Proton Relative Biological Effectiveness Research Articles

Beyond a constant proton relative biological effectiveness: A survey of clinical and research perspectives among proton institutions in Europe and the United States.

Although proton relative biological effectiveness (RBE) depends on factors like linear energy transfer (LET), tissue properties, dose, and biological endpoint, a constant RBE of 1.1 is recommended in clinical practice. This study surveys proton institutions to explore activities using functionalities beyond a constant proton RBE. Research versions of RayStation integrate functionalities considering variable proton RBE, LET, proton track-ends, and dirty dose. A survey of 19 institutions in Europe and the United States, with these functionalities available, investigated clinical adoption and research prospects using a 25-question online questionnaire. Of the 16 institutions that responded (84% response rate), 13 were clinically active. These clinical institutions prescribe RBE=1.1 but also employ planning strategies centered around special beam arrangements to address potentially enhanced RBE effects in serially structured organs at risk (OARs). Clinical plan evaluation encompassed beam angles/spot position (69%), dose-averaged LET (LETd) (46%), and variable RBE distributions (38%). High ratings (discrete scale: 1-5) were reported for the research functionalities using linear LETd-RBE models, LETd, track-end frequency and dirty dose (averages: 4.0-4.8), while LQ-based phenomenological RBE models dependent on LETd scored lower for optimization (average: 2.2) but congruent for evaluation (average: 4.1). The institutions preferred LET reported as LETd (94%), computed in unit-density water (56%), for all protons (63%), and lean toward LETd-based phenomenological RBE models for clinical use (>50%). Proton institutions recognize RBE variability but adhere to a constant RBE while actively mitigating potential enhancements, particularly in serially structured OARs. Research efforts focus on planning techniques that utilize functionalities beyond a constant RBE, emphasizing standardized LET and RBE calculations to facilitate their adoption in clinical practice and improve clinical data collection. LETd calculated in unit-density water for all protons as input to adaptable phenomenological RBE models was the most suggested approach, aligning with predominant clinical LET and variable RBE reporting.

Interpreting the biological effects of protons as a function of physical quantity: linear energy transfer or microdosimetric lineal energy spectrum?

The choice of appropriate physical quantities to characterize the biological effects of ionizing radiation has evolved over time coupled with advances in scientific understanding. The basic hypothesis in radiation dosimetry is that the energy deposited by ionizing radiation initiates all the consequences of exposure in a biological sample (e.g., DNA damage, reproductive cell death). Physical quantities defined to characterize energy deposition have included dose, a measure of the mean energy imparted per unit mass of the target, and linear energy transfer (LET), a measure of the mean energy deposition per unit distance that charged particles traverse in a medium. The primary advantage of using the “dose and LET” physical system is its relative simplicity, especially for presenting and recording results. Inclusion of additional information such as the energy spectrum of charged particles renders this approach adequate to describe the biological effects of large dose levels from homogeneous sources. The primary disadvantage of this system is that it does not provide a unique description of the stochastic nature of radiation interactions. We and others have used dose-averaged LET (LETd) as a correlative physical quantity to the relative biological effectiveness (RBE) of proton beams. This approach is based on established experimental findings that proton RBE increases with LETd. However, this approach might not be applicable to intensity-modulated proton therapy or other applications in which the proton energy spectrum is highly heterogeneous. In the current study, we irradiated cancer cells with scanning proton beams with identical LETd (3.4 keV/µm) but arising from two different proton energy/LET spectra (a narrow spectrum in group 1 and a widespread heterogeneous spectrum in group 2). Clonogenic survival after irradiation revealed significant differences in RBE at any cell surviving fraction: e.g., at a surviving fraction of 0.1, the RBE was 0.97 ± 0.03 in group 1 and 1.16 ± 0.04 in group 2 (p≤0.01), validating our hypothesis that LETd alone may not adequately indicate proton RBE. Further analysis showed that microdosimetric spectrum (the probability density function of the stochastic physical quantity lineal energy y) was helpful for interpreting observed differences in biological effects. However, more accurate use of microdosimetric spectrum to quantify RBE requires a cell line–specific mechanistic model.

