An updated variable RBE model for proton therapy.

Objective.While a constant relative biological effectiveness (RBE) of 1.1 forms the basis for clinical proton therapy, variable RBE models are increasingly being used in plan evaluation. However, there is substantial variation across RBE models, and several newin vitrodatasets have not yet been included in the existing models. In this study, an updatedin vitroproton RBE database was collected and used to examine current RBE model assumptions, and to propose an up-to-date RBE model as a tool for evaluating RBE effects in clinical settings. Approach.A proton database (471 data points) was collected from the literature, almost twice the size of the previously largest model database. Each data point included linear-quadratic model parameters and linear energy transfer (LET). Statistical analyses were performed to test the validity of commonly applied assumptions of phenomenological RBE models, and new model functions were proposed for RBEmaxand 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 RBEmaxfunction should be a free fitting parameter and RBE estimates were clearly higher for models with free intercept. RBEminincreased with increasing LET, while a dependency of RBEminon the reference radiation fractionation sensitivity ((α/ß)x) 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/ß)x=2.1Gy) 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. .

The dirty and clean dose concept: Towards creating proton therapy treatment plans with a photon-like dose response.

BACKGROUND: Applying tolerance doses for organs at risk (OAR) from photon therapy introduces uncertainties in proton therapy when assuming a constant relative biological effectiveness (RBE) of 1.1. PURPOSE: This work introduces the novel dirty and clean dose concept, which allows for creating treatment plans with a more photon-like dose response for OAR and, thus, less uncertainties when applying photon-based tolerance doses. METHODS: The concept divides the 1.1-weighted dose distribution into two parts: the clean and the dirty dose. The clean and dirty dose are deposited by protons with a linear energy transfer (LET) below and above a set LET threshold, respectively. For the former, a photon-like dose response is assumed, while for the latter, the RBE might exceed 1.1. To reduce the dirty dose in OAR, a MaxDirtyDose objective was added in treatment plan optimization. It requires setting two parameters: LET threshold and max dirty dose level. A simple geometry consisting of one target volume and one OAR in water was used to study the reduction in dirty dose in the OAR depending on the choice of the two MaxDirtyDose objective parameters during plan optimization. The best performing parameter combinations were used to create multiple dirty dose optimized (DDopt) treatment plans for two cranial patient cases. For each DDopt plan, 1.1-weighted dose, variable RBE-weighted dose using the Wedenberg RBE model and dose-average LETd distributions as well as resulting normal tissue complication probability (NTCP) values were calculated and compared to the reference plan (RefPlan) without MaxDirtyDose objectives. RESULTS: In the water phantom studies, LET thresholds between 1.5 and 2.5 keV/µm yielded the best plans and were subsequently used. For the patient cases, nearly all DDopt plans led to a reduced Wedenberg dose in critical OAR. This reduction resulted from an LET reduction and translated into an NTCP reduction of up to 19 percentage points compared to the RefPlan. The 1.1-weighted dose in the OARs was slightly increased (patient 1: 0.45 Gy(RBE), patient 2: 0.08 Gy(RBE)), but never exceeded clinical tolerance doses. Additionally, slightly increased 1.1-weighted dose in healthy brain tissue was observed (patient 1: 0.81 Gy(RBE), patient 2: 0.53 Gy(RBE)). The variation of NTCP values due to variation of α/ß from 2 to 3 Gy was much smaller for DDopt (2 percentage points (pp)) than for RefPlans (5 pp). CONCLUSIONS: The novel dirty and clean dose concept allows for creating biologically more robust proton treatment plans with a more photon-like dose response. The reduced uncertainties in RBE can, therefore, mitigate uncertainties introduced by using photon-based tolerance doses for OAR.

Proton dosimetry in a magnetic field: Measurement and calculation of magnetic field correction factors for a plane-parallel ionization chamber.

