Script-based implementation of automatic grid placement for lattice stereotactic body radiation therapy.

Background And Purpose: Spatially fractionated radiation therapy (SFRT) has demonstrated promising clinical response in treating large tumors with heterogeneous dose distributions. Lattice stereotactic body radiation therapy (SBRT) is an SFRT technique that leverages inverse optimization to precisely localize regions of high and lose dose within disease. The aim of this study was to evaluate an automated heuristic approach to sphere placement in lattice SBRT treatment planning.

A quality assurance framework for routine monitoring of deep learning cardiac substructure computed tomography segmentation models in radiotherapy.

Background: For autosegmentation models, the data used to train the model (e.g., public datasets and/or vendor-collected data) and the data on which the model is deployed in the clinic are typically not the same, potentially impacting the performance of these models by a process called domain shift.

trial of simulation-free hippocampal-avoidance whole brain adaptive radiotherapy.

Background And Purpose: Hippocampal-avoidance whole brain radiotherapy (HA-WBRT) can be a time-consuming process compared to conventional whole brain techniques, thus potentially limiting widespread utilization. Therefore, we evaluated the clinical feasibility, via dose-volume metrics and timing, by leveraging a computed tomography (CT)-based commercial adaptive radiotherapy (ART) platform and workflow in order to create and deliver patient-specific, simulation-free HA-WBRT.

Materials And Methods: Ten patients previously treated for central nervous system cancers with cone-beam computed tomography (CBCT) imaging were included in this study.

Prospective In Silico Evaluation of Cone-Beam Computed Tomography-Guided StereoTactic Adaptive Radiation Therapy (CT-STAR) for the Ablative Treatment of Ultracentral Thoracic Disease.

Purpose: We conducted a prospective, in silico study to evaluate the feasibility of cone-beam computed tomography (CBCT)-guided stereotactic adaptive radiation therapy (CT-STAR) for the treatment of ultracentral thoracic cancers (NCT04008537). We hypothesized that CT-STAR would reduce dose to organs at risk (OARs) compared with nonadaptive stereotactic body radiation therapy (SBRT) while maintaining adequate tumor coverage.

Methods And Materials: Patients who were already receiving radiation therapy for ultracentral thoracic malignancies underwent 5 additional daily CBCTs on the ETHOS system as part of a prospective imaging study.

A clinical and time savings evaluation of a deep learning automatic contouring algorithm.

Automatic contouring algorithms may streamline clinical workflows by reducing normal organ-at-risk (OAR) contouring time. Here we report the first comprehensive quantitative and qualitative evaluation, along with time savings assessment for a prototype deep learning segmentation algorithm from Siemens Healthineers. The accuracy of contours generated by the prototype were evaluated quantitatively using the Sorensen-Dice coefficient (Dice), Jaccard index (JC), and Hausdorff distance (Haus).

Clinical application of a template-guided automated planning routine.

Purpose: Determine the dosimetric quality and the planning time reduction when utilizing a template-based automated planning application.

Methods: A software application integrated through the treatment planning system application programing interface, QuickPlan, was developed to facilitate automated planning using configurable templates for contouring, knowledge-based planning structure matching, field design, and algorithm settings. Validations are performed at various levels of the planning procedure and assist in the evaluation of readiness of the CT image, structure set, and plan layout for automated planning.

Robustness of deep learning segmentation of cardiac substructures in noncontrast computed tomography for breast cancer radiotherapy.

Purpose: To develop and evaluate deep learning-based autosegmentation of cardiac substructures from noncontrast planning computed tomography (CT) images in patients undergoing breast cancer radiotherapy and to investigate the algorithm sensitivity to out-of-distribution data such as CT image artifacts.

Methods: Nine substructures including aortic valve (AV), left anterior descending (LAD), tricuspid valve (TV), mitral valve (MV), pulmonic valve (PV), right atrium (RA), right ventricle (RV), left atrium (LA), and left ventricle (LV) were manually delineated by a radiation oncologist on noncontrast CT images of 129 patients with breast cancer; among them 90 were considered in-distribution data, also named as "clean" data. The image/label pairs of 60 subjects were used to train a 3D deep neural network while the remaining 30 were used for testing.

Comprehensive validation of halcyon 2.0 plans and the implementation of patient specific QA with multiple detector platforms.

Purpose: To perform a comprehensive validation of plans generated on a preconfigured Halcyon 2.0 with preloaded beam model, including evaluations of new features and implementing the patient specific quality assurance (PSQA) process with multiple detectors.

Methods: A total of 56 plans were generated in Eclipse V15.

Technical Note: Dosimetric characterization of the dynamic beam flattening MLC sequence on a ring shaped, Jawless Linear Accelerator with double stacked MLC.

Purpose: To characterize the dosimetric features and limitations of the dynamic beam flattening (DBF) on the Halcyon 2.0 linear accelerator (Varian Medical Systems).

Methods: A predefined multi-leaf collimator (MLC) sequence was introduced and used to flatten the 6 MV flattening filter free (FFF) beam on the Halcyon 2.

Field-in-field breast planning for a jawless, double-stack MLC LINAC using flattening-filter-free beams.

Background: This study intends to develop an efficient field-in-field (FiF) planning technique with the Eclipse treatment planning system (TPS) to determine the feasibility of using the Halcyon treatment delivery system for 3D treatment of breast cancer.

Methods: Ten treatment plans were prepared on the Halcyon treatment planning system and compared to the same patients' clinically delivered TrueBeam plans which used flattened 6 MV and 10 MV beams. Patients selected for this study were treated via simple, tangential breast irradiation and did not receive radiotherapy of the supraclavicular or internal mammary lymph nodes.

Radiation Therapy Workflow and Dosimetric Analysis from a Phase 1/2 Trial of Noninvasive Cardiac Radioablation for Ventricular Tachycardia.

Purpose: A prospective phase 1/2 trial for electrophysiologic guided noninvasive cardiac radioablation treatment of ventricular tachycardia (ENCORE-VT) demonstrating efficacy for arrhythmia control has recently been reported. The treatment workflow, report dose-volume metrics, and overall process improvements are described here.

Methods And Materials: Patients receiving 25 Gy in a single fraction to the cardiac ventricular tachycardia substrate (identified on presimulation multimodality imaging) on the phase 1/2 trial were included for analysis.

In Silico Trial of MR-Guided Midtreatment Adaptive Planning for Hypofractionated Stereotactic Radiation Therapy in Centrally Located Thoracic Tumors.

Purpose: Hypofractionated (>5 fraction) stereotactic radiation therapy (HSRT) may allow for ablative biologically equivalent dose to tumors with a lower risk of organ-at-risk (OAR) toxicity in central thoracic tumors. Adaptive planning may further improve OAR sparing while maintaining planning target volume (PTV) coverage. We hypothesized that midtreatment adaptive replanning would offer dosimetric advantages during HSRT for central thorax malignancies using magnetic resonance imaging (MRI)-guided radiation therapy.