Proton- compared to X-irradiation leads to more acinar atrophy and greater hyposalivation accompanied by a differential cytokine response.

Proton therapy gives less dose to healthy tissue compared to conventional X-ray therapy, but systematic comparisons of normal tissue responses are lacking. The aim of this study was to investigate late tissue responses in the salivary glands following proton- or X-irradiation of the head and neck in mice. Moreover, we aimed at investigating molecular responses by monitoring the cytokine levels in serum and saliva. Female C57BL/6J mice underwent local fractionated irradiation with protons or X-rays to the maximally tolerated acute level. Saliva and serum were collected before and at different time points after irradiation to assess salivary gland function and cytokine expression. To study late responses in the major salivary glands, histological analyses were performed on tissues collected at day 105 after onset of irradiation. Saliva volume after proton and X-irradiation was significantly lower than for controls and remained reduced at all time points after irradiation. Protons caused reduced saliva production and fewer acinar cells in the submandibular glands compared to X-rays at day 105. X-rays induced a stronger inflammatory cytokine response in saliva compared to protons. This work supports previous preclinical findings and indicate that the relative biological effectiveness of protons in normal tissue might be higher than the commonly used value of 1.1.

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 (LET d ) of protons in proton-beam therapy based on the planned dose distribution and patient anatomy in the form of computed tomography (CT) images. As LET d is 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). LET d predictions were compared to MC-simulated results in clinically relevant regions of interest. We assessed the impact on NTCP models by leveraging LET d predictions 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 LET d distributions. Root mean squared errors (RMSE) for the median LET d within 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 LET d based 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 new in vitro datasets have not yet been included in the existing models. In this study, an updated in vitro proton 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 for RBEmax and RBEmin (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 the RBEmax function should be a free fitting parameter and RBE estimates were clearly higher for models with free intercept. RBEmin increased with increasing LET, while a dependency of RBEmin on the reference radiation fractionation sensitivity ( α/βx ) 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/βx = 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.

Novel radiation quality metrics accounting for proton energy spectra for RBE proton models.

For proton therapy, a relative biological effectiveness (RBE) of 1.1 is widely applied clinically. However, due to abundant evidence of variable RBE in vitro, and as suggested in studies of patient outcomes, RBE might increase by the end of the proton tracks, as described by several proposed variable RBE models. Typically, the dose averaged linear energy transfer ( ) has been used as a radiation quality metric (RQM) for these models. However, the optimal choice of RQM has not been fullyexplored. This study aims to propose novel RQMs that effectively weight protons of different energies, and assess their predictive power for variable RBE in proton therapy. The overall objective is to identify an RQM that better describes the contribution of individual particles to the RBE of protonbeams. High-throughput experimental set-ups of in vitro cell survival studies for proton RBE determination are simulated utilizing the SHIELD-HIT12A Monte Carlo particle transport code. For every data point, the proton energy spectra are simulated, allowing the calculation of novel RQMs by applying different power levels to the spectra of LET or effective ( ) values. A phenomenological linear-quadratic-based RBE model is then applied to the in vitro data, using various RQMs as input variables, and the model performance is evaluated by root-mean-square-error (RMSE) for the logarithm of cell surviving fractions of each datapoint. Increasing the power level, that is, putting an even higher weight on higher LET particles when constructing the RQM is generally associated with an increased model performance, with dose averaged (i.e., dose averaged cubed LET, ) resulting in a RMSE value 0.31, compared to 0.45 for a model based on (linearly weighted) , with similar trends also observed for track averaged and -basedRQMs. The results indicate that improved proton variable RBE models can be constructed assuming a non-linear RBE(LET) relationship for individual protons. If similar trends hold also for an in vitro-environment, variable RBE effects are likely better described by or tracked averaged cubed LET ( ), or corresponding -based RQM, rather than linearly weighted or which is conventionally applied today.