BACKGROUND: The combination of magnetic resonance imaging and proton therapy offers the potential to improve cancer treatment. The magnetic field (MF)-dependent change in the dosage of ionization chambers in magnetic resonance imaging-integrated proton therapy (MRiPT) is considered by the correction factor k B ⃗ , M , Q $k_{\vec{B},M,Q}$ , which needs to be determined experimentally or computed via Monte Carlo (MC) simulations. PURPOSE: In this study, k B ⃗ , M , Q $k_{\vec{B},M,Q}$ was both measured and simulated with high accuracy for a plane-parallel ionization chamber at different clinical relevant proton energies and MF strengths. MATERIAL AND METHODS: The dose-response of the Advanced Markus chamber (TM34045, PTW, Freiburg, Germany) irradiated with homogeneous 10 × $\times$ 10 cm 2 $^2$ quasi mono-energetic fields, using 103.3, 128.4, 153.1, 223.1, and 252.7 MeV proton beams was measured in a water phantom placed in the MF of an electromagnet with MF strengths of 0.32, 0.5, and 1 T. The detector was positioned at a depth of 2 g/cm 2 $^2$ in water, with chamber electrodes parallel to the MF lines and perpendicular to the proton beam incidence direction. The measurements were compared with TOPAS MC simulations utilizing COMSOL-calculated 0.32, 0.5, and 1 T MF maps of the electromagnet. k B ⃗ , M , Q $k_{\vec{B},M,Q}$ was calculated for the measurements for all energies and MF strengths based on the equation: k B ⃗ , M , Q = M Q M Q B ⃗ $k_{\vec{B},M,Q}=\frac{M_\mathrm{Q}}{M_\mathrm{Q}^{\vec{B}}}$ , where M Q B ⃗ $M_\mathrm{Q}^{\vec{B}}$ and M Q $M_\mathrm{Q}$ were the temperature and air-pressure corrected detector readings with and without the MF, respectively. MC-based correction factors were determined as k B ⃗ , M , Q = D det D det B ⃗ $k_{\vec{B},M,Q}=\frac{D_\mathrm{det}}{D_\mathrm{det}^{\vec{B}}}$ , where D det B ⃗ $D_\mathrm{det}^{\vec{B}}$ and D det $D_\mathrm{det}$ were the doses deposited in the air cavity of the ionization chamber model with and without the MF, respectively. Furthermore, MF effects on the chamber dosimetry are studied using MC simulations, examining the impact on the absorbed dose-to-water ( D W $D_{W}$ ) and the shift in depth of the Bragg peak. RESULTS: The detector showed a reduced dose-response for all measured energies and MF strengths, resulting in experimentally determined k B ⃗ , M , Q $k_{\vec{B},M,Q}$ values larger than unity. For all energies and MF strengths examined, k B ⃗ , M , Q $k_{\vec{B},M,Q}$ ranged between 1.0065 and 1.0205. The dependence on the energy and the MF strength was found to be non-linear with a maximum at 1 T and 252.7 MeV. The MC simulated k B ⃗ , M , Q $k_{\vec{B},M,Q}$ values agreed with the experimentally determined correction factors within their standard deviations with a maximum difference of 0.6%. The MC calculated impact on D W $D_{W}$ was smaller 0.2 %. CONCLUSION: For the first time, measurements and simulations were compared for proton dosimetry within MFs using an Advanced Markus chamber. Good agreement of k B ⃗ , M , Q $k_{\vec{B},M,Q}$ was found between experimentally determined and MC calculated values. The performed benchmarking of the MC code allows for calculating k B ⃗ , M , Q $k_{\vec{B},M,Q}$ for various ionization chamber models, MF strengths and proton energies to generate the data needed for a proton dosimetry protocol within MFs and is, therefore, a step towards MRiPT.

Proton range uncertainty caused by synthetic computed tomography generated with deep learning from pelvic magnetic resonance imaging.

BACKGROUND: In proton therapy, it is disputed whether synthetic computed tomography (sCT), derived from magnetic resonance imaging (MRI), permits accurate dose calculations. On the one hand, an MRI-only workflow could eliminate errors caused by, e.g., MRI-CT registration. On the other hand, the extra error would be induced due to an sCT generation model. This work investigated the systematic and random model error induced by sCT generation of a widely discussed deep learning model, pix2pix. MATERIAL AND METHODS: An open-source image dataset of 19 patients with cancer in the pelvis was employed and split into 10, 5, and 4 for training, testing, and validation of the model, respectively. Proton pencil beams (200 MeV) were simulated on the real CT and generated sCT using the tool for particle simulation (TOPAS). Monte Carlo (MC) dropout was used for error estimation (50 random sCT samples). Systematic and random model errors were investigated for sCT generation and dose calculation on sCT. RESULTS: For sCT generation, random model error near the edge of the body (∼200 HU) was higher than that within the body (∼100 HU near the bone edge and <10 HU in soft tissue). The mean absolute error (MAE) was 49 ± 5, 191 ± 23, and 503 ± 70 HU for the whole body, bone, and air in the patient, respectively. Random model errors of the proton range were small (<0.2 mm) for all spots and evenly distributed throughout the proton fields. Systematic errors of the proton range were -1.0(±2.2) mm and 0.4(±0.9)%, respectively, and were unevenly distributed within the proton fields. For 4.5% of the spots, large errors (>5 mm) were found, which may relate to MRI-CT mismatch due to, e.g., registration, MRI distortion anatomical changes, etc. CONCLUSION: The sCT model was shown to be robust, i.e., had a low random model error. However, further investigation to reduce and even predict and manage systematic error is still needed for future MRI-only proton therapy.

