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Abstract Background Radiotherapy delivery regimens can vary between a single fraction (SF) and multiple fractions (MF) given daily for up to several weeks depending on the location of the cancer or metastases. With limited evidence comparing fractionation regimens for oligometastases, there is support to explore toxicity levels to nearby organs at risk as a primary outcome while using SF and MF stereotactic ablative radiotherapy (SABR) as well as explore differences in patient-reported quality of life and experience. Methods This study will randomize 598 patients in a 1:1 ratio between the standard arm (MF SABR) and the experimental arm (SF SABR). This trial is designed as two randomized controlled trials within one patient population for resource efficiency. The primary objective of the first randomization is to determine if SF SABR is non-inferior to MF SABR, with respect to healthcare provider (HCP)-reported grade 3-5 adverse events (AEs) that are related to SABR. Primary endpoint is toxicity while secondary endpoints include lesional control rate (LCR), and progression-free survival (PFS). The second randomization (BC Cancer sites only) will allocate participants to either complete quality of life (QoL) questionnaires only; or QoL questionnaires and a symptom-specific survey with symptom-guided HCP intervention. The primary objective of the second randomization is to determine if radiation-related symptom questionnaire-guided HCP intervention results in improved reported QoL as measured by the EuroQoL-5-dimensions-5levels (EQ-5D-5L) instrument. The primary endpoint is patient-reported QoL and secondary endpoints include: persistence/resolution of symptom reporting, QoL, intervention cost effectiveness, resource utilization, and overall survival. Discussion This study will compare SF and MF SABR in the treatment of oligometastases and oligoprogression to determine if there is non-inferior toxicity for SF SABR in selected participants with 1-5 oligometastatic lesions. This study will also compare patient-reported QoL between participants who receive radiation-related symptom-guided HCP intervention and those who complete questionnaires alone. Trial registration Clinicaltrials.gov identifier: NCT05784428. Date of Registration: 23 March 2023.

PurposeDeep learning-based auto-segmented contour (DC) models require high quality data for their development, and previous studies have typically used prospectively produced contours, which can be resource intensive and time consuming to obtain. The aim of this study was to investigate the feasibility of using retrospective peer-reviewed radiotherapy planning contours in the training and evaluation of DC models for lung stereotactic ablative radiotherapy (SABR).MethodsUsing commercial deep learning-based auto-segmentation software, DC models for lung SABR organs at risk (OAR) and gross tumor volume (GTV) were trained using a deep convolutional neural network and a median of 105 contours per structure model obtained from 160 publicly available CT scans and 50 peer-reviewed SABR planning 4D-CT scans from center A. DCs were generated for 50 additional planning CT scans from center A and 50 from center B, and compared with the clinical contours (CC) using the Dice Similarity Coefficient (DSC) and 95% Hausdorff distance (HD).ResultsComparing DCs to CCs, the mean DSC and 95% HD were 0.93 and 2.85mm for aorta, 0.81 and 3.32mm for esophagus, 0.95 and 5.09mm for heart, 0.98 and 2.99mm for bilateral lung, 0.52 and 7.08mm for bilateral brachial plexus, 0.82 and 4.23mm for proximal bronchial tree, 0.90 and 1.62mm for spinal cord, 0.91 and 2.27mm for trachea, and 0.71 and 5.23mm for GTV. DC to CC comparisons of center A and center B were similar for all OAR structures.ConclusionsThe DCs developed with retrospective peer-reviewed treatment contours approximated CCs for the majority of OARs, including on an external dataset. DCs for structures with more variability tended to be less accurate and likely require using a larger number of training cases or novel training approaches to improve performance. Developing DC models from existing radiotherapy planning contours appears feasible and warrants further clinical workflow testing.