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Background: Automated catheter localization for ultrasound guided high‐dose‐rate prostate brachytherapy faces challenges relating to imaging noise and artifacts. To date, catheter reconstruction during the clinical procedure is performed manually. Deep learning has been successfully applied to a wide variety of complex tasks and has the potential to tackle the unique challenges associated with multiple catheter localization on ultrasound. Such a task is well suited for automation, with the potential to improve productivity and reliability. Purpose: We developed a deep learning model for automated catheter reconstruction and investigated potential factors influencing model performance. The model was designed to integrate into a clinical workflow, with a proposed reconstruction confidence metric to aid in planner verification. Methods: Datasets from 242 patients treated from 2016 to 2020 were collected retrospectively. The anonymized dataset comprises 31,000 transverse images reconstructed from 3D sagittal ultrasound acquisitions and 3500 implanted catheters manually localized by the planner. Each catheter was retrospectively ranked based on the severity of imaging artifacts affecting reconstruction difficulty. The U‐NET deep learning architecture was trained to localize implanted catheters on transverse images. A fivefold cross‐validation method was used, allowing for evaluation over the entire dataset. The postprocessing software combined the predictions with patient‐specific implant information to reconstructed catheters in 3D space, uniquely matched to the implanted grid positions. A reconstruction confidence metric was calculated based on the number and probability of localized predictions per catheter. For each patient, deep learning prediction and postprocessing reconstruction were completed in under 2 min on a nonperformance PC. Results: Overall, 80% of catheter reconstructions were accurate, within 2 mm along 90% of the length. The catheter tip was often not detected and required extrapolation during reconstruction. The reconstruction accuracy was 89% for the easiest catheter ranking and decreased to 13% for the highest difficulty ranking, when the aid of live ultrasound would have been recommended. Even when limited to the easiest ranked catheters, the reconstruction accuracy decreased at distal grid positions, down to 50%. Individual implantation style was found to influence the frequency of severe artifacts, slightly impacting the model accuracy. A reconstruction confidence metric identified the difficult catheters, removed the observed individual variation, and increased the overall accuracy to 91% while excluding 27% of the reconstructions. Conclusions: The deep learning model localized implanted catheters over a large clinical dataset, with overall promising results. The model faced challenges due to ultrasound artifacts and image degradation distal to the probe, underlining the continued importance of maintaining image quality and minimizing artifacts. A potential workflow for integration into the clinical procedure was demonstrated, including the use of a confidence metric to predict low accuracy reconstructions. Comparison between models evaluated on different datasets should also consider underlying differences, such as the frequency and severity of imaging artifacts. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: To determine the current equipment, technology, and treatment planning methods used in Canadian cancer centers for intrauterine cervix brachytherapy. Methods and Materials: A questionnaire was developed to survey medical physicists in Canada regarding technical aspects of intrauterine cervix brachytherapy. A response was obtained from one physicist at each radiotherapy facility in Canada. Results: A total of 32 of the 41 Canadian radiotherapy facilities perform intrauterine brachytherapy. Most (88%) use high-dose-rate brachytherapy. Images used for treatment planning are two-dimensional (2D) X-rays (63%), CT (66%), MRI (13%), and cone beam CT (9%). Patients are moved to another room to obtain images at 66% of the centers. Dose is prescribed to a volume at 28% of centers, and dose–volume histogram information is used in evaluating dose to the organs at risk (47%) and target (31%). Manual optimization was the most common optimization method (81%). A total of 69% of the institutions made significant changes within the past 5 years, and 66% plan major changes within the next 2 years. Conclusions: Intrauterine brachytherapy treatment for cervical cancer is rapidly evolving in Canada, with centers moving toward 3D image-based methods. Often these imaging modalities are not located in the brachytherapy room, so studies on immobilization and verification would be useful. Access to MRI is increasing, but remains low, correlating with a low adoption of volume-based parameters for evaluating target coverage. National treatment guidelines would be useful for centers making the transition from 2D to 3D methods and for encouraging access to MRI. [Copyright &y& Elsevier]