Technical note: Experimental dosimetric characterization of proton pencil beam distortion in a perpendicular magnetic field of an in-beam MR scanner.

BACKGROUND: As it promises more precise and conformal radiation treatments, magnetic resonance imaging-integrated proton therapy (MRiPT) is seen as a next step in image guidance for proton therapy. The Lorentz force, which affects the course of the proton pencil beams, presents a problem for beam delivery in the presence of a magnetic field. PURPOSE: To investigate the influence of the 0.32-T perpendicular magnetic field of an MR scanner on the delivery of proton pencil beams inside an MRiPT prototype system. METHODS: An MRiPT prototype comprising of a horizontal pencil beam scanning beam line and an open 0.32-T MR scanner was used to evaluate the impact of the vertical magnetic field on proton beam deflection and dose spot pattern deformation. Three different proton energies (100, 150, and 220 MeV) and two spot map sizes (15 × 15 and 30 × 20 cm2 ) at four locations along the beam path without and with magnetic field were measured. Pencil-beam dose spots were measured using EBT3 films and a 2D scintillation detector. To study the magnetic field effects, a 2D Gaussian fit was applied to each individual dose spot to determine the central position ( X , Y ) $(X,Y)$ , minimum and maximum lateral standard deviation ( σ m i n $\sigma _{min}$ and σ m a x $\sigma _{max}$ ), orientation (θ), and the eccentricity (ε). RESULTS: The dose spots were subjected to three simultaneous effects: (a) lateral horizontal beam deflection, (b) asymmetric trapezoidal deformation of the dose spot pattern, and (c) deformation and rotation of individual dose spots. The strongest effects were observed at a proton energy of 100 MeV with a horizontal beam deflection of 14-186 mm along the beam path. Within the central imaging field of the MR scanner, the maximum relative dose spot size σ m a x $\sigma _{max}$ decreased by up to 3.66%, while σ m i n $\sigma _{min}$ increased by a maximum of 2.15%. The largest decrease and increase in the eccentricity of the dose spots were 0.08 and 0.02, respectively. The spot orientation θ was rotated by a maximum of 5.39°. At the higher proton energies, the same effects were still seen, although to a lesser degree. CONCLUSIONS: The effect of an MRiPT prototype's magnetic field on the proton beam path, dose spot pattern, and dose spot form has been measured for the first time. The findings show that the impact of the MF must be appropriately recognized in a future MRiPT treatment planning system. The results emphasize the need for additional research (e.g., effect of magnetic field on proton beams with range shifters and impact of MR imaging sequences) before MRiPT applications can be employed to treat patients.

Data-driven ion-independent relative biological effectiveness modeling using the beam quality Q.

Beam quality Q = Z2/E (Z = ion charge, E = energy), an alternative to the conventionally used linear energy transfer (LET), enables ion-independent modeling of the relative biological effectiveness (RBE) of ions. Therefore, the Q concept, i.e. different ions with similar Q have similar RBE values, could help to transfer clinical RBE knowledge from better-studied ion types (e.g. carbon) to other ions. However, the validity of the Q concept has so far only been demonstrated for low LET values. In this work, the Q concept was explored in a broad LET range, including the so-called overkilling region. The particle irradiation data ensemble (PIDE) was used as experimentalin vitrodataset. Data-driven models, i.e. neural network (NN) models with low complexity, were built to predict RBE values for H, He, C and Ne ions at differentin vitroendpoints taking different combinations of clinically available candidate inputs: LET, Q and linear-quadratic photon parameterαx/ßx. Models were compared in terms of prediction power and ion dependence. The optimal model was compared to published model data using the local effect model (LEM IV). The NN models performed best for the prediction of RBE at reference photon doses between 2 and 4 Gy or RBE near 10% cell survival, using onlyαx/ßxand Q instead of LET as input. The Q model was not significantly ion dependent (p > 0.5) and its prediction power was comparable to that of LEM IV. In conclusion, the validity of the Q concept was demonstrated in a clinically relevant LET range including overkilling. A data-driven Q model was proposed and observed to have an RBE prediction power comparable to a mechanistic model regardless of particle type. The Q concept provides the possibility of reducing RBE uncertainty in treatment planning for protons and ions in the future by transferring clinical RBE knowledge between ions.

Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D.