Impact of Relative Biologic Effectiveness for Proton Therapy for Head and Neck and Skull-Base Tumors: A Technical and Clinical Review.

Proton therapy has emerged as a crucial tool in the treatment of head and neck and skull-base cancers, offering advantages over photon therapy in terms of decreasing integral dose and reducing acute and late toxicities, such as dysgeusia, feeding tube dependence, xerostomia, secondary malignancies, and neurocognitive dysfunction. Despite its benefits in dose distribution and biological effectiveness, the application of proton therapy is challenged by uncertainties in its relative biological effectiveness (RBE). Overcoming the challenges related to RBE is key to fully realizing proton therapy's potential, which extends beyond its physical dosimetric properties when compared with photon-based therapies. In this paper, we discuss the clinical significance of RBE within treatment volumes and adjacent serial organs at risk in the management of head and neck and skull-base tumors. We review proton RBE uncertainties and its modeling and explore clinical outcomes. Additionally, we highlight technological advancements and innovations in plan optimization and treatment delivery, including linear energy transfer/RBE optimizations and the development of spot-scanning proton arc therapy. These advancements show promise in harnessing the full capabilities of proton therapy from an academic standpoint, further technological innovations and clinical outcome studies, however, are needed for their integration into routine clinical practice.

Benchmarking proton RBE models

Objective. To biologically optimise proton therapy, models which can accurately predict variations in proton relative biological effectiveness (RBE) are essential. Current phenomenological models show large disagreements in RBE predictions, due to different model assumptions and differences in the data to which they were fit. In this work, thirteen RBE models were benchmarked against a comprehensive proton RBE dataset to evaluate predictions when all models are fit using the same data and fitting techniques, and to assess the 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 of weighting the fit and the choice of biological endpoint (either single or multiple survival levels) was also evaluated. Main results. Fitting the models to a common dataset reduced differences between their predictions, however significant disagreements remained due to different underlying assumptions. All models performed poorly under cross-validation in the weighted fits, suggesting that some uncertainties on the experimental data were significantly underestimated, resulting in over-fitting and poor performance on unseen data. The simplest model, which depends linearly on the LET but has no tissue or dose dependence, performed best for a single survival level. However, when fitting to multiple survival levels simultaneously, more complex models with tissue dependence performed better. All models had significant residual uncertainty in their predictions compared to experimental data. Significance. This analysis highlights that poor quality of error estimation on the dose response parameters introduces substantial uncertainty in model fitting. The significant residual error present in all approaches illustrates the challenges inherent in fitting to large, heterogeneous datasets and the importance of robust statistical validation of RBE models.

Proton relative biological effectiveness for the induction of DNA double strand breaks based on Geant4

Uncertainties in the relative biological effectiveness (RBE) of proton remains a major barrier to the biological optimization of proton therapy. A large amount of experimental data suggest that proton RBE is variable. As an evolving Monte Carlo code toolkit, Geant4-DNA is able to simulate the initial DNA damage caused by particle beams through physical and chemical interactions at the nanometer scale over a short period of time. This contributes to evaluating the radiobiological effects induced by ionizing radiation. Based on the Geant4-DNA toolkit, this study constructed a DNA geometric model containing 6.32Gbp, simulated the relationship between radiochemical yields (G-values) and their corresponding chemical constructors, and calculated a detailed calculation of the sources of damage and the complexity of damage in DNA strand breaks. The damage model constructed in this study can simulate the relative biological effectiveness (RBE) in the proton Bragg peak region. The results indicate that: (1) When the electron energy is below 400 keV, the yield of OH· account for 18.1% to 25.3% of the total water radiolysis yields. (2) Under the influence of histone clearance function, the yield of indirect damage account for over 72.93% of the yield of DNA strand breaks (SBs). When linear energy transfer (LET) increased from 29.79 (keV/μm) to 64.29 (keV/μm), the yield of double strand breaks (DSB) increased from 17.27% to 32.65%. (3) By investigating the effect of proton Bragg peak depth on the yield of direct DSB (DSBdirect) and total DSB (DSBtotal), the RBEDSBtot and RBEDSBdir levels of cells show that the RBE value of protons reaches 2.2 in the Bragg peak region.

