Stoker, J.

AbstractBackgroundBolus materials have been used for decades in radiotherapy. Most frequently, these materials are utilized to bring dose closer to the skin surface to cover superficial targets optimally. While cavity filling, such as nasal cavities, is desirable, traditional commercial bolus is lacking, requiring other solutions. Recently, investigators have worked on utilizing 3D printing technology, including commercially available solutions, which can overcome some challenges with traditional bolus.PurposeTo utilize failure modes and effects analysis (FMEA) to successfully implement a comprehensive 3D printed bolus solution to replace commercial bolus in our clinic using a series of open‐source (or free) software products.Methods3D printed molds for bespoke bolus were created by exporting the DICOM structures of the bolus designed in the treatment planning system and manipulated to create a multipart mold for 3D printing. A silicone (Ecoflex 00–30) mixture is poured into the mold and cured to form the bolus. Molds for sheet bolus of five thicknesses were also created. A comprehensive FMEA was performed to guide workflow adjustments and QA steps.ResultsThe process map identified 39 and 30 distinct steps for the bespoke and flat sheet bolus workflows, respectively. The corresponding FMEA highlighted 119 and 86 failure modes, with 69 shared between the processes. Misunderstanding of plan intent was a potential cause for most of the highest‐scoring failure modes, indicating that physics and dosimetry involvement early in the process is paramount.ConclusionFMEA informed the design and implementation of QA steps to guarantee a safe and high‐quality comprehensive implementation of silicone bolus from 3D printed molds. This approach allows for greater adaptability not afforded by traditional bolus, as well as potential dissemination to other clinics due to the open‐source nature of the workflow.

Abstract Purpose . To enhance an in-house graphic-processing-unit accelerated virtual particle (VP)-based Monte Carlo (MC) proton dose engine (VPMC) to model aperture blocks in both dose calculation and optimization for pencil beam scanning proton therapy (PBSPT)-based stereotactic radiosurgery (SRS). Methods and materials . A module to simulate VPs passing through patient-specific aperture blocks was developed and integrated in VPMC based on simulation results of realistic particles (primary protons and their secondaries). To validate the aperture block module, VPMC was first validated by an opensource MC code, MCsquare, in eight water phantom simulations with 3 cm thick brass apertures: four were with aperture openings of 1, 2, 3, and 4 cm without a range shifter, while the other four were with same aperture opening configurations with a range shifter of 45 mm water equivalent thickness. Then, VPMC was benchmarked with MCsquare and RayStation MC for 10 patients with small targets (average volume 8.4 c.c. with range of 0.4–43.3 c.c.). Finally, 3 typical patients were selected for robust optimization with aperture blocks using VPMC. Results . In the water phantoms, 3D gamma passing rate (2%/2 mm/10%) between VPMC and MCsquare was 99.71 ± 0.23%. In the patient geometries, 3D gamma passing rates (3%/2 mm/10%) between VPMC/MCsquare and RayStation MC were 97.79 ± 2.21%/97.78 ± 1.97%, respectively. Meanwhile, the calculation time was drastically decreased from 112.45 ± 114.08 s (MCsquare) to 8.20 ± 6.42 s (VPMC) with the same statistical uncertainties of ~0.5%. The robustly optimized plans met all the dose–volume-constraints (DVCs) for the targets and OARs per our institutional protocols. The mean calculation time for 13 influence matrices in robust optimization by VPMC was 41.6 s and the subsequent on-the-fly ‘trial-and-error’ optimization procedure took only 71.4 s on average for the selected three patients. Conclusion . VPMC has been successfully enhanced to model aperture blocks in dose calculation and optimization for the PBSPT-based SRS.

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.

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.

PurposeTo commission an open source Monte Carlo (MC) dose engine, “MCsquare” for a synchrotron‐based proton machine, integrate it into our in‐house C++‐based I/O user interface and our web‐based software platform, expand its functionalities, and improve calculation efficiency for intensity‐modulated proton therapy (IMPT).MethodsWe commissioned MCsquare using a double Gaussian beam model based on in‐air lateral profiles, integrated depth dose of 97 beam energies, and measurements of various spread‐out Bragg peaks (SOBPs). Then we integrated MCsquare into our C++‐based dose calculation code and web‐based second check platform “DOSeCHECK.” We validated the commissioned MCsquare based on 12 different patient geometries and compared the dose calculation with a well‐benchmarked GPU‐accelerated MC (gMC) dose engine. We further improved the MCsquare efficiency by employing the computed tomography (CT) resampling approach. We also expanded its functionality by adding a linear energy transfer (LET)‐related model‐dependent biological dose calculation.ResultsDifferences between MCsquare calculations and SOBP measurements were <2.5% (<1.5% for ~85% of measurements) in water. The dose distributions calculated using MCsquare agreed well with the results calculated using gMC in patient geometries. The average 3D gamma analysis (2%/2 mm) passing rates comparing MCsquare and gMC calculations in the 12 patient geometries were 98.0 ± 1.0%. The computation time to calculate one IMPT plan in patients’ geometries using an inexpensive CPU workstation (Intel Xeon E5‐2680 2.50 GHz) was 2.3 ± 1.8 min after the variable resolution technique was adopted. All calculations except for one craniospinal patient were finished within 3.5 min.ConclusionsMCsquare was successfully commissioned for a synchrotron‐based proton beam therapy delivery system and integrated into our web‐based second check platform. After adopting CT resampling and implementing LET model‐dependent biological dose calculation capabilities, MCsquare will be sufficiently efficient and powerful to achieve Monte Carlo‐based and LET‐guided robust optimization in IMPT, which will be done in the future studies.