Comprehending cellular changes of radiation-induced brain injury is crucial to prevent and treat the pathology. We provide a unique open dataset of proton-irradiated mouse brains consisting of medical imaging, radiation dose simulations, and large-scale microscopy images, all registered into a common coordinate system. This allows dose-dependent analyses on single-cell level.

Increased relative biological effectiveness and periventricular radiosensitivity in proton therapy of glioma patients.

PURPOSE: Currently, there is an intense debate on variations in intra-cerebral radiosensitivity and relative biological effectiveness (RBE) in proton therapy of primary brain tumours. Here, both effects were retrospectively investigated using late radiation-induced brain injuries (RIBI) observed in follow-up after proton therapy of patients with diagnosed glioma. METHODS: In total, 42 WHO grade 2-3 glioma patients out of a consecutive patient cohort having received (adjuvant) proton radio(chemo)therapy between 2014 and 2017 were eligible for analysis. RIBI lesions (symptomatic or clinically asymptomatic) were diagnosed and delineated on contrast-enhanced T1-weighted magnetic resonance imaging scans obtained in the first two years of follow-up. Correlation of RIBI location and occurrence with dose (D), proton dose-averaged linear energy transfer (LET) and variable RBE dose parameters were tested in voxel- and in patient-wise logistic regression analyses. Additionally, anatomical and clinical parameters were considered. Model performance was estimated through cross-validated area-under-the-curve (AUC) values. RESULTS: In total, 64 RIBI lesions were diagnosed in 21 patients. The median time between start of proton radio(chemo)therapy and RIBI appearance was 10.2 months. Median distances of the RIBI volume centres to the cerebral ventricles and to the clinical target volume border were 2.1 mm and 1.3 mm, respectively. In voxel-wise regression, the multivariable model with D, D × LET and periventricular region (PVR) revealed the highest AUC of 0.90 (95 % confidence interval: 0.89-0.91) while the corresponding model without D × LET revealed a value of 0.84 (0.83-0.86). In patient-level analysis, the equivalent uniform dose (EUD11, a = 11) in the PVR using a variable RBE was the most prominent predictor for RIBI with an AUC of 0.63 (0.32-0.90). CONCLUSIONS: In this glioma cohort, an increased radiosensitivity within the PVR was observed as well as a spatial correlation of RIBI with an increased RBE. Both need to be considered when delivering radio(chemo)therapy using proton beams.

Combined proton radiography and irradiation for high-precision preclinical studies in small animals.

Background and purpose: Proton therapy has become a popular treatment modality in the field of radiooncology due to higher spatial dose conformity compared to conventional radiotherapy, which holds the potential to spare normal tissue. Nevertheless, unresolved research questions, such as the much debated relative biological effectiveness (RBE) of protons, call for preclinical research, especially regarding in vivo studies. To mimic clinical workflows, high-precision small animal irradiation setups with image-guidance are needed. Material and methods: A preclinical experimental setup for small animal brain irradiation using proton radiographies was established to perform planning, repositioning, and irradiation of mice. The image quality of proton radiographies was optimized regarding the resolution, contrast-to-noise ratio (CNR), and minimal dose deposition in the animal. Subsequently, proof-of-concept histological analysis was conducted by staining for DNA double-strand breaks that were then correlated to the delivered dose. Results: The developed setup and workflow allow precise brain irradiation with a lateral target positioning accuracy of<0.26mm for in vivo experiments at minimal imaging dose of<23mGy per mouse. The custom-made software for image registration enables the fast and precise animal positioning at the beam with low observer-variability. DNA damage staining validated the successful positioning and irradiation of the mouse hippocampus. Conclusion: Proton radiography enables fast and effective high-precision lateral alignment of proton beam and target volume in mouse irradiation experiments with limited dose exposure. In the future, this will enable irradiation of larger animal cohorts as well as fractionated proton irradiation.

Comparing biological effectiveness guided plan optimization strategies for cranial proton therapy: potential and challenges.