Voxel-wise dose rate calculation in clinical pencil beam scanning proton therapy

Objective. Clinical outcomes after proton therapy have shown some variability that is not fully understood. Different approaches have been suggested to explain the biological outcome, but none has yet provided a comprehensive and satisfactory rationale for observed toxicities. The relatively recent transition from passive scattering (PS) to pencil beam scanning (PBS) treatments has significantly increased the voxel-wise dose rate in proton therapy. In addition, the dose rate distribution is no longer uniform along the cross section of the target but rather highly heterogeneous, following the spot placement. We suggest investigating dose rate as potential contributor to a more complex proton RBE model. Approach. Due to the time structure of the PBS beam delivery the instantaneous dose rate is highly variable voxel by voxel. Several possible parameters to represent voxel-wise dose rate for a given clinical PBS treatment plan are detailed. These quantities were implemented in the scripting environment of our treatment planning system, and computations experimentally verified. Sample applications to treated patient plans are shown. Main results. Computed dose rates we experimentally confirmed. Dose rate maps vary depending on which method is used to represent them. Mainly, the underlying time and dose intervals chosen determine the topography of the resultant distributions. The maximum dose rates experienced by any target voxel in a given PBS treatment plan in our system range from ∼100 to ∼450 Gy(RBE)/min, a factor of 10–100 increase compared to PS. These dose rate distributions are very heterogeneous, with distinct hot spots. Significance. Voxel-wise dose rates for current clinical PBS treatment plans vary greatly from clinically established practice with PS. The exploration of different dose rate measures to evaluate potential correlations with observed clinical outcomes is suggested, potentially adding a missing component in the understanding of proton relative biological effectiveness (RBE).

Hypoxia-informed RBE-weighted beam orientation optimization for intensity modulated proton therapy.

Variable relative biological effectiveness (RBE) models in treatment planning have been proposed to optimize the therapeutic ratio of proton therapy. It has been reported that proton RBE decreases with increasing tumor oxygen level, offering an opportunity to address hypoxia-related radioresistance with RBE-weighted optimization. Here, we obtain a voxel-level estimation of partial oxygen pressure to weigh RBE values in a single biologically informed beam orientation optimization (BOO) algorithm. Three glioblastoma patients with [18 F]-fluoromisonidazole (FMISO)-PET/CT images were selected from the institutional database. Oxygen values were derived from tracer uptake using a nonlinear least squares curve fitting. McNamara RBE, calculated from proton dose, was then weighed using oxygen enhancement ratios (OER) for each voxel and incorporated into the dose fidelity term of the BOO algorithm. The nonlinear optimization problem was solved using a split-Bregman approach, with FISTA as the solver. The proposed hypoxia informed RBE-weighted method (HypRBE) was compared to dose fidelity terms using the constant RBE of 1.1 (cRBE) and the normoxic McNamara RBE model (RegRBE). Tumor homogeneity index (HI), maximum biological dose (Dmax), and D95%, as well as OAR therapeutic index (TI=gEUDCTV /gEUDOAR ) were evaluated along with worst-case statistics after normalization to normal tissue isotoxicity. Compared to [cRBE, RegRBE], HypRBE increased tumor HI, Dmax, and D95% across all plans by on average [31.3%, 31.8%], [48.6%, 27.1%], and [50.4%, 23.8%], respectively. In the worst-case scenario, the parameters increase on average by [12.5%, 14.7%], [7.3%,-8.9%], and [22.3%, 2.1%]. Despite increased OAR Dmean and Dmax by [8.0%, 3.0%] and [13.1%, -0.1%], HypRBE increased average TI by [22.0%, 21.1%]. Worst-case OAR Dmean, Dmax, and TI worsened by [17.9%, 4.3%], [24.5%, -1.2%], and [9.6%, 10.5%], but in the best cases, HypRBE escalates tumor coverage significantly without compromising OAR dose, increasing the therapeutic ratio. We have developed an optimization algorithm whose dose fidelity term accounts for hypoxia-informed RBE values. We have shown that HypRBE selects bE:\Alok\aaeams better suited to deliver high physical dose to low RBE, hypoxic tumor regions while sparing the radiosensitive normal tissue.

