S1552
Physics - Autosegmentation
ESTRO 2026
Digital Poster 979 Improved Accuracy of Prostate CTV Autosegmentation Using Combined CT and MR Imaging: A Deep Learning Approach Chia-Yu Lai 1 , Yu-Te Wu 1 , Weir-Chiang You 2,3 , Tzu-Hsuan Chen 1 , Chi-Wen Jao 1,4 , Chun-Yi Lin 1 , Mau-Shin Chi 5 , Wei-Kai Lee 6 , Chia-Chi Wen 5 , Chung-Hsien Hsu 5 , Kai-Lin Yang 5,7 1 Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei City, Taiwan. 2 Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung City, Taiwan. 3 Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung City, Taiwan. 4 Department of Research, Shin Kong Wu Ho-Su Memorial Hospital, Taipei City, Taiwan. 5 Department of Radiation Therapy and Oncology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei City, Taiwan. 6 Brain Research Center, National Yang Ming Chiao Tung University, Taipei City, Taiwan. 7 School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan Purpose/Objective: Accurate delineation of the clinical target volume (CTV) is a crucial prerequisite for safe and effective radiotherapy in prostate cancer. However, compared with magnetic resonance imaging (MRI), computed tomography (CT) images have limited soft-tissue contrast, making it difficult to visualize the boundaries between adjacent structures. This study investigates the impact of integrating MRI with CT images on the performance of automatic CTV segmentation, compared with models trained and tested using CT images alone. Material/Methods: This retrospective study included 73 prostate cancer patients who underwent both CT and MRI before receiving definitive radiotherapy between 2013 and 2023. The ground truth contours were derived from RT Structure Set (RTSS) files and manually delineated by experienced radiation oncologists.The 3D U-Net model was trained for automatic segmentation of the prostate CTV. Patients were randomly divided into training and test sets in an 8:2 ratio. The models were optimized using the Generalized Dice loss function, trained for 500 epochs, and employed the Adam optimizer with a learning rate of .Performance was evaluated using the Dice similarity coefficient (DSC), recall, precision, and the 95th percentile Hausdorff distance (HD95). Results: For prostate CTV delineation, the model using combined CT and MRI inputs achieved a Dice similarity coefficient (DSC) of 0.76 ± 0.07, a recall of 0.71 ± 0.13, a precision of 0.84 ± 0.09, and an HD95 of 3.30 ± 1.88 mm. In comparison, the CT-only model yielded a DSC
Median vDSC values for stages 1, 2 and 3 time points were as follows: bladder (0.97, 0.99, 1.00), rectum (0.89, 0.99, 0.97), sigmoid (0.73, 0.89, 0.85), and bowel (0.91, 0.97, 0.98). Median sDSC values for stages 1, 2 and 3 were: bladder (0.95, 0.99, 1.00), rectum (0.89, 0.98, 0.95), sigmoid (0.74, 0.86, 0.79), and bowel (0.89, 0.97, 0.97). Significant differences were observed between stage 1 and stages 2 & 3 for all structures. Comparison between stage 1 and 3 revealed significant differences for all structures and metrics except for rectum (sDSC) and sigmoid (sDSC). With the exception of bladder, no differences were found between stages 2 and 3 for any structures or metric. Conclusion: The observed improvement in model performance between testing and later stages is likely primarily due to the difference in ground truth delineation methods: full manual delineation (model testing, stage 1) versus AI-structure editing (prospective evaluation and routine clinical use). The increase in quantitative scores confirms the model’s utility, as clinically- approved contours can be obtained with AI structure edits rather than full manual delineation. The equivalent performance observed between prospective evaluation and routine clinical use demonstrated consistent model accuracy and stability, with no clear evidence of user automation bias. Regular monitoring will continue. References: [1] Image-Based Deep Learning Enables the Reduction of Gastro-Intestinal Toxicity in Pelvic Radiotherapy, Thomas, C. (Author). 1 Oct 2023, Student thesis: Doctoral Thesis › Doctor of Philosophy[2] Implementation of in-house pelvic radiotherapy auto- contouring in real world clinical practice, Luis Ribeiro et. Al. RCR Global AI Conference 2025 Proceedings, 2025 Keywords: Autosegmentation, pelvis, metrics
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