BACKGROUND: To introduce and compare multiple biological effectiveness guided (BG) proton plan optimization strategies minimizing variable relative biological effectiveness (RBE) induced dose burden in organs at risk (OAR) while maintaining plan quality with a constant RBE. METHODS: Dose-optimized (DOSEopt) proton pencil beam scanning reference treatment plans were generated for ten cranial patients with prescription doses ≥ 54 Gy(RBE) and ≥ 1 OAR close to the clinical target volume (CTV). For each patient, four additional BG plans were created. BG objectives minimized either proton track-ends, dose-averaged linear energy transfer (LETd), energy depositions from high-LET protons or variable RBE-weighted dose (DRBE) in adjacent serially structured OARs. Plan quality (RBE = 1.1) was assessed by CTV dose coverage and robustness (2 mm setup, 3.5% density), dose homogeneity and conformity in the planning target volumes and adherence to OAR tolerance doses. LETd, DRBE (Wedenberg model, α/ßCTV = 10 Gy, α/ßOAR = 2 Gy) and resulting normal tissue complication probabilities (NTCPs) for blindness and brainstem necrosis were derived. Differences between DOSEopt and BG optimized plans were assessed and statistically tested (Wilcoxon signed rank, α = 0.05). RESULTS: All plans were clinically acceptable. DOSEopt and BG optimized plans were comparable in target volume coverage, homogeneity and conformity. For recalculated DRBE in all patients, all BG plans significantly reduced near-maximum DRBE to critical OARs with differences up to 8.2 Gy(RBE) (p < 0.05). Direct DRBE optimization primarily reduced absorbed dose in OARs (average ΔDmean = 2.0 Gy; average ΔLETd,mean = 0.1 keV/µm), while the other strategies reduced LETd (average ΔDmean < 0.3 Gy; average ΔLETd,mean = 0.5 keV/µm). LET-optimizing strategies were more robust against range and setup uncertaintes for high-dose CTVs than DRBE optimization. All BG strategies reduced NTCP for brainstem necrosis and blindness on average by 47% with average and maximum reductions of 5.4 and 18.4 percentage points, respectively. CONCLUSIONS: All BG strategies reduced variable RBE-induced NTCPs to OARs. Reducing LETd in high-dose voxels may be favourable due to its adherence to current dose reporting and maintenance of clinical plan quality and the availability of reported LETd and dose levels from clinical toxicity reports after cranial proton therapy. These optimization strategies beyond dose may be a first step towards safely translating variable RBE optimization in the clinics.

An ion-independent phenomenological relative biological effectiveness (RBE) model for proton therapy.

BACKGROUND: A relative biological effectiveness (RBE) of 1.1 is used for proton therapy though clinical evidence of varying RBE was raised. Clinical studies on RBE variability have been conducted for decades for carbon radiation, which could advance the understanding of the clinical proton RBE given an ion-independent RBE model. In this work, such a model, linear and simple, using the beam quantity Q = Z2/E (Z = ion charge, E = kinetic energy per nucleon) was tested and compared to the commonly used, proton-specific and linear energy transfer (LET) based Wedenberg RBE model. MATERIAL AND METHODS: The Wedenberg and Q models, both predicting RBEmax and RBEmin (i.e., RBE at vanishing and very high dose, respectively), are compared in terms of ion-dependence and prediction power. An experimental in-vitro data ensemble covering 115 publications for various ions was used as dataset. RESULTS: The model parameter of the Q model was observed to be similar for different ions (in contrast to LET). The Q model was trained without any prior knowledge of proton data. For proton RBE, the differences between experimental data and corresponding predictions of the Wedenberg or the Q model were highly comparable. CONCLUSIONS: A simple linear RBE model using Q instead of LET was proposed and tested to be able to predict proton RBE using model parameter trained based on only RBE data of other particles in a clinical proton energy range for a large in-vitro dataset. Adding (pre)clinical knowledge from carbon ion therapy may, therefore, reduce the dominating biological uncertainty in proton RBE modelling. This would translate in reduced RBE related uncertainty in proton therapy treatment planning.

Clinical use and future requirements of relative biological effectiveness: Survey among all European proton therapy centres.

BACKGROUND AND PURPOSE: The relative biological effectiveness (RBE) varies along the treatment field. However, in clinical practice, a constant RBE of 1.1 is assumed, which can result in undesirable side effects. This study provides an accurate overview of current clinical practice for considering proton RBE in Europe. MATERIALS AND METHODS: A survey was devised and sent to all proton therapy centres in Europe that treat patients. The online questionnaire consisted of 39 questions addressing various aspects of RBE consideration in clinical practice, including treatment planning, patient follow-up and future demands. RESULTS: All 25 proton therapy centres responded. All centres prescribed a constant RBE of 1.1, but also applied measures (except for one eye treatment centre) to counteract variable RBE effects such as avoiding beams stopping inside or in front of an organ at risk and putting restrictions on the minimum number and opening angle of incident beams for certain treatment sites. For the future, most centres (16) asked for more retrospective or prospective outcome studies investigating the potential effect of the effect of a variable RBE. To perform such studies, 18 centres asked for LET and RBE calculation and visualisation tools developed by treatment planning system vendors. CONCLUSION: All European proton centres are aware of RBE variability but comply with current guidelines of prescribing a constant RBE. However, they actively mitigate uncertainty and risk of side effects resulting from increased RBE by applying measures and restrictions during treatment planning. To change RBE-related clinical guidelines in the future more clinical data on RBE are explicitly demanded.