Characterizing Proton-Induced Biological Effects in a Mouse Spinal Cord Model: A Comparison of Bragg Peak and Entrance Beam Response in Single and Fractionated Exposures

Proton relative biological effectiveness (RBE) is a dynamic variable influenced by factors like linear energy transfer (LET), dose, tissue type, and biological endpoint. The standard fixed proton RBE of 1.1, currently used in clinical planning, may not accurately represent the true biological effects of proton therapy (PT) in all cases. This uncertainty can contribute to radiation-induced normal tissue toxicity in patients. In late-responding tissues such as the spinal cord, toxicity can cause devastating complications. This study investigated spinal cord tolerance in mice subjected to proton irradiation and characterized the influence of fractionation on proton- induced myelopathy at entrance (ENT) and Bragg peak (BP) positions. Cervical spinal cords of 8-week-old C57BL/6J female mice were irradiated with single- or multi-fractions (18x) using lateral opposed radiation fields at 1 of 2 positions along the Bragg curve: ENT (dose-mean LET = 1.2 keV/μm) and BP (LET = 6.9 keV/μm). Mice were monitored over 1 year for changes in weight, mobility, and general health, with radiation-induced myelopathy as the primary biological endpoint. Calculations of the RBE of the ENT and BP curve (RBEENT/BP) were performed. Single-fraction RBEENT/BP for 50% effect probability (tolerance dose (TD50), grade II paresis, determined using log-logistic model fitting) was 1.10 ± 0.06 (95% CI) and for multifraction treatments it was 1.19 ± 0.05 (95% CI). Higher incidence and faster onset of paralysis were seen in mice treated at the BP compared with ENT. The findings challenge the universally fixed RBE value in PT, indicating up to a 25% mouse spinal cord RBEENT/BP variation for multifraction treatments. These results highlight the importance of considering fractionation in determining RBE for PT. Robust characterization of proton-induced toxicity, aided by in vivo models, is paramount for refining clinical decision-making and mitigating potential patient side effects.

Deep learning-based synthetic dose-weighted LET map generation for intensity modulated proton therapy

The advantage of proton therapy as compared to photon therapy stems from the Bragg peak effect, which allows protons to deposit most of their energy directly at the tumor while sparing healthy tissue. However, even with such benefits, proton therapy does present certain challenges. The biological effectiveness differences between protons and photons are not fully incorporated into clinical treatment planning processes. In current clinical practice, the relative biological effectiveness (RBE) between protons and photons is set as constant 1.1. Numerous studies have suggested that the RBE of protons can exhibit significant variability. Given these findings, there is a substantial interest in refining proton therapy treatment planning to better account for the variable RBE. Dose-average linear energy transfer (LETd) is a key physical parameter for evaluating the RBE of proton therapy and aids in optimizing proton treatment plans. Calculating precise LETd distributions necessitates the use of intricate physical models and the execution of specialized Monte-Carlo simulation software, which is a computationally intensive and time-consuming progress. In response to these challenges, we propose a deep learning based framework designed to predict the LETd distribution map using the dose distribution map. This approach aims to simplify the process and increase the speed of LETd map generation in clinical settings. The proposed CycleGAN model has demonstrated superior performance over other GAN-based models. The mean absolute error (MAE), peak signal-to-noise ratio and normalized cross correlation of the LETd maps generated by the proposed method are 0.096 ± 0.019 keV μm−1, 24.203 ± 2.683 dB, and 0.997 ± 0.002, respectively. The MAE of the proposed method in the clinical target volume, bladder, and rectum are 0.193 ± 0.103, 0.277 ± 0.112, and 0.211 ± 0.086 keV μm−1, respectively. The proposed framework has demonstrated the feasibility of generating synthetic LETd maps from dose maps and has the potential to improve proton therapy planning by providing accurate LETd information.

