Proton Relative Biological Effectiveness Research Articles

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.

Hypoxia adapted relative biological effectiveness models for proton therapy: a simulation study

In proton therapy, a constant relative biological effectiveness (RBE) factor of 1.1 is applied although the RBE has been shown to depend on factors including the Linear Energy Transfer (LET). The biological effectiveness of radiotherapy has also been shown to depend on the level of oxygenation, quantified by the oxygen enhancement ratio (OER). To estimate the biological effectiveness across different levels of oxygenation the RBE-OER-weighted dose (ROWD) can be used. To investigate the consistency between different approaches to estimate ROWD, we implemented and compared OER models in a Monte Carlo (MC) simulation tool. Five OER models were explored: Wenzl and Wilkens 2011 (WEN), Tinganelli et al 2015 (TIN), Strigari et al 2018 (STR), Dahle et al 2020 (DAH) and Mein et al 2021 (MEI). OER calculations were combined with a proton RBE model and the microdosimetric kinetic model for ROWD calculations. ROWD and OER were studied for a water phantom scenario and a head and neck cancer case using hypoxia PET data for the OER calculation. The OER and ROWD estimates from the WEN, MEI and DAH showed good agreement while STR and TIN gave higher OER values and lower ROWD. The WEN, STR and DAH showed some degree of OER-LET dependency while this was negligible for the MEI and TIN models. The ROWD for all implemented models is reduced in hypoxic regions with an OER of 1.0–2.1 in the target volume. While some variations between the models were observed, all models display a large difference in the estimated dose from hypoxic and normoxic regions. This shows the potential to increase the dose or LET in hypoxic regions or reduce the dose to normoxic regions which again could lead to normal tissue sparing. With reliable hypoxia imaging, RBE-OER weighting could become a useful tool for proton therapy plan optimization.

Proton Tumor Control Relative Biological Effectiveness (RBE) Estimated for Various Fractionation Schedules and Hypoxia Levels

<h3>Purpose/Objective(s)</h3> We previously modeled clinical proton local control data of early-stage lung cancer using a simulation model based on an energy budget. The fitted modeling results suggest that proton therapy is nearly independent of hypoxia reoxygenation which is a key process to model photon therapy results. In this work, we investigated the effect of variable hypoxia levels on the local control efficacy of proton therapy compared to photon, in terms of RBE, for various fractionation schedules, searching for the optimal treatment option. <h3>Materials/Methods</h3> Model simulations were performed with a previously developed tumor simulation model with clinically derived parameter values for photon (CCR; 23; 5469-79; 2017) and proton (presented at RRS 2021). For five different levels of hypoxia, represented by growth fractions (GF), where lower GF represents more hypoxic condition, simulations were performed for 14 clinically applied fractionation schedules (see Table). For each proton schedule, the cell survival fraction was estimated and a partner simulation was performed with photon parameters, to find the dose level with the same fractionation scheme that would lead to an equivalent cell survival level. Finally, the proton RBE was calculated from the ratio of determined photon physical dose to physical dose of proton. <h3>Results</h3> The estimated proton RBE depends on both the hypoxia level as well as the fractionation schedule (see Table). The proton RBE increases with increasing hypoxic level (decreasing GF) for all fractionation schedules. For shorter fractionation schedules (3-5 fxs), proton therapy is always more effective than photon, with RBEs of 1.06-1.18. For longer fractionation schedules (>10), however, the RBE decreases as the GF increases (less hypoxia) and even drops below 1.0 (down to 0.81) for some schedules, due to the ability of photon therapy to take advantage of long term reoxygenation. For each hypoxia level, an optimal schedule exists with the highest RBE value: longer schedules (10-12 fx) are better for hypoxic tumors; 5 fx schedules are better for less hypoxic tumors. <h3>Conclusion</h3> The results predict that proton vs. photon local control will depend critically on the hypoxic level, with proton therapy more effective as the level of hypoxia increases. For longer treatment schedules, proton therapy loses this advantage with decreasing hypoxia, suggesting that longer proton schedules may be less effective for well-oxygenation tumors. These predicted effects should be further probed through both preclinical experiments and clinical data analyses.