Radiomics-based tumor phenotype determination based on medical imaging and tumor microenvironment in a preclinical setting.

BACKGROUND AND PURPOSE: Radiomics analyses have been shown to predict clinical outcomes of radiotherapy based on medical imaging-derived biomarkers. However, the biological meaning attached to such image features often remains unclear, thus hindering the clinical translation of radiomics analysis. In this manuscript, we describe a preclinical radiomics trial, which attempts to establish correlations between the expression of histological tumor microenvironment (TME)- and magnetic resonance imaging (MRI)-derived image features. MATERIALS & METHODS: A total of 114 mice were transplanted with the radioresistant and radiosensitive head and neck squamous cell carcinoma cell lines SAS and UT-SCC-14, respectively. The models were irradiated with five fractions of protons or photons using different doses. Post-treatment T1-weighted MRI and histopathological evaluation of the TME was conducted to extract quantitative features pertaining to tissue hypoxia and vascularization. We performed radiomics analysis with leave-one-out cross validation to identify the features most strongly associated with the tumor's phenotype. Performance was assessed using the area under the curve (AUCValid) and F1-score. Furthermore, we analyzed correlations between TME- and MRI features using the Spearman correlation coefficient ρ. RESULTS: TME and MRI-derived features showed good performance (AUCValid,TME = 0.72, AUCValid,MRI = 0.85, AUCValid,Combined=0.85) individual tumor phenotype prediction. We found correlation coefficients of ρ=-0.46 between hypoxia-related TME features and texture-related MRI features. Tumor volume was a strong confounder for MRI feature expression. CONCLUSION: We demonstrated a preclinical radiomics implementation and notable correlations between MRI- and TME hypoxia-related features. Developing additional TME features may help to further unravel the underlying biology.

Cellular plasticity upon proton irradiation determines tumor cell radiosensitivity.

Proton radiotherapy has been implemented into the standard-of-care for cancer patients within recent years. However, experimental studies investigating cellular and molecular mechanisms are lacking, and prognostic biomarkers are needed. Cancer stem cell (CSC)-related biomarkers, such as aldehyde dehydrogenase (ALDH), are known to influence cellular radiosensitivity through inactivation of reactive oxygen species, DNA damage repair, and cell death. In a previous study, we found that ionizing radiation itself enriches for ALDH-positive CSCs. In this study, we analyze CSC marker dynamics in prostate cancer, head and neck cancer, and glioblastoma cells upon proton beam irradiation. We find that proton irradiation has a higher potential to target CSCs through induction of complex DNA damages, lower rates of cellular senescence, and minor alteration in histone methylation pattern compared with conventional photon irradiation. Mathematical modeling indicates differences in plasticity rates among ALDH-positive CSCs and ALDH-negative cancer cells between the two irradiation types.

Towards harmonizing clinical linear energy transfer (LET) reporting in proton radiotherapy: a European multi-centric study.

BACKGROUND: Clinical data suggest that the relative biological effectiveness (RBE) in proton therapy (PT) varies with linear energy transfer (LET). However, LET calculations are neither standardized nor available in clinical routine. Here, the status of LET calculations among European PT institutions and their comparability are assessed. MATERIALS AND METHODS: Eight European PT institutions used suitable treatment planning systems with their center-specific beam model to create treatment plans in a water phantom covering different field arrangements and fulfilling commonly agreed dose objectives. They employed their locally established LET simulation environments and procedures to determine the corresponding LET distributions. Dose distributions D1.1 and DRBE assuming constant and variable RBE, respectively, and LET were compared among the institutions. Inter-center variability was assessed based on dose- and LET-volume-histogram parameters. RESULTS: Treatment plans from six institutions fulfilled all clinical goals and were eligible for common analysis. D1.1 distributions in the target volume were comparable among PT institutions. However, corresponding LET values varied substantially between institutions for all field arrangements, primarily due to differences in LET averaging technique and considered secondary particle spectra. Consequently, DRBE using non-harmonized LET calculations increased inter-center dose variations substantially compared to D1.1 and significantly in mean dose to the target volume of perpendicular and opposing field arrangements (p < 0.05). Harmonizing LET reporting (dose-averaging, all protons, LET to water or to unit density tissue) reduced the inter-center variability in LET to the order of 10-15% within and outside the target volume for all beam arrangements. Consequentially, inter-institutional variability in DRBE decreased to that observed for D1.1. CONCLUSION: Harmonizing the reported LET among PT centers is feasible and allows for consistent multi-centric analysis and reporting of tumor control and toxicity in view of a variable RBE. It may serve as basis for harmonized variable RBE dose prescription in PT.