A systematic approach for calibrating a Monte Carlo code to a treatment planning system for obtaining dose, LET, variable proton RBE and out-of-field dose

Objective. While integration of variable relative biological effectiveness (RBE) has not reached full clinical implementation, the importance of having the ability to recalculate proton treatment plans in a flexible, dedicated Monte Carlo (MC) code cannot be understated . Here we provide a step-wise method for calibrating dose from a MC code to a treatment planning system (TPS), to obtain required parameters for calculating linear energy transfer (LET), variable RBE and in general enabling clinical realistic research studies beyond the capabilities of a TPS. Approach. Initially, Pristine Bragg peaks (PBP) were calculated in both the Eclipse TPS and the FLUKA MC code. A rearranged Bortfeld energy-range relation was applied to the initial energy of the beam to fine-tune the range of the MC code at 80% dose level distal to the PBP. The energy spread was adapted by dividing the TPS range by the MC range for dose level 80%–20% distal to the PBP. Density and relative proton stopping power were adjusted by comparing the TPS and MC for different Hounsfield units. To find the relationship of dose per primary particle from the MC to dose per monitor unit in the TPS, integration was applied to the area of the Bragg curve. The calibration was validated for spread-out Bragg peaks (SOBP) in water and patient treatment plans. Following the validation, variable RBE were calculated using established models. Main results. The PBPs ranges were within ± mm threshold, and a maximum of 5.5% difference for the SOBPs was observed. The patient validation showed excellent dose agreement between the TPS and MC, with the greatest differences for the lung tumor patient. Significance. A procedure for calibrating a MC code to a TPS was developed and validated. The procedure enables MC-based calculation of dose, LET, variable RBE, advanced (secondary) particle tracking and more from treatment plans.

Variable RBE in proton radiotherapy: a comparative study with the predictive Mayo Clinic Florida microdosimetric kinetic model and phenomenological models of cell survival

Objectives. (1) To examine to what extent the cell- and exposure- specific information neglected in the phenomenological proton relative biological effectiveness (RBE) models could influence the computed RBE in proton therapy. (2) To explore similarities and differences in the formalism and the results between the linear energy transfer (LET)-based phenomenological proton RBE models and the microdosimetry-based Mayo Clinic Florida microdosimetric kinetic model (MCF MKM). (3) To investigate how the relationship between the RBE and the dose-mean proton LET is affected by the proton energy spectrum and the secondary fragments. Approach. We systematically compared six selected phenomenological proton RBE models with the MCF MKM in track-segment simulations, monoenergetic proton beams in a water phantom, and two spread-out Bragg peaks. A representative comparison with in vitro data for human glioblastoma cells (U87 cell line) is also included. Main results. Marked differences were observed between the results of the phenomenological proton RBE models, as reported in previous studies. The dispersion of these models’ results was found to be comparable to the spread in the MCF MKM results obtained by varying the cell-specific parameters neglected in the phenomenological models. Furthermore, while single cell-specific correlation between RBE and the dose-mean proton LET seems reasonable above 2 keV μm−1, caution is necessary at lower LET values due to the relevant contribution of secondary fragments. The comparison with in vitro data demonstrates comparable agreement between the MCF MKM predictions and the results of the phenomenological models. Significance. The study highlights the importance of considering cell-specific characteristics and detailed radiation quality information for accurate RBE calculations in proton therapy. Furthermore, these results provide confidence in the use of the MCF MKM for clonogenic survival RBE calculations in proton therapy, offering a more mechanistic approach compared to phenomenological models.

