Morales, Danairis Hernandez

Background/Objectives: Adjuvant radiation for gynecologic malignancies often exposes organs at risk (OARs), such as the bone marrow, bowel, rectum, and bladder, to radiation, leading to toxicities that impact treatment tolerance and patient quality of life. Scanning proton beam therapy, particularly with Individual Field Simultaneous Optimization (IFSO), may offer dosimetric and biological advantages over volumetric modulated arc therapy (VMAT). This study evaluates the clinical impact of IFSO-based proton planning in post-operative gynecologic cancer patients. Materials and Methods: Fourteen patients receiving adjuvant proton therapy to 45 Gy in 25 fractions were retrospectively analyzed. Comparison VMAT plans were generated on the same datasets. Dose–volume metrics for key OARs and normal tissue complication probabilities (NTCPs) were compared using paired statistical tests. Robustness evaluations accounted for setup and range uncertainties. Results: Proton plans significantly reduced dose to bone marrow (V10Gy: 58% vs. 86%, p < 0.00001; V20Gy: 47% vs. 58%, p < 0.00001), small bowel (V20Gy: 21% vs. 56%, p < 0.00001), and femoral heads (left femoral head mean: 11Gy vs. 13Gy, p = 0.032; right femoral head mean: 11Gy vs. 13Gy, p = 0.022). NTCP modeling predicted significantly lower rates of bowel urgency (9.4% vs. 3.3%, p < 0.001) and hematologic toxicity (10.2% vs. 4.9%, p < 0.001) with proton therapy. Plans remained robust across uncertainty scenarios. Conclusions: IFSO-based scanning proton therapy provides clinically meaningful sparing of bone marrow and bowel, with the potential to reduce hematologic and gastrointestinal toxicities. These findings support its use in patients receiving adjuvant pelvic radiotherapy, particularly those undergoing extended field treatment or chemotherapy.

AbstractBackgroundIn proton therapy dose calculation, Monte Carlo (MC) simulations are superior in accuracy but more time consuming, compared to analytical calculations. Graphic processing units (GPUs) are effective in accelerating MC simulations but may suffer thread divergence and racing condition in GPU threads that degrades the computing performance due to the generation of secondary particles during nuclear reactions.PurposeA novel concept of virtual particle (VP) MC (VPMC) is proposed to avoid simulating secondary particles in GPU‐accelerated proton MC dose calculation and take full advantage of the computing power of GPU.MethodsNeutrons and gamma rays were ignored as escaping from the human body; doses of electrons, heavy ions, and nuclear fragments were locally deposited; the tracks of deuterons were converted into tracks of protons. These particles, together with primary and secondary protons, are considered to be the realistic particles. Histories of primary and secondary protons were replaced by histories of multiple VPs. Each VP corresponded to one proton (either primary or secondary). A continuous‐slowing‐down‐approximation model, an ionization model, and a large angle scattering event model corresponding to nuclear interactions were developed for VPs by generating probability distribution functions (PDFs) based on simulation results of realistic particles using MCsquare. For efficient calculations, these PDFs were stored in the Compute Unified Device Architecture textures. VPMC was benchmarked with TOPAS and MCsquare in phantoms and with MCsquare in 13 representative patient geometries. Comparisons between the VPMC calculated dose and dose measured in water during patient‐specific quality assurance (PSQA) of the selected 13 patients were also carried out. Gamma analysis was used to compare the doses derived from different methods and calculation efficiencies were also compared.ResultsIntegrated depth dose and lateral dose profiles in both homogeneous and inhomogeneous phantoms all matched well among VPMC, TOPAS, and MCsquare calculations. The 3D–3D gamma passing rates with a criterion of 2%/2 mm and a threshold of 10% was 98.49% between MCsquare and TOPAS and 98.31% between VPMC and TOPAS in homogeneous phantoms, and 99.18% between MCsquare and TOPAS and 98.49% between VPMC and TOPAS in inhomogeneous phantoms, respectively. In patient geometries, the 3D–3D gamma passing rates with 2%/2 mm/10% between dose distributions from VPMC and MCsquare were 98.56 ± 1.09% in patient geometries. The 2D–3D gamma analysis with 3%/2 mm/10% between the VPMC calculated dose distributions and the 2D measured planar dose distributions during PSQA was 98.91 ± 0.88%. VPMC calculation was highly efficient and took 2.84 ± 2.44 s to finish for the selected 13 patients running on four NVIDIA Ampere GPUs in patient geometries.ConclusionVPMC was found to achieve high accuracy and efficiency in proton therapy dose calculation.

