA repair processes, the spatial distribution of double strand breaks and the resulting probability and severity of misrepair. This model enables predictions to be made of a range of key biological endpoints (DNA repair kinetics, chromosome aberration and mutation formation, survival) across a range of cell types based on a set of 11 mechanistic fitting parameters that are common across all cells. Applying this model to cellular survival showed its capacity to stratify the radiosensitivity of cells based on aspects of their phenotype and experimental conditions such as cell cycle phase and plating delay (correlation between modelled and observed Mean Inactivation Doses R(2) > 0.9). By explicitly incorporating underlying mechanistic factors, this model can integrate knowledge from a wide range of biological studies to provide robust predictions and may act as a foundation for future calculations of individualised radiosensitivity

Uncertainties and correction methods when modeling passive scattering proton therapy treatment heads with Monte Carlo

Monte Carlo models of proton therapy treatment heads are being used to improve beam delivery systems and to calculate the radiation field for patient dose calculations. The achievable accuracy of the model depends on the exact knowledge of the treatment head geometry and time structure, the material characteristics, and the underlying physics. This work aimed at studying the uncertainties in treatment head simulations for passive scattering proton therapy. The sensitivities of spread-out Bragg peak (SOBP) dose distributions on material densities, mean ionization potentials, initial proton beam energy spread and spot size were investigated. An improved understanding of the nature of these parameters may help to improve agreement between calculated and measured SOBP dose distributions and to ensure that the range, modulation width, and uniformity are within clinical tolerance levels. Furthermore, we present a method to make small corrections to the uniformity of spread-out Bragg peaks by utilizing the time structure of the beam delivery. In addition, we re-commissioned the models of the two proton treatment heads located at our facility using the aforementioned correction methods presented in this paper

Evaluation of permanent alopecia in pediatric medulloblastoma patients treated with proton radiation

Background: To precisely calculate skin dose and thus to evaluate the relationship between the skin dose and permanent alopecia for pediatric medulloblastoma patients treated with proton beams. Methods: The dosimetry and alopecia outcomes of 12 children with medulloblastoma (ages 4-15 years) comprise the study cohort. Permanent alopecia was assessed and graded after completion of the entire therapy. Skin threshold doses of permanent alopecia were calculated based on the skin dose from the craniospinal irradiation (CSI) plan using the concept of generalized equivalent uniform dose (gEUD) and accounting for chemotherapy intensity. Monte Carlo simulations were employed to accurately assess uncertainties due to beam range prediction and secondary particles. Results: Increasing the dose of the CSI field or the dose given by the boost field to the posterior fossa increased total skin dose delivered in that region. It was found that permanent alopecia could be correlated with CSI dose with a threshold of about 21 Gy (relative biological effectiveness, RBE) with high dose chemotherapy and 30 Gy (RBE) with conventional chemotherapy. Conclusions: Our results based on 12 patients provide a relationship between the skin dose and permanent alopecia for pediatric medulloblastoma patients treated with protons. The alopecia risk as assessed with gEUD could be predicted based on the treatment plan information

Monte Carlo study of the potential reduction in out-of-field dose using a patient-specific aperture in pencil beam scanning proton therapy

This study is aimed at identifying the potential benefits of using a patientspecific aperture in proton beam scanning. For this purpose, an accurate Monte Carlo model of the pencil beam scanning (PBS) proton therapy (PT) treatment head at Massachusetts General Hospital (MGH) was developed based on an existing model of the passive double-scattering (DS) system. The Monte Carlo code specifies the treatment head at MGH with sub-millimeter accuracy. The code was configured based on the results of experimental measurements performed at MGH. This model was then used to compare out-of-field doses in simulated DS treatments and PBS treatments. For the conditions explored, the penumbra in PBS is wider than in DS, leading to higher absorbed doses and equivalent doses adjacent to the primary field edge. For lateral distances greater than 10 cm from the field edge, the doses in PBS appear to be lower than those observed for DS. We found that placing a patient-specific aperture at nozzle exit during PBS treatments can potentially reduce doses lateral to the primary radiation field by over an order of magnitude. In conclusion, using a patient-specific aperture has the potential to further improve the normal tissue sparing capabilities of PBS

Detector Simulation Challenges for Future Accelerator Experiments

Detector simulation is a key component for studies on prospective future high-energy colliders, the design, optimization, testing and operation of particle physics experiments, and the analysis of the data collected to perform physics measurements. This review starts from the current state of the art technology applied to detector simulation in high-energy physics and elaborates on the evolution of software tools developed to address the challenges posed by future accelerator programs beyond the HL-LHC era, into the 2030–2050 period. New accelerator, detector, and computing technologies set the stage for an exercise in how detector simulation will serve the needs of the high-energy physics programs of the mid 21st century, and its potential impact on other research domains

Detector simulation challenges for future accelerator experiments

Detector simulation is a key component for studies on prospective future high-energy colliders, the design, optimization, testing and operation of particle physics experiments, and the analysis of the data collected to perform physics measurements. This review starts from the current state of the art technology applied to detector simulation in high-energy physics and elaborates on the evolution of software tools developed to address the challenges posed by future accelerator programs beyond the HL-LHC era, into the 2030–2050 period. New accelerator, detector, and computing technologies set the stage for an exercise in how detector simulation will serve the needs of the high-energy physics programs of the mid 21st century, and its potential impact on other research domains