Acute normal tissue responses in a murine model following fractionated irradiation of the head and neck with protons or X-rays

Background The purpose of this study was to investigate acute normal tissue responses in the head and neck region following proton- or X-irradiation of a murine model. Materials and methods Female C57BL/6J mice were irradiated with protons (25 or 60 MeV) or X-rays (100 kV). The radiation field covered the oral cavity and the major salivary glands. For protons, two different treatment plans were used, either with the Bragg Peak in the middle of the mouse (BP) or outside the mouse (transmission mode; TM). Delivered physical doses were 41, 45, and 65 Gy given in 6, 7, and 10 fractions for BP, TM, and X-rays, respectively. Alanine dosimetry was used to assess delivered doses. Oral mucositis and dermatitis were scored using CTC v.2.0-based tables. Saliva was collected at baseline, right after end of irradiation, and at day 35. Results The measured dose distribution for protons (TM) and X-rays was very similar. Oral mucositis appeared earlier, had a higher score and was found in a higher percentage of mice after proton irradiation compared to X-irradiation. Dermatitis, on the other hand, had a similar appearance after protons and X-rays. Compared to controls, saliva production was lower right after termination of proton- and X-irradiation. The BP group demonstrated saliva recovery compared to the TM and X-ray group at day 35. Conclusion With lower delivered doses, proton irradiation resulted in similar skin reactions and increased oral mucositis compared to X-irradiation. This indicates that the relative biological effectiveness of protons for acute tissue responses in the mouse head and neck is greater than the clinical standard of 1.1. Thus, there is a need for further investigations of the biological effect of protons in normal tissues.

GPU-accelerated calculation of proton microdosimetric spectra as a function of target size, proton energy, and bounding volume size

Objective. Shortcomings of dose-averaged linear energy transfer (LETD), the quantity which is most commonly used to quantify proton relative biological effectiveness, have long been recognized. Microdosimetric spectra may overcome the limitations of LETD but are extremely computationally demanding to calculate. A systematic library of lineal energy spectra for monoenergetic protons could enable rapid determination of microdosimetric spectra in a clinical environment. The objective of this work was to calculate and validate such a library of lineal energy spectra. Approach. SuperTrack, a GPU-accelerated CUDA/C++ based application, was developed to superimpose tracks calculated using Geant4 onto targets of interest and to compute microdosimetric spectra. Lineal energy spectra of protons with energies from 0.1 to 100 MeV were determined in spherical targets of diameters from 1 nm to 10 μm and in bounding voxels with side lengths of 5 μm and 3 mm. Main results. Compared to an analogous Geant4-based application, SuperTrack is up to 3500 times more computationally efficient if each track is resampled 1000 times. Dose spectra of lineal energy and dose-mean lineal energy calculated with SuperTrack were consistent with values published in the literature and with comparison to a Geant4 simulation. Using SuperTrack, we developed the largest known library of proton microdosimetric spectra as a function of primary proton energy, target size, and bounding volume size. Significance. SuperTrack greatly increases the computational efficiency of the calculation of microdosimetric spectra. The elevated lineal energy observed in a 3 mm side length bounding volume suggests that lineal energy spectra determined experimentally or computed in small bounding volumes may not be representative of the lineal energy spectra in voxels of a dose calculation grid. The library of lineal energy spectra calculated in this work could be integrated with a treatment planning system for rapid determination of lineal energy spectra in patient geometries.

