Investigation of dosimetric effect of beam current fluctuations in synchrotron-based proton PBS continuous scanning.

Objective.In proton pencil beam scanning (PBS) continuous delivery, the beam is continuously delivered without interruptions between spots. For synchrotron-based systems, the extracted beam current exhibits a spill structure, and recent publications on beam current measurements have demonstrated significant fluctuations around the nominal values. These fluctuations potentially lead to dose deviations from those calculated assuming a stable beam current. This study investigated the dosimetric implications of such beam current fluctuations during proton PBS continuous scanning.Approach.Using representative clinical proton PBS plans, we performed simulations to mimic a worst-case clinical delivery environment with beam current varies from 50% to 250% of the nominal values. The simulations used the beam delivery parameters optimized for the best beam delivery efficiency of the upcoming particle therapy system at Mayo Clinic Florida. We reconstructed the simulated delivered dose distributions and evaluated the dosimetric impact of beam current fluctuations.Main results.Despite significant beam current fluctuations resulting in deviations at each spot level, the overall dose distributions were nearly identical to those assuming a stable beam current. The 1 mm/1% Gamma passing rate was 100% for all plans. Less than 0.2% root mean square error was observed in the planning target volume dose-volume histogram. Minimal differences were observed in all dosimetric evaluation metrics.Significance.Our findings demonstrate that with our beam delivery system and clinical planning practice, while significant beam current fluctuations may result in large local move monitor unit deviations at each spot level, the overall impact on the dose distribution is minimal.

Deep learning based linear energy transfer calculation for proton therapy.

Objective.This study aims to address the limitations of traditional methods for calculating linear energy transfer (LET), a critical component in assessing relative biological effectiveness (RBE). Currently, Monte Carlo (MC) simulation, the gold-standard for accuracy, is resource-intensive and slow for dose optimization, while the speedier analytical approximation has compromised accuracy. Our objective was to prototype a deep-learning-based model for calculating dose-averaged LET (LETd) using patient anatomy and dose-to-water (DW) data, facilitating real-time biological dose evaluation and LET optimization within proton treatment planning systems.Approach. 275 4-field prostate proton Stereotactic Body Radiotherapy plans were analyzed, rendering a total of 1100 fields. Those were randomly split into 880, 110, and 110 fields for training, validation, and testing. A 3D Cascaded UNet model, along with data processing and inference pipelines, was developed to generate patient-specific LETddistributions from CT images and DW. The accuracy of the LETdof the test dataset was evaluated against MC-generated ground truth through voxel-based mean absolute error (MAE) and gamma analysis.Main results.The proposed model accurately inferred LETddistributions for each proton field in the test dataset. A single-field LETdcalculation took around 100 ms with trained models running on a NVidia A100 GPU. The selected model yielded an average MAE of 0.94 ± 0.14 MeV cm-1and a gamma passing rate of 97.4% ± 1.3% when applied to the test dataset, with the largest discrepancy at the edge of fields where the dose gradient was the largest and counting statistics was the lowest.Significance.This study demonstrates that deep-learning-based models can efficiently calculate LETdwith high accuracy as a fast-forward approach. The model shows great potential to be utilized for optimizing the RBE of proton treatment plans. Future efforts will focus on enhancing the model's performance and evaluating its adaptability to different clinical scenarios.

The effect of common dental fixtures on treatment planning and delivery for head and neck intensity modulated proton therapy.