Technical note: Providing proton fields down to the few-MeV level at clinical pencil beam scanning facilities for radiobiological experiments.

PURPOSE: The adequate performance of radiobiological experiments using clinical proton beams typically requires substantial preparations to provide the appropriate setup for specific experiments. Providing radiobiologically interesting low-energy protons is a particular challenge, due to various physical effects that become more pronounced with larger absorber thickness and smaller proton energy. This work demonstrates the generation of decelerated low-energy protons from a clinical proton beam. METHODS: Monte Carlo simulations of proton energy spectra were performed for energy absorbers with varying thicknesses to reduce the energy of the clinical proton beam down to the few-MeV level corresponding to µ m-ranges. In this way, a setup with an optimum thickness of the absorber with a maximum efficiency of the proton fluence for the provisioning of low-energy protons is supposed to be found. For the specific applications of 2.5-3.3 MeV protons and α -particle range equivalent protons, the relative depth dose was measured and simulated together with the dose-averaged linear energy transfer (LETd) distribution. RESULTS: The resulting energy spectra from Monte Carlo simulations indicate an optimal absorber thickness for providing low-energy protons with maximum efficiency of proton fluence at an user-requested energy range for experiments. For instance, providing energies lower than 5 MeV, an energy spectrum with a relative total efficiency of 38.6 % to the initial spectrum was obtained with the optimal setup. The measurements of the depth dose, compared to the Monte Carlo simulations, showed that the dosimetry of low-energy protons works and protons with high LETd down to the range of α -particles can be produced. CONCLUSIONS: This work provides a method for generating all clinically and radiobiologically relevant energies - especially down to the few-MeV level - at one clinical facility with pencil beam scanning. Thereby, it enables radiobiological experiments under environmentally uniform conditions.

Does the uncertainty in relative biological effectiveness affect patient treatment in proton therapy?

Clinical treatment with protons uses the concept of relative biological effectiveness (RBE) to convert the absorbed dose into an RBE-weighted dose that equals the dose for radiotherapy with photons causing the same biological effect. Currently, in proton therapy a constant RBE of 1.1 is generically used. However, empirical data indicate that the RBE is not constant, but increases at the distal edge of the proton beam. This increase in RBE is of concern, as the clinical impact is still unresolved, and clinical studies demonstrating a clinical effect of an increased RBE are emerging. Within the European Particle Therapy Network (EPTN) work package 6 on radiobiology and RBE, a workshop was held in February 2020 in Manchester with one day of discussion dedicated to the impact of proton RBE in a clinical context. Current data on RBE effects, patient outcome and modelling from experimental as well as clinical studies were presented and discussed. Furthermore, representatives from European clinical proton therapy centres, who were involved in patient treatment, laid out their current clinical practice on how to consider the risk of a variable RBE in their centres. In line with the workshop, this work considers the actual impact of RBE issues on patient care in proton therapy by reviewing preclinical data on the relation between linear energy transfer (LET) and RBE, current clinical data sets on RBE effects in patients, and applied clinical strategies to manage RBE uncertainties. A better understanding of the variability in RBE would allow development of proton treatments which are safer and more effective.

Identification of patient benefit from proton beam therapy in brain tumour patients based on dosimetric and NTCP analyses.

BACKGROUND: The limited availability of proton beam therapy (PBT) requires individual treatment selection strategies, such as the model-based approach. In this study, we assessed the dosimetric benefit of PBT compared to photon therapy (XRT), analysed the corresponding changes in normal tissue complication probability (NTCP) on a variety of available models, and illustrated model-based patient selection in an in-silico study for patients with brain tumours. METHODS: For 92 patients treated at two PBT centres, volumetric modulated arc therapy treatment plans were retrospectively created for comparison with the clinically applied PBT plans. Several dosimetric parameters for the brain excluding tumour and margins, cerebellum, brain stem, frontal and temporal lobes, hippocampi, cochleae, chiasm, optic nerves, lacrimal glands, lenses, pituitary gland, and skin were compared between both modalities using Wilcoxon signed-rank tests. NTCP differences (ΔNTCP) were calculated for 11 models predicting brain necrosis, delayed recall, temporal lobe injury, hearing loss, tinnitus, blindness, ocular toxicity, cataract, endocrine dysfunction, alopecia, and erythema. A patient was assumed to be selected for PBT if ΔNTCP exceeded a threshold of 10 percentage points for at least one of the side-effects. RESULTS: PBT substantially reduced the dose in almost all investigated OARs, especially in the low and intermediate dose ranges and for contralateral organs. In general, NTCP predictions were significantly lower for PBT compared to XRT, in particular in ipsilateral organs. Considering ΔNTCP of all models, 80 patients (87.0%) would have been selected for PBT in this in-silico study, mainly due to predictions of a model on delayed recall (51 patients). CONCLUSION: In this study, substantial dose reductions for PBT were observed, mainly in contralateral organs. However, due to the sigmoidal dose response, NTCP was particularly reduced in ipsilateral organs. This underlines that physical dose-volume parameters alone may not be sufficient to describe the clinical relevance between different treatment techniques and highlights potential benefits of NTCP models. Further NTCP models for different modern treatment techniques are mandatory and existing models have to be externally validated in order to implement the model-based approach in clinical practice for cranial radiotherapy.

Modelling of late side-effects following cranial proton beam therapy.

BACKGROUND: The limited availability of proton beam therapy (PBT) requires individual treatment selection strategies that can be based on normal tissue complication probability (NTCP) models. We developed and externally validated NTCP models for common late side-effects following PBT in brain tumour patients to optimise patients' quality of life. METHODS: Cohorts from three PBT centres (216 patients) were investigated for several physician-rated endpoints at 12 and 24 months after PBT: alopecia, dry eye syndrome, fatigue, headache, hearing and memory impairment, and optic neuropathy. Dose-volume parameters of associated normal tissues and clinical factors were used for logistic regression modelling in a development cohort. Statistically significant parameters showing high area under the receiver operating characteristic curve (AUC) values in internal cross-validation were externally validated. In addition, analyses of the pooled cohorts and of time-dependent generalised estimating equations including all patient data were performed. RESULTS: In the validation study, mild alopecia was related to high dose parameters to the skin [e.g. the dose to 2% of the volume (D2%)] at 12 and 24 months after PBT. Mild hearing impairment at 24 months after PBT was associated with the mean dose to the ipsilateral cochlea. Additionally, the pooled analyses revealed dose-response relations between memory impairment and intermediate to high doses to the remaining brain as well as D2% of the hippocampi. Mild fatigue at 24 months after PBT was associated with D2% to the brainstem as well as with concurrent chemotherapy. Moreover, in generalised estimating equations analysis, dry eye syndrome was associated with the mean dose to the ipsilateral lacrimal gland. CONCLUSION: We developed and in part validated NTCP models for several common late side-effects following PBT in brain tumour patients. Validation studies are required for further confirmation.

Computer-assisted beam modeling for particle therapy.

PURPOSE: To develop a computer-driven and thus less user-dependent method, allowing for a simple and straightforward generation of a Monte Carlo (MC) beam model of a scanned proton and carbon ion beam delivery system. METHODS: In a first step, experimental measurements were performed for proton and carbon ion energies in the available energy ranges. Data included depth dose profiles measured in water and spot sizes in air at various isocenter distances. Using an automated regularization-based optimization process (AUTO-BEAM), GATE/Geant4 beam models of the respective beam lines were generated. These were obtained sequentially by using least square weighting functions with and without regularization, to iteratively tune the beam parameters energy, energy spread, beam sigma, divergence, and emittance until a user-defined agreement was reached. Based on the parameter tuning for a set of energies, a beam model was semi-automatically generated. The resulting beam models were validated for all centers comparing to independent measurements of laterally integrated depth dose curves and spot sizes in air. For one representative center, three-dimensional dose cubes were measured and compared to simulations. The method was applied on one research as well as four different clinical beam lines for proton and carbon ions of three different particle therapy centers using synchrotron or cyclotron accelerator systems: (a) MedAustron ion therapy center, (b) University Proton Therapy Dresden, and (c) Center Antoine Lacassagne Nice. RESULTS: Particle beam ranges in the MC beam models agreed on average within 0.2 mm compared to measurements for all energies and beam lines. Spot sizes in air (full-width at half maximum) at all positions differed by less than 0.4% from the measurements. Dose calculation with the beam model for the clinical beam line at MedAustron agreed better than 1.7% in absolute dose for a representative clinical case treated with protons. For protons, beam model generation, including geometry creation, data conversion, and validation, was possible within three working days. The number of iterations required for the optimization process to converge, was found to be similar for all beam line geometries and particle types. CONCLUSION: The presented method was demonstrated to work independently of the beam optics behavior of the different beam lines, particle types, and geometries. Furthermore, it is suitable for non-expert users and requires only limited user interaction. Beam model validation for different beam lines based on different beam delivery systems, showed good agreement.