Deep learning‐based auto‐contouring for organs at risk in three‐dimensional image‐guided brachytherapy for cervical cancer and endometrial cancer

BACKGROUND: Automatic 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. METHODS: ) was evaluated. Differences in accuracy with or without interstitial needles were compared using Welch's t-test (significance level: p < 0.05). RESULTS: values (Gy) were 0.17, 0.53, 0.014, -0.073, and -0.045, respectively. No significant accuracy differences related to interstitial needles were observed for any of the OARs. CONCLUSIONS: The 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.