PURPOSE: Proton treatment plan perturbation by common dental fixtures such as amalgams (Am) and porcelain-fused-to-metal (PFM) crowns has, to date, been uncharacterized. Previous studies have been conducted to determine the physical effect of these materials within the beam path for single spots, but their effects on complex treatment plans and clinical anatomy have not yet been quantified. The present manuscript aims to study the effect of Am and PFM fixtures on proton treatment planning in a clinical setting. METHODS: An anthropomorphic phantom with removable tongue, maxilla, and mandible modules was simulated on a clinical computed tomography (CT) scanner. Spare maxilla modules were modified to include either a 1.5 mm depth central groove occlusal amalgam (Am) or a porcelain-fused-to-metal (PFM) crown, implanted on the first right molar. Modified tongue modules were 3D printed to accommodate several axial or sagittal oriented pieces of EBT-3 film. Clinically representative spot-scanning proton plans were generated in Eclipse v.15.6 using the proton convolution superposition (PCS) algorithm v.15.6.06 using a multi-field optimization (MFO) technique with the goal of delivering a uniform 54 Gy dose to a clinical target volume (CTV) typical of a base-of-tongue (BoT) treatment. A typical geometric beam arrangement of two anterior oblique (AO) beams and a posterior beam was employed. Plans optimized without any material overrides were delivered to the phantom A) without implants; B) with Am fixture; or C) with PFM crown. Plans were also reoptimized and delivered with inclusion of material overrides to equate relative stopping power of the fixture with that of a previously measured result. RESULTS: Plans exhibit slightly greater dose weight towards AO beams. The optimizer accounted for inclusion of fixture overrides by increasing beam weights to the beam closest to the implant. Film measurements exhibited cold spots directly within the beam path through the fixture in plans with and without overridden materials. Cold spots were somewhat mitigated in plans including overridden materials in the structure set but were not entirely eliminated. Cold spots associated with Am and PFM fixtures were quantified at 17% and 14% for plans without overrides, respectively, and 11% and 9% with using Monte Carlo simulation. Compared with film measurements and Monte Carlo simulation, the treatment planning system underestimates the dose shadowing effect in plans including material overrides. CONCLUSIONS: Dental fixtures create a dose shadowing effect directly in line with the beam path through the material. This cold spot is partially mitigated by overriding the material to measured relative stopping powers. Due to uncertainties in modeling perturbation through the fixture, the magnitude of the cold spot is underestimated using the institutional TPS when compared to measurement and MC simulation.

Physical characterization of therapeutic proton delivery through common dental materials.

PURPOSE: Dental fixtures are commonplace in an aging, radiation treatment population. The current, local standard of practice in particle therapy is to employ treatment geometries to avoid delivery through implanted dental fixtures. The present study aims to observe the physical effect of delivering therapeutic proton beams through common dental fixture materials as prelude to an eventual goal of assessing the feasibility of using treatment geometries not specified for avoidance of oral implants. A sampling of common dental materials was selected based on prosthodontic consult and was evaluated in terms of relative stopping power and three-dimensional (3D) dose perturbation. METHODS: Amalgams, porcelain-fused-to-metal (PFM) crowns consisting of zirconia and non-noble base metals, and lithium disilicate implants were chosen for analysis. Theoretical stopping power (S) and mass stopping power (S/ρ) were calculated using the Stopping and Range of Ions in Matter (SRIM) application, basing stoichiometric compositions of each fixture on published materials data. S and S/ρ were calculated for a range of historically available compositions of amalgams from 1900 until the current era. The perturbance of S and S/ρ as a function of clinically relevant ranges of amalgam compositions for the modern era was analyzed. Water equivalent thickness (WET) and relative stopping power (Srel ) of each material was measured for a clinical spot-scanning proton beam with monoenergies of 159.9 and 228.8 MeV with a multi-layer ionization chamber (MLIC). Subsequently, 3D dose perturbation was assessed by delivering proton beams through a custom phantom designed to simulate both en-face and on-edge treatment geometries through the selected materials. A treatment plan mimicking the experimental delivery was constructed in the institutional treatment planning system and calculated using TOPAS-based Monte Carlo simulation (MCS). Experimental results were used to validate the MCS. Finally, treatment planning system (TPS) outputs were compared to MCS to determine the accuracy of the dose calculation model. RESULTS: Historical compositions of amalgams ranged in S from 44.8 to 42.9 MeV/cm, with the greatest deviation being observed for the 1900-1959 era. Deviation as a function of amalgam composition from the modern era was most sensitive to proportion of Hg, accounting for deviations up to -4.2% at the greatest clinically relevant concentration. S/ρ was not found to vary greatly between each porcelain and metal alloy material for PFM type crowns. Relative stopping powers ranged between 1.3 and 5.4 for all studied materials, suggesting substantial changes in proton range with respect to water. Film measurements of pristine spots confirm dose perturbance and shortening of proton range, with an upstream shift of each Bragg peak being observed directly behind the installed fixture. At high energies, cold spots were found in all cases directly behind each material feature with a medial fill-in of dose occurring distally. Qualitative agreement of spot perturbance was confirmed between film measurements and MCS. Finally, when comparing integrated depth doses (IDD) by summing over all axial directions, good agreement is observed between TPS and MCS. CONCLUSIONS: All dental materials studied substantially perturbed the dosimetry of pristine proton spots both in terms of WET/Srel as well as the spatial distribution of dose. Proton range was quantifiably shortened, and each dental material affected a cold spot directly behind the object with medial dose back-filling was observed distally. MCS and Eclipse dose calculations exhibited good agreement with measurements, suggesting that treatment planning without employing avoidance strategies may be possible with further investigation.

