Tryggestad, E.

AbstractIntroductionThis study presents a comprehensive collision avoidance framework based on three‐dimension (3D) computer‐aided design (CAD) modeling, a graphical user interface (GUI) as peripheral to the radiation treatment planning (RTP) environment, and patient‐specific plan parameters for intensity‐modulated proton therapy (IMPT).MethodsA stand‐alone software application was developed leveraging the Varian scripting application programming interface (API) for RTP database object accessibility. The Collision Avoider software models the Hitachi ProBeat‐V half gantry design and the Kuka robotic couch with triangle mesh structures. Patient‐specific plan parameters are displayed in the collision avoidance software for potential proximity evaluation. The external surfaces of the patients and the immobilization devices are contoured based on computed tomography (CT) images. A “table junction‐to‐CT‐origin” (JCT) measurement is made for every patient at the time of CT simulation to accurately provide reference location of the patient contours to the treatment couch. Collision evaluations were performed virtually with the program during treatment planning to prevent four major types of collisional events: collisions between the gantry head and the treatment couch, gantry head and the patient's body, gantry head and the robotic arm, and collisions between the gantry head and the immobilization devices.ResultsThe Collision Avoider software was able to accurately model the proton treatment delivery system and the robotic couch position. Commonly employed clinical beam configuration and JCT values were investigated. Brain and head and neck patients require more complex gantry and patient positioning system configurations. Physical measurements were performed to validate 3D CAD model geometry. Twelve clinical proton treatment plans were used to validate the accuracy of the software. The software can predict all four types of collisional events in our clinic since its full implementation in 2020.ConclusionA highly efficient patient‐specific collision prevention program for scanning proton therapy has been successfully implemented. The graphical program has provided accurate collision detection since its inception at our institution.

Abstract Purpose: Outcomes for resistant metastatic castration-resistant prostate cancer (CRPC) are poor. Stereotactic ablative radiotherapy (SABR) induces antitumor immunity in clinical and preclinical studies, but immunologic biomarkers are lacking. Patients and Methods: Eighty-nine patients with oligometastatic CRPC were identified by 11C-Choline-PET (Choline-PET) from August 2016 to December 2019 and treated with SABR. Prespecified coprimary endpoints were 2-year overall survival (OS) and PSA progression. Secondary endpoints included 2-year SABR-treated local failure and 6-month adverse events. Correlative studies included peripheral blood T-cell subpopulations before and after SABR. Results: 128 lesions in 89 patients were included in this analysis. Median OS was 29.3 months, and 1- and 2-year OS were 96% and 80%, respectively. PSA PFS was 40% at 1 year and 21% at 2 years. Local PFS was 84.4% and 75.3% at 1 and 2 years, respectively, and no grade ≥3 AEs were observed. Baseline high levels of tumor-reactive T cells (TTR; CD8+CD11ahigh) predicted superior local, PSA, and distant PFS. Baseline high levels of effector memory T cells (TEM; CCR7−CD45RA−) were associated with improved PSA PFS. An increase in TTR at day 14 from baseline was associated with superior OS. Conclusions: This is the first comprehensive effector T-cell immunophenotype analysis in a phase II trial before and after SABR in CRPC. Results are favorable and support the incorporation of immune-based markers in the design of future randomized trials in patients with oligometastatic CRPC treated with SABR.

Background: Lower-dose cone-beam computed tomography protocols for image-guided radiotherapy may permit target localization while minimizing radiation exposure. We prospectively evaluated a lower-dose cone-beam protocol for central nervous system image-guided radiotherapy across a multinational pediatrics consortium. Methods: Seven institutions prospectively employed a lower-dose cone-beam computed tomography central nervous system protocol (weighted average dose 0.7 mGy) for patients ≤21 years. Treatment table shifts between setup with surface lasers versus cone-beam computed tomography were used to approximate setup accuracy, and vector magnitudes for these shifts were calculated. Setup group mean, interpatient, interinstitution, and random error were estimated, and clinical factors were compared by mixed linear modeling. Results: Among 96 patients, with 2179 pretreatment cone-beam computed tomography acquisitions, median age was 9 years (1-20). Setup parameters were 3.13, 3.02, 1.64, and 1.48 mm for vector magnitude group mean, interpatient, interinstitution, and random error, respectively. On multivariable analysis, there were no significant differences in mean vector magnitude by age, gender, performance status, target location, extent of resection, chemotherapy, or steroid or anesthesia use. Providers rated >99% of images as adequate or better for target localization. Conclusions: A lower-dose cone-beam computed tomography protocol demonstrated table shift vector magnitude that approximate clinical target volume/planning target volume expansions used in central nervous system radiotherapy. There were no significant clinical predictors of setup accuracy identified, supporting use of this lower-dose cone-beam computed tomography protocol across a diverse pediatric population with brain tumors.