Modeling Variable Proton Relative Biological Effectiveness (RBE) Using Voxel-Level Image Density Change for Non-Small Cell Lung Cancer (NSCLC) Patients Treated with Passive Scattering Proton Therapy (PSPT) or Intensity Modulated Photon Therapy (IMRT)

<h3>Purpose/Objective(s)</h3> To test the hypothesis that radiation damage measured via lung image density change is a function of variable RBE for protons with a coefficient of determination (R²) greater than 0.8. <h3>Materials/Methods</h3> Data from 61 NSCLC patients previously treated on a prospective clinical trial with PSPT (N = 24) or IMRT (N = 37) to 74 Gy(RBE) (proton RBE of 1.1) were acquired. Patients with noticeable atelectasis or pleural effusion were excluded. For each patient, a ∼5-month post-treatment PETCT was obtained to assess response and was deformed to the planning exhale phase of the 4DCT (exCT) via a biomechanical model-based image registration algorithm. Subsequently, voxel-level image density change (IDC) within the normal ipsilateral lung was obtained by subtracting the exCT from the deformed PETCT, as a signal of biological damage. IMRT dose (D_x) was recalculated on the exCT using a commercial treatment planning system while the PSPT dose (D_p) and dose-averaged linear energy transfer (LET_d) were recalculated on the exCT using a commissioned track-repeating Monte-Carlo system. The fitted variable RBE models included a linear model (RBE = 1 + λ•LET_d) and a linear-quadratic (LQ) model (RBE = 1/(2D_p)•{√[(α/β)² + 4D_p(α/β)(p₀+(p₁•LET_d)/(α/β) + 4D_p²(p₂ + p₃•√(α/β)•LET_d)] - α/β}) with α/β = 3 Gy for lung tissue. <b>RBE was modeled against the ratio between D_x and D_p at given IDC levels.</b> We adapted Lyman-Kutcher-Burman normal tissue complication probability (NTCP) model to fit the dose-IDC relationship for each cohort by averaging the dose and IDC of voxels in 5-Gy dose intervals (adaptations: n = 1, effective dose = voxel dose, and complication probability = IDC normalized against global min and max IDC). LET_d corresponding to each D_p was approximated by averaging the LET_d of voxels that received D_p with a margin of 0.001Gy. All NTCP and RBE models were fitted using non-linear least squares with the Python SciPy package. <h3>Results</h3> For NTCP fitting, min and max IDC were 2.7 HU and 136.3 HU, respectively, and TD₅₀ and m were fitted to be 31.5 Gy and 0.51 for PSPT (R² = 0.99) and 44.4 Gy and 0.40 for IMRT (R² = 0.98). The table shows the IDC levels, the corresponding LET_d (SD), and the fitted D_x and D_p. The linear model resulted in λ = 0.154 µm/keV (R² = 0.27), and the LQ model resulted in p₀ = -0.72, p₁ = 10.02, p₂ = 1.51, and p₃ = -0.08 (R² = 0.999). <h3>Conclusion</h3> The study demonstrates that voxel-level radiation damage and image changes for proton therapy are functions of dose and LET and, thus, variable RBE.

First experimental measurements of 2D microdosimetry maps in proton therapy.

Empirical data in proton therapy indicate that relative biological effectiveness (RBE) is not constant, and it is directly related to the linear energy transfer (LET). The experimental assessment of LET with high resolution would be a powerful tool for minimizing the LET hot spots in intensity-modulated proton therapy, RBE- or LET-guided evaluation and optimization to achieve biologically optimized proton plans, verifying the theoretical predictions of variable proton RBE models, and so on. This could impact clinical outcomes by reducing toxicities in organs at risk. The present work shows the first 2D LET maps obtained at a proton therapy facility using the double scattering delivery mode in clinical conditions by means of new silicon 3D-cylindrical microdetectors. The device consists of a matrix of 121 independent silicon-based detectors that have 3D-cylindrical electrodes of 25-µm diameter and 20-µm depth, resulting each one of them in a well-defined micrometric radiation sensitive volume etched inside the silicon. They have been specifically designed for a hadron therapy, improving the performance of current silicon-based microdosimeters. Microdosimetry spectra were obtained at different positions of the Bragg curve by using a water-equivalent phantom along an 89-MeV pristine proton beam generated in the Y1 proton passive scattering beamline of the Orsay Proton Therapy Centre (Institut Curie, France). Microdosimetry 2D-maps showing the variation of the lineal energy with depth in the three dimensions were obtained in situ during irradiation at clinical fluence rates (∼108 s-1 cm-2 ) for the first time with a spatial resolution of 200µm, the highest achieved in the transverse plane so far. The experimental results were cross-checked with Monte Carlo simulations and a good agreement between the spectra shapes was found. The experimental frequency-mean lineal energy values in silicon were 1.858±0.019keVµm-1 at the entrance, 2.61±0.03keVµm-1 at the proximal distance, 4.97±0.05keVµm-1 close to the Bragg peak, and 8.6±0.1keVµm-1 at the distal edge. They are in good agreement with the expected trends in the literature in clinical proton beams. We present the first 2D microdosimetry maps obtained in situ during irradiation at clinical fluence rates in proton therapy. Our results show that the arrays of 3D-cylindrical microdetectors are a reliable microdosimeter to evaluate LET maps not only in the longitudinal axis of the beam, but also in the transverse plane allowing for LET characterization in three dimensions. This work is a proof of principle showing the capacity of our system to deliver LET 2D maps. This kind of experimental data is needed to validate variable proton RBE models and to optimize LET-guided plans.