Initial Results of a Phase 2 Trial of 18F-DOPA PET-Guided Dose-Escalated Radiation Therapy for Glioblastoma.

PURPOSE: Our previous work demonstrated that 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (18F-DOPA) positron emission tomography (PET) is sensitive and specific for identifying regions of high density and biologically aggressive glioblastoma. The purpose of this prospective phase 2 study was to determine the safety and efficacy of biologic-guided, dose-escalated radiation therapy (DERT) using 18F-DOPA PET in patients with glioblastoma. METHODS AND MATERIALS: Patients with newly diagnosed, histologically confirmed glioblastoma aged ≥18 years without contraindications to 18F-DOPA were eligible. Target volumes included 51, 60, and 76 Gy in 30 fractions with a simultaneous integrated boost, and concurrent and adjuvant temozolomide for 6 months. 18F-DOPA PET imaging was used to guide DERT. The study was designed to detect a true progression-free survival (PFS) at 6 months (PFS6) rate ≥72.5% in O6-methylguanine methyltransferase (MGMT) unmethylated patients (DE-Un), with an overall significance level (alpha) of 0.20 and a power of 80%. Kaplan-Meier analysis was performed for PFS and overall survival (OS). Historical controls (HCs) included 139 patients (82 unmethylated) treated on prospective clinical trials or with standard RT at our institution. Toxicities were evaluated with Common Terminology Criteria for Adverse Events v4.0. RESULTS: Between January 2014 and December 2018, 75 evaluable patients were enrolled (39 DE-Un, 24 methylated [DE-Mth], and 12 indeterminate). PFS6 for DE-Un was 79.5% (95% confidence interval, 63.1%-90.1%). Median PFS was longer for DE-Un patients compared with historical controls (8.7 months vs 6.6 months; P = .017). OS was similarly longer, but the difference was not significant (16.0 vs 13.5 months; P = .13). OS was significantly improved for DE-Mth patients compared with HC-Mth (35.5 vs 23.3 months; P = .049) despite nonsignificant improvement in PFS (10.7 vs 9.0 months; P = .26). Grade 3 central nervous system necrosis occurred in 13% of patients, but treatment with bevacizumab improved symptoms in all cases. CONCLUSIONS: 18F-DOPA PET-guided DERT appears to be safe, and it significantly improves PFS in MGMT unmethylated glioblastoma. OS is significantly improved in MGMT methylated patients. Further investigation of 18F-DOPA PET biologic guided DERT for glioblastoma is warranted.

Robust radiobiological optimization of ion beam therapy utilizing Monte Carlo and microdosimetric kinetic model.

To develop a Monte Carlo (MC)-based and robust ion beam therapy optimization system that separates the optimization algorithm from the relative biological effectiveness (RBE) modeling. Robustly optimized dose distributions were calculated and compared across three ion therapy beams (proton, helium, carbon). The effect of different averaging techniques in calculating RBE in mixed beams was also investigated. Ion particles were transported in TOPAS MC. The microdosimetric-kinetic model (MKM) parameter, saturation corrected specific energy ([Formula: see text]), was calculated with a customized MKM implementation in TOPAS MC. Intensity modulated ion therapy robust optimization was performed by a quasi-Newton iterative method based on dose-volume objective function. The robust optimization took setup and range uncertainties into account. In the present work, the biological dose for each individual spot was calculated, and then summed together to calculate total biological dose. In other published works, radiosensitive parameters, such as [Formula: see text], were first averaged over all beam spots within a mixed-beam field, after which biological dose was calculated using the averaged radiosensitive parameters. The difference between the two mixed-beam biological dose calculations was quantified. Robust plans were achieved with the three particle types. The effect of averaging [Formula: see text] depended on particle type. The difference between biological doses calculated with individual [Formula: see text] and averaged [Formula: see text] may be greater than 3% for a carbon beam. MC based radiobiological and robust optimization was made flexible to incorporate dose-volume histogram constraints and to be independent of RBE models. Iterative optimization with RBE models was feasible. Evaluation of the RBE calculation for mixed beam could be necessary if better accuracy was demanded.