AbstractPurposeThe aim of this work is to describe the clinical implementation of respiratory‐gated spot‐scanning proton therapy (SSPT) for the treatment of thoracic and abdominal moving targets. The experience of our institution is summarized, from initial acceptance and commissioning tests to the development of standard clinical operating procedures for simulation, motion assessment, motion mitigation, treatment planning, and gated SSPT treatment delivery.Materials and methodsA custom respiratory gating interface incorporating the Real‐Time Position Management System (RPM, Varian Medical Systems, Inc., Palo Alto, CA, USA) was developed in‐house for our synchrotron‐based delivery system. To assess gating performance, a motion phantom and radiochromic films were used to compare gated vs nongated delivery. Site‐specific treatment planning protocols and conservative motion cutoffs were developed, allowing for free‐breathing (FB), breath‐holding (BH), or phase‐gating (Ph‐G). Room usage efficiency of BH and Ph‐G treatments was retrospectively evaluated using beam delivery data retrieved from our record and verify system and DICOM files from patient‐specific quality assurance (QA) procedures.ResultsMore than 70 patients were treated using active motion management between the launch of our motion mitigation program in October 2015 and the end date of data collection of this study in January 2018. During acceptance procedures, we found that overall system latency is clinically‐suitable for Ph‐G. Regarding room usage efficiency, the average number of energy layers delivered per minute was <10 for Ph‐G, 10‐15 for BH and ≥15 for FB, making Ph‐G the slowest treatment modality. When comparing to continuous delivery measured during pretreatment QA procedures, the median values of BH treatment time were extended from 6.6 to 9.3 min (+48%). Ph‐G treatments were extended from 7.3 to 13.0 min (+82%).ConclusionsActive motion management has been crucial to the overall success of our SSPT program. Nevertheless, our conservative approach has come with an efficiency cost that is more noticeable in Ph‐G treatments and should be considered in decision‐making.

PurposeThe 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.MethodsTo 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.ResultsFour‐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.ConclusionsA 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.

Abstract Background and study aims Little consideration has been given to selection of endoscopic ultrasound-guided fiducials for proton radiotherapy and the resulting perturbations in the therapy dose and pattern. Our aim was to assess the impact of perturbations caused by six fiducials of different composition and dimensions in a phantom gel model. Materials and methods The phantom was submerged in a water bath and irradiated with a uniform 10 cm × 10 cm field of 119.7 MeV monoenergetic spot scanning protons delivered through a 45 mm range shifter. The proton “Bragg Peak” was evaluated. Results Dose perturbations manifesting as dose reductions up to 30 % were observed. A carbon composite (1 × 5 mm) and gold (0.4 × 10 mm) fiducial with backload potential rather than dedicated EUS pre-loaded gold fiducial needles had the best performance in terms of minimizing the dose perturbation. Conclusions Our data demonstrate that a carbon composite fiducial has a less untoward effect on proton therapy dose distribution than dedicated EUS pre-loaded gold fiducial needles. Such information is important to consider when selecting fiducials specifically for proton therapy.

PurposeTo accurately model the beam delivery time (BDT) for a synchrotron‐based proton spot scanning system using experimentally determined beam parameters.MethodsA model to simulate the proton spot delivery sequences was constructed, and BDT was calculated by summing times for layer switch, spot switch, and spot delivery. Test plans were designed to isolate and quantify the relevant beam parameters in the operation cycle of the proton beam therapy delivery system. These parameters included the layer switch time, magnet preparation and verification time, average beam scanning speeds in x‐ and y‐directions, proton spill rate, and maximum charge and maximum extraction time for each spill. The experimentally determined parameters, as well as the nominal values initially provided by the vendor, served as inputs to the model to predict BDTs for 602 clinical proton beam deliveries. The calculated BDTs (TBDT) were compared with the BDTs recorded in the treatment delivery log files (TLog): ∆t = TLog−TBDT.ResultsThe experimentally determined average layer switch time for all 97 energies was 1.91 s (ranging from 1.9 to 2.0 s for beam energies from 71.3 to 228.8 MeV), average magnet preparation and verification time was 1.93 ms, the average scanning speeds were 5.9 m/s in x‐direction and 19.3 m/s in y‐direction, the proton spill rate was 8.7 MU/s, and the maximum proton charge available for one acceleration is 2.0 ± 0.4 nC. Some of the measured parameters differed from the nominal values provided by the vendor. The calculated BDTs using experimentally determined parameters matched the recorded BDTs of 602 beam deliveries (∆t = −0.49 ± 1.44 s), which were significantly more accurate than BDTs calculated using nominal timing parameters (∆t = −7.48 ± 6.97 s).ConclusionsAn accurate model for BDT prediction was achieved by using the experimentally determined proton beam therapy delivery parameters, which may be useful in modeling the interplay effect and patient throughput. The model may provide guidance on how to effectively reduce BDT and may be used to identifying deteriorating machine performance.