ArticleJournal of applied clinical medical physics2026
Deep learning-based auto-contouring for organs at risk in three-dimensional image-guided brachytherapy for cervical cancer and endometrial cancer.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAutomatic contouring can reduce the time required for delineating organs at risk (OARs) in brachytherapy planning and minimize interobserver variability. PURPOSE: This study aimed to develop and evaluate a deep learning-based automatic contouring model for OARs in three-dimensional image-guided brachytherapy (3D-IGBT) for cervical and endometrial cancer, including cases with interstitial needles.
methodsThe dataset comprised 100 patients (140 cases) with cervical or endometrial cancer who underwent 3D-IGBT. Interstitial needles were used in 74 cases. The nnU-Net model was trained (80 patients, 80 cases) and tested (20 patients, 60 cases). The OARs considered were the bladder, small bowel, rectum, and sigmoid. Ground truth (GT) contours were manually delineated by radiation oncologists and medical physicists. Evaluation included measuring inference time and assessing geometric agreement using dice similarity coefficient (DSC), surface DSC (sDSC), Hausdorff distance (HD), and 95th percentile HD (95HD). These metrics were also calculated for a combined structure of the rectum and sigmoid (Rec+Sig). Furthermore, D
resultsMean processing time was 30.3 s per case. Mean DSC values for the bladder, small bowel, rectum, sigmoid, and Rec+Sig were 0.96, 0.79, 0.83, 0.76, and 0.87, respectively. Mean 95HD values (mm) were 4.01, 18.8, 13.6, 25.5, and 17.8, respectively; ΔD
conclusionsThe proposed deep learning model demonstrated potential for application in cases involving interstitial needles and may contribute to improving the efficiency of the treatment planning workflow.
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