AbstractPurposeTo develop an online graphic processing unit (GPU)‐accelerated Monte Carlo‐based adaptive radiation therapy (ART) workflow for pencil beam scanning (PBS) proton therapy to address interfraction anatomical changes in patients treated with PBS.Methods and MaterialsA four‐step workflow was developed using our in‐house developed GPU‐accelerated Monte Carlo‐based treatment planning system to implement online Monte Carlo‐based ART for PBS. The first step conducts diffeomorphic demon‐based deformable image registration (DIR) to propagate contours on the initial planning CT (pCT) to the verification CT (vCT) to form a new structure set. The second step performs forward dose calculation of the initial plan on the vCT with the propagated contours after manual approval (possible modifications involved). The third step triggers a reoptimization of the plan depending on whether the verification dose meets the clinical requirements or not. A robust evaluation will be done for both the verification plan in the second step and the reopotimized plan in the third step. The fourth step involves a two‐stage (before and after delivery) patient‐specific quality assurance (PSQA) of the reoptimized plan. The before‐delivery PSQA is to compare the plan dose to the dose calculated using an independent fast open‐source Monte Carlo code, MCsquare. The after‐delivery PSQA is to compare the plan dose to the dose recalculated using the log file (spot MU, spot position, and spot energy) collected during the delivery. Jaccard index (JI), dice similarity coefficients (DSCs), and Hausdorff distance (HD) were used to assess the quality of the propagated contours in the first step. A commercial plan evaluation software, ClearCheck™, was integrated into the workflow to carry out efficient plan evaluation. 3D Gamma analysis was used during the fourth step to ensure the accuracy of the plan dose from reoptimization. Three patients with three different disease sites were chosen to evaluate the feasibility of the online ART workflow for PBS.ResultsFor all three patients, the propagated contours were found to have good volume conformance [JI (lowest‐highest: 0.833–0.983) and DSC (0.909–0.992)] but suboptimal boundary coincidence [HD (2.37–20.76 mm)] for organs‐at‐risk. The verification dose evaluated by ClearCheck™ showed significant degradation of the target coverage due to the interfractional anatomical changes. Reoptimization on the vCT resulted in great improvement of the plan quality to a clinically acceptable level. 3D Gamma analyses of PSQA confirmed the accuracy of the plan dose before delivery (mean Gamma index = 98.74% with a threshold of 2%/2 mm/10%), and after delivery based on the log files (mean Gamma index = 99.05% with a threshold of 2%/2 mm/10%). The average time cost for the complete execution of the workflow was around 858 s, excluding the time for manual intervention.ConclusionThe proposed online ART workflow for PBS was demonstrated to be efficient and effective by generating a reoptimized plan that significantly improved the plan quality.

AbstractPurposeTo measure diode sensitivity degradation (DSD) induced by cumulative proton dose delivered to a commercial daily quality assurance (QA) device.MethodsAt our institution, six Daily QA 3 (DQA3, Sun Nuclear Corporation, Melbourne, FL, USA) devices have been used for daily proton pencil beam scanning QA in four proton gantry rooms over a span of 4 years. DQA3 diode counts were cross‐calibrated using a homogenous field with a known dose of 1 Gy. The DSD rate (ΔR%/100 Gy) was calculated using linear regression on time‐series plots of diode counts and an estimate of cumulative dose per year based on the cross‐calibration. The effect of DSD on daily QA spot position measurements was quantified by converting DSD to baseline spot position shift.ResultsThe average dose delivered to the four inner DQA3 diodes was 104 ± 5 Gy/year, and the rate of DSD was ‐5.1% ± 1.0/100 Gy with the exception of one DQA3 device that had a significantly higher rate of DSD (–12%/100 Gy). The R2s of the linear fit to time‐series plots were between 0.92 and 0.98. The DSD rates were not constant but decreases with accumulated doses. The four center diodes, which received 40% of the cumulative dose received by inner diodes, had a DSD rate of –7.2% ± 0.9/100 Gy. For our daily QA program, 1 year of DSD was equivalent to a 0.2 mm shift in spot position.ConclusionsThe DSD rate of DQA3 diodes determined by long‐term proton daily QA data was about –5%/100 Gy, which is more than 10 times greater than the reported DSD rate from photon irradiation. DQA3 diodes may be used for daily proton QA programs, provided that they are recalibrated at an appropriate frequency that should be determined specifically for different daily QA programs.