Comparison of 126 MeV antiproton and proton—a FLUKA-based microdosimetric approach

Objective. This study aims at comparing dosimetric parameters of 126 MeV antiprotons and protons using microdosimetric approach. Approach. Microdosimetric distributions of 126 MeV proton and antiproton beams at 1 μm site size are calculated using the Monte Carlo-based FLUKA code. The distributions are calculated at various depths along the central axis in water phantom as well as at different off-axis locations. The study also includes calculations of secondary radiations produced by antiprotons and protons. Mean quality factor, is calculated using the ICRP 60 and ICRU 40 recommendations. The Relative Biological Effectiveness (RBE) of HSG tumour cell at 10% survival level is calculated based on Microdosimetric Kinetic Model. Main results. and RBE for antiprotons are higher by a factor of about 3.60, 3.41 and 1.24, respectively, at Bragg-peak and higher by a factor of about 1.41, 1.76 and 1.05, respectively, at plateau region of depth-dose profile when compared to protons. At 15 cm depth along the central axis, and RBE for protons are higher by a factor of about 1.42, 1.66 and 1.26, respectively, when compared to antiprotons. At the off-axis distance (Ld ) of 6 cm (at 11.5 cm depth in water), and of protons are higher than that of antiproton whereas the trend is opposite at off-axis distance of 4 cm. At Ld = 4 cm (at 11.5 cm depth in water), RBE of antiprotons is higher by about 4% than protons whereas at Ld = 6 cm, RBE of protons is higher by about 13% than antiprotons. Significance. The study shows that antiproton radiotherapy is advantageous as compared to protons considering enhancements in the absorbed dose and RBE-weighed dose values at the Bragg-peak.

An empirical model of proton RBE based on the linear correlation between x-ray and proton radiosensitivity.

Proton relative biological effectiveness (RBE) is known to depend on physical factors of the proton beam, such as its linear energy transfer (LET), as well as on cell-line specific biological factors, such as their ability to repair DNA damage. However, in a clinical setting, proton RBE is still considered to have a fixed value of 1.1 despite the existence of several empirical models that can predict proton RBE based on how a cell's survival curve (linear-quadratic model [LQM]) parameters α and β vary with the LET of the proton beam. Part of the hesitation to incorporate variable RBE models in the clinic is due to the great noise in the biological datasets on which these models are trained, often making it unclear which model, if any, provides sufficiently accurate RBE predictions to warrant a departure from RBE=1.1. Here, we introduce a novel model of proton RBE based on how a cell's intrinsic radiosensitivity varies with LET, rather than its LQM parameters. We performed clonogenic cell survival assays for eight cell lines exposed to 6 MV x-rays and 1.2, 2.6, or 9.9keV/µm protons, and combined our measurements with published survival data (n=397 total cell line/LET combinations). We characterized how radiosensitivity metrics of the form DSF% , (the dose required to achieve survival fraction [SF], e.g., D10% ) varied with proton LET, and calculated the Bayesian information criteria associated with different LET-dependent functions to determine which functions best described the underlying trends. This allowed us to construct a six-parameter model that predicts cells' proton survival curves based on the LET dependence of their radiosensitivity, rather than the LET dependence of the LQM parameters themselves. We compared the accuracy of our model to previously established empirical proton RBE models, and implemented our model within a clinical treatment plan evaluation workflow to demonstrate its feasibility in a clinical setting. Our analyses of the trends in the data show that DSF% is linearly correlated between x-rays and protons, regardless of the choice of the survival level (e.g., D10% , D37% , or D50% are similarly correlated), and that the slope and intercept of these correlations vary with proton LET. The model we constructed based on these trends predicts proton RBE within 15%-30% at the 68.3% confidence level and offers a more accurate general description of the experimental data than previously published empirical models. In the context of a clinical treatment plan, our model generally predicted higher RBE-weighted doses than the other empirical models, with RBE-weighted doses in the distal portion of the field being up to 50.7% higher than the planned RBE-weighted doses (RBE=1.1) to the tumor. We established a new empirical proton RBE model that is more accurate than previous empirical models, and that predicts much higher RBE values in the distal edge of clinical proton beams.