Proton boron capture therapy (PBCT) induces cell death and mitophagy in a heterotopic glioblastoma model

Despite aggressive therapeutic regimens, glioblastoma (GBM) represents a deadly brain tumor with significant aggressiveness, radioresistance and chemoresistance, leading to dismal prognosis. Hypoxic microenvironment, which characterizes GBM, is associated with reduced therapeutic effectiveness. Moreover, current irradiation approaches are limited by uncertain tumor delineation and severe side effects that comprehensively lead to unsuccessful treatment and to a worsening of the quality of life of GBM patients. Proton beam offers the opportunity of reduced side effects and a depth-dose profile, which, unfortunately, are coupled with low relative biological effectiveness (RBE). The use of radiosensitizing agents, such as boron-containing molecules, enhances proton RBE and increases the effectiveness on proton beam-hit targets. We report a first preclinical evaluation of proton boron capture therapy (PBCT) in a preclinical model of GBM analyzed via μ-positron emission tomography/computed tomography (μPET-CT) assisted live imaging, finding a significant increased therapeutic effectiveness of PBCT versus proton coupled with an increased cell death and mitophagy. Our work supports PBCT and radiosensitizing agents as a scalable strategy to treat GBM exploiting ballistic advances of proton beam and increasing therapeutic effectiveness and quality of life in GBM patients.

Proposing a Clinical Model for RBE Based on Proton Track-End Counts

In proton therapy, the clinical application of linear energy transfer (LET) optimization remains contentious, in part because of challenges associated with the definition and calculation of LET and its exact relationship with relative biological effectiveness (RBE) because of large variation in experimental in vitro data. This has raised interest in other metrics with favorable properties for biological optimization, such as the number of proton track ends in a voxel. In this work, we propose a novel model for clinical calculations of RBE, based on proton track end counts. We developed an effective dose concept to translate between the total proton track-end count per unit mass in a voxel and a proton RBE value. Dose, track end, and dose-averaged LET (LETd) distributions were simulated using Monte Carlo models for a series of water phantoms, in vitro radiobiological studies, and patient treatment plans. We evaluated the correlation between track ends and regions of elevated biological effectiveness in comparison to LETd-based models of RBE. Track ends were found to correlate with biological effects in in vitro experiments with an accuracy comparable to LETd. In patient simulations, our track end model identified the same biological hotspots as predicted by LETd-based radiobiological models of proton RBE. These results suggest that, for clinical optimization and evaluation, an RBE model based on proton track end counts may match LETd-based models in terms of information provided while also offering superior statistical properties.

Modeling RBE with other quantities than LET significantly improves prediction of in vitro cell survival for proton therapy.

For proton therapy, a relative biological effectiveness (RBE) of 1.1 has broadly been applied clinically. However, as unexpected toxicities have been observed by the end of the proton tracks, variable RBE models have been proposed. Typically, the dose-averaged linear energy transfer (LETd ) has been used as an input variable for these models but the way the LETd was defined, calculated, or determined was not always consistent, potentially impacting the corresponding RBE value. This study compares consistently calculated LETd with other quantities as input variables for a phenomenological RBE model and attempts to determine which quantity that can best predicts proton RBE. The comparison was performed within the frame of introducing a new model for the protonRBE. High-throughput experimental setups of in vitro cell survival studies for proton RBE determination are simulated using the SHIELD-HIT12A Monte Carlo particle transport code. Together with LET, , here called effective Q (Qeff ), and Q are scored. Each quantity is calculated using the dose and track averaging methods, because the scoring includes all hadronic particles, all protons or only primaries. A phenomenological linear-quadratic-based RBE model is subsequently applied to the in vitro data with the various beam quality descriptors used as input variables and the goodness of fit is determined and compared using a bootstrapping approach. Both linear and nonlinear fit functions weretested. Versions of Qeff and Q outperform LET with a statistically significant margin, with the best nonlinear and linear fit having a relative root mean square error (RMSE) for RBE2Gy ± one standard error of 1.55 ± 0.04 (Qeff, t, primary ) and 2.84 ± 0.07 (Qeff, d, primary ), respectively. For comparison, the corresponding best nonlinear and linear fits for LETd, all protons had a relative RMSE of 2.07 ± 0.06 and 3.39 ± 0.08, respectively. Applying Welch's t-test for comparing the calculated RMSE of RBE2Gy resulted in two-tailed p-values of <0.002 for all Q and Qeff quantities compared to LETd, all protons . The study shows that Q or Qeff could be better RBE descriptors that dose averagedLET.