Abstract Purpose The multiple energy extraction (MEE) delivery technique for synchrotron‐based proton delivery systems reduces beam delivery time by decelerating the beam multiple times during one accelerator spill, but this might cause additional plan quality degradation due to intrafractional motion. We seek to determine whether MEE causes significantly different plan quality degradation compared to single energy extraction (SEE) for lung cancer treatments due to the interplay effect. Methods Ten lung cancer patients treated with IMPT at our institution were nonrandomly sampled based on a representative range of tumor motion amplitudes, tumor volumes, and respiratory periods. Dose‐volume histogram (DVH) indices from single‐fraction SEE and MEE four‐dimensional (4D) dynamic dose distributions were compared using the Wilcoxon signed‐rank test. Distributions of monitor units (MU) to breathing phases were investigated for features associated with plan quality degradation. SEE and MEE DVH indices were compared in fractionated deliveries of the worst‐case patient treatment scenario to evaluate the impact of fractionation. Results There were no clinically significant differences in target mean dose, target dose conformity, or dose to organs‐at‐risk between SEE and MEE in single‐fraction delivery. Three patients had significantly worse dose homogeneity with MEE compared to SEE (single‐fraction mean D 5% ‐D 95% increased by up to 9.6% of prescription dose), and plots of MU distribution to breathing phases showed synchronization patterns with MEE but not SEE. However, after 30 fractions the patient in the worst‐case scenario had clinically acceptable target dose homogeneity and coverage with MEE (mean D 5% –D 95% increased by 1% compared to SEE). Conclusions For some patients with breathing periods close to the mean spill duration, MEE resulted in significantly worse single‐fraction target dose homogeneity compared to SEE due to the interplay effect. However, this was mitigated by fractionation, and target dose homogeneity and coverage were clinically acceptable after 30 fractions with MEE.

Purpose: To describe and validate the dose calculation algorithm of an independent second-dose check software for spot scanning proton delivery systems with full width at half maximum between 5 and 14 mm and with a negligible spray component. Methods: The analytical dose engine of our independent second-dose check software employs an altered pencil beam algorithm with 3 lateral Gaussian components. It was commissioned using Geant4 and validated by comparison to point dose measurements at several depths within spread-out Bragg peaks of varying ranges, modulations, and field sizes. Water equivalent distance was used to compensate for inhomogeneous geometry. Twelve patients representing different disease sites were selected for validation. Dose calculation results in water were compared to a fast Monte Carlo code and ionization chamber array measurements using dose planes and dose profiles as well as 2-dimensional–3-dimensional and 3-dimensional–3-dimensional γ-index analysis. Results in patient geometry were compared to Monte Carlo simulation using dose–volume histogram indices, 3-dimensional–3-dimensional γ-index analysis, and inpatient dose profiles. Results: Dose engine model parameters were tuned to achieve 1.5% agreement with measured point doses. The in-water γ-index passing rates for the 12 patients using 3%/2 mm criteria were 99.5% ± 0.5% compared to Monte Carlo. The average inpatient γ-index analysis passing rate compared to Monte Carlo was 95.8% ± 2.9%. The average difference in mean dose to the clinical target volume between the dose engine and Monte Carlo was −0.4% ± 1.0%. For a typical plan, dose calculation time was 2 minutes on an inexpensive workstation. Conclusions: Following our commissioning process, the analytical dose engine was validated for all treatment sites except for the lung or for calculating dose–volume histogram indices involving point doses or critical structures immediately distal to target volumes. Monte Carlo simulations are recommended for these scenarios.

PurposeAt our institution, all proton patient plans undergo patient‐specific quality assurance (PSQA) prior to treatment delivery. For intensity‐modulated proton beam therapy, quality assurance is complex and time consuming, and it may involve multiple measurements per field. We reviewed our PSQA workflow and identified the steps that could be automated and developed solutions to improve efficiency.MethodsWe used the treatment planning system's (TPS) capability to support C# scripts to develop an Eclipse scripting application programming interface (ESAPI) script and automate the preparation of the verification phantom plan for measurements. A local area network (LAN) connection between our measurement equipment and shared database was established to facilitate equipment control, measurement data transfer, and storage. To improve the analysis of the measurement data, a Python script was developed to automatically perform a 2D–3D γ‐index analysis comparing measurements in the plane of a two‐dimensional detector array with TPS predictions in a water phantom for each acquired measurement.ResultsDevice connection via LAN granted immediate access to the plan and measurement information for downstream analysis using an online software suite. Automated scripts applied to verification plans reduced time from preparation steps by at least 50%; time reduction from automating γ‐index analysis was even more pronounced, dropping by a factor of 10. On average, we observed an overall time savings of 55% in completion of the PSQA per patient plan.ConclusionsThe automation of the routine tasks in the PSQA workflow significantly reduced the time required per patient, reduced user fatigue, and frees up system users from routine and repetitive workflow steps allowing increased focus on evaluating key quality metrics.