| Wang Kaiyue,Xue Xian,Sun Haitao,et al.Deep learning-based automatic segmentation of organs at risk in postoperative brachytherapy for endometrial carcinoma[J].Chinese Journal of Radiological Medicine and Protection,2025,45(10):958-965 |
| Deep learning-based automatic segmentation of organs at risk in postoperative brachytherapy for endometrial carcinoma |
| Received:September 19, 2024 |
| DOI：10.3760/cma.j.cn112271-20240919-00362 |
| KeyWords:Deep learning Endometrial neoplasm Brachytherapy Automatic segmentation Organs at risk(OARs) |
| FundProject:国家重点研发计划(2022YFC2404606)；北京市自然科学基金(Z210008)；北京大学第三医院院临床重点项目(BYSYZD202011) |
| Author Name | Affiliation | E-mail | | Wang Kaiyue | Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China | | | Xue Xian | Key Laboratory of Radiological Protection and Nuclear Emergency, China CDC, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beijing 100088, China | | | Sun Haitao | Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China | | | Jiang Ping | Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China | | | Wang Junjie | Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China | junjiewang_edu@sina.cn |
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| Abstract:: |
| Objective To develop and assess a deep learning-based model for automatic segmentation of organs at risk (OARs) in postoperative brachytherapy for endometrial carcinoma (EC). Methods A retrospective study was conducted on the computed tomography (CT) images of 108 EC patients who received high-dose-rate (HDR) 192Ir intracavitary vaginal-cuff brachytherapy (VCB) at the Peking University Third Hospital from November 2021 to October 2022. Then, the rectum, colon, small intestine, and bladder in these images were manually segmented. These patients were randomly divided into two groups using a random number table: 90 cases for training the 3D no-new-U-Net (nnU-Net) segmentation model and 18 cases for model testing. The precision