Incorporation of Biologic Response Variance Modeling Into the Clinic: Limiting Risk of Brachial Plexopathy and Other Late Effects of Breast Cancer Proton Beam Therapy.

PURPOSE: The relative biologic effectiveness (RBE) rises with increasing linear energy transfer toward the end of proton tracks. Presently, there is no consensus on how RBE heterogeneity should be accounted for in breast cancer proton therapy treatment planning. Our purpose was to determine the dosimetric consequences of incorporating a brachial plexus (BP) biologic dose constraint and to describe other clinical implications of biologic planning. METHODS AND MATERIALS: We instituted a biologic dose constraint for the BP in the context of MC1631, a randomized trial of conventional versus hypofractionated postmastectomy intensity modulated proton therapy (IMPT). IMPT plans of 13 patients treated before the implementation of the biologic dose constraint (cohort A) were compared with IMPT plans of 38 patients treated on MC1631 after its implementation (cohort B) using (1) a commercially available Eclipse treatment planning system (RBE = 1.1); (2) an in-house graphic processor unit-based Monte Carlo physical dose simulation (RBE = 1.1); and (3) an in-house Monte Carlo biologic dose (MCBD) simulation that assumes a linear relationship between RBE and dose-averaged linear energy transfer (product of RBE and physical dose = biologic dose). RESULTS: Before implementation of a BP biologic dose constraint, the Eclipse mean BP D0.01 cm3 was 107%, and the MCBD estimate was 128% (ie, 64 Gy [RBE = biologic dose] in 25 fractions for a 50-Gy [RBE = 1.1] prescription), compared with 100.0% and 116.0%, respectively, after the implementation of the constraint. Implementation of the BP biologic dose constraint did not significantly affect clinical target volume coverage. MCBD plans predicted greater internal mammary node coverage and higher heart dose than Eclipse plans. CONCLUSIONS: Institution of a BP biologic dose constraint may reduce brachial plexopathy risk without compromising target coverage. MCBD plan evaluation provides valuable information to physicians that may assist in making clinical judgments regarding relative priority of target coverage versus normal tissue sparing.

Biologic Dose and Imaging Changes in Pediatric Brain Tumor Patients Receiving Spot Scanning Proton Therapy.

PURPOSE: To evaluate the incidence of imaging changes in our pediatric brain tumor population treated with spot-scanning proton therapy and analyze the spatial correlation of imaging changes with a novel biologic dose model. METHODS AND MATERIALS: All pediatric patients treated during the first year of our institution's experience who received a minimum treatment planning dose (TPD) of 5040 cGyE with available follow-up magnetic resonance imaging scans were selected for analysis. Posttreatment magnetic resonance imaging scans were fused with the treatment planning computed tomography. All T1 post-gadolinium enhancement, T2 fluid attenuated inversion recovery changes, TPD, and biologic dose (BD) volumes outside of the original gross tumor volume were contoured for analysis. RESULTS: Thirty patients were included in the analysis, 7 of whom developed posttreatment radiologic changes. The volumetric overlap of the T2 fluid attenuated inversion recovery changes and BD volumes was significantly greater than the overlap with the TPD volumes. Median volumetric overlaps of 85%, 18%, and 0% were observed with the BD105%, BD110%, and TPD105%, respectively. A nonsignificant increase in the volumetric overlap of the T1C+ changes and BD volumes was also observed. No correlation was observed between the total volume of BD110%, BD105%, or physical dose 105% and the development of imaging changes. CONCLUSIONS: Within our pediatric brain tumor population treated with spot-scanning proton therapy, our BD model demonstrated superior volumetric overlap with posttreatment T2 changes compared with the TPD model. Using a BD model in treatment planning for spot-scanning proton therapy may help avoid delivery of excessive BD to critical structures and may help minimize the risk of radiation-related late effects.

A Monte-Carlo-based and GPU-accelerated 4D-dose calculator for a pencil beam scanning proton therapy system.

PURPOSE: The presence of respiratory motion during radiation treatment leads to degradation of the expected dose distribution, both for target coverage and healthy tissue sparing, particularly for techniques like pencil beam scanning proton therapy which have dynamic delivery systems. While tools exist to estimate this degraded four-dimensional (4D) dose, they typically have one or more deficiencies such as not including the particular effects from a dynamic delivery, using analytical dose calculations, and/or using nonphysical dose-accumulation methods. This work presents a clinically useful 4D-dose calculator that addresses each of these shortcomings. METHODS: To quickly compute the 4D dose, the three main tasks of the calculator were run on graphics processing units (GPUs). These tasks were (a) simulating the delivery of the plan using measured delivery parameters to distribute the plan amongst 4DCT phases characterizing the patient breathing, (b) using an in-house Monte Carlo simulation (MC) dose calculator to determine the dose delivered to each breathing phase, and (c) accumulating the doses from the various breathing phases onto a single phase for evaluation. The accumulation was performed by individually transferring the energy and mass of dose-grid subvoxels, a technique that models the transfer of dose in a more physically realistic manner. The calculator was run on three test cases, with lung, esophagus, and liver targets, respectively, to assess the various uncertainties in the beam delivery simulation as well as to characterize the dose-accumulation technique. RESULTS: Four-dimensional doses were successfully computed for the three test cases with computation times ranging from 4-6 min on a server with eight NVIDIA Titan X graphics cards; the most time-consuming component was the MC dose engine. The subvoxel-based dose-accumulation technique produced stable 4D-dose distributions at subvoxel scales of 0.5-1.0 mm without impairing the total computation time. The uncertainties in the beam delivery simulation led to moderate variations of the dose-volume histograms for these cases; the variations were reduced by implementing repainting or phase-gating motion mitigation techniques in the calculator. CONCLUSIONS: A MC-based and GPU-accelerated 4D-dose calculator was developed to estimate the effects of respiratory motion on pencil beam scanning proton therapy treatments. After future validation, the calculator could be used to assess treatment plans and its quick runtime would make it easily usable in a future 4D-robust optimization system.

A robust intensity modulated proton therapy optimizer based on Monte Carlo dose calculation.

PURPOSE: Accuracy of dose calculation models and robustness under various uncertainties are key factors influencing the quality of intensity modulated proton therapy (IMPT) plans. To mitigate the effects of uncertainties and to improve the dose calculation accuracy, an all-scenario robust IMPT optimization based on accurate Monte Carlo (MC) dose calculation was developed. METHODS: In the all-scenario robust IMPT optimization, dose volume histograms (DVHs) were computed for the nominal case and for each uncertainty scenario. All scenarios were weighted by DVH values dynamically throughout optimization iterations. In contrast, probabilistic approach weighted scenarios with fixed scenario weights and worst case optimizations picked one single scenario - the worst scenario for each iteration. Uncertainties in patient setup and proton range were considered in all clinical cases studied. Graphics processing unit (GPU) computation was employed to reduce the computational time in both the MC dose calculation and optimization stages. A previously published adaptive quasi-Newton method for proton optimization was extended to include robustness. To validate the all-scenario algorithm extension, it was compared with the single scenario optimization target volume (OTV) based approach in clinical cases of three different disease sites. Additional comparisons with worst case optimization methods were conducted to evaluate the performance of the all-scenario robust optimization against other robust optimizations. RESULTS: The all-scenario robust IMPT optimization spared organs at risk (OARs) better than the OTV-based method while maintaining target coverage and improving the robustness of targets and OARs. Compared with composite and voxel-wise worst case optimization, the all-scenario robust optimization converged faster, and arrived at solutions of tighter DVH robustness spread, better target coverage and lower OAR dose. CONCLUSION: An all-scenario robust IMPT treatment planning system was developed using an adaptive quasi-Newton optimization method. The optimization system was GPU-accelerated and based on MC dose calculation. Improved performance was observed in clinical cases when compared with worst case optimization methods.

Clinically Applicable Monte Carlo-based Biological Dose Optimization for the Treatment of Head and Neck Cancers With Spot-Scanning Proton Therapy.

PURPOSE: Our aim is to demonstrate the feasibility of fast Monte Carlo (MC)-based inverse biological planning for the treatment of head and neck tumors in spot-scanning proton therapy. METHODS AND MATERIALS: Recently, a fast and accurate graphics processor unit (GPU)-based MC simulation of proton transport was developed and used as the dose-calculation engine in a GPU-accelerated intensity modulated proton therapy (IMPT) optimizer. Besides dose, the MC can simultaneously score the dose-averaged linear energy transfer (LETd), which makes biological dose (BD) optimization possible. To convert from LETd to BD, a simple linear relation was assumed. By use of this novel optimizer, inverse biological planning was applied to 4 patients, including 2 small and 1 large thyroid tumor targets, as well as 1 glioma case. To create these plans, constraints were placed to maintain the physical dose (PD) within 1.25 times the prescription while maximizing target BD. For comparison, conventional intensity modulated radiation therapy (IMRT) and IMPT plans were also created using Eclipse (Varian Medical Systems) in each case. The same critical-structure PD constraints were used for the IMRT, IMPT, and biologically optimized plans. The BD distributions for the IMPT plans were obtained through MC recalculations. RESULTS: Compared with standard IMPT, the biologically optimal plans for patients with small tumor targets displayed a BD escalation that was around twice the PD increase. Dose sparing to critical structures was improved compared with both IMRT and IMPT. No significant BD increase could be achieved for the large thyroid tumor case and when the presence of critical structures mitigated the contribution of additional fields. The calculation of the biologically optimized plans can be completed in a clinically viable time (<30 minutes) on a small 24-GPU system. CONCLUSIONS: By exploiting GPU acceleration, MC-based, biologically optimized plans were created for small-tumor target patients. This optimizer will be used in an upcoming feasibility trial on LETd painting for radioresistant tumors.

A GPU-accelerated and Monte Carlo-based intensity modulated proton therapy optimization system.

PURPOSE: Conventional spot scanning intensity modulated proton therapy (IMPT) treatment planning systems (TPSs) optimize proton spot weights based on analytical dose calculations. These analytical dose calculations have been shown to have severe limitations in heterogeneous materials. Monte Carlo (MC) methods do not have these limitations; however, MC-based systems have been of limited clinical use due to the large number of beam spots in IMPT and the extremely long calculation time of traditional MC techniques. In this work, the authors present a clinically applicable IMPT TPS that utilizes a very fast MC calculation. METHODS: An in-house graphics processing unit (GPU)-based MC dose calculation engine was employed to generate the dose influence map for each proton spot. With the MC generated influence map, a modified least-squares optimization method was used to achieve the desired dose volume histograms (DVHs). The intrinsic CT image resolution was adopted for voxelization in simulation and optimization to preserve spatial resolution. The optimizations were computed on a multi-GPU framework to mitigate the memory limitation issues for the large dose influence maps that resulted from maintaining the intrinsic CT resolution. The effects of tail cutoff and starting condition were studied and minimized in this work. RESULTS: For relatively large and complex three-field head and neck cases, i.e., >100,000 spots with a target volume of ∼ 1000 cm(3) and multiple surrounding critical structures, the optimization together with the initial MC dose influence map calculation was done in a clinically viable time frame (less than 30 min) on a GPU cluster consisting of 24 Nvidia GeForce GTX Titan cards. The in-house MC TPS plans were comparable to a commercial TPS plans based on DVH comparisons. CONCLUSIONS: A MC-based treatment planning system was developed. The treatment planning can be performed in a clinically viable time frame on a hardware system costing around 45,000 dollars. The fast calculation and optimization make the system easily expandable to robust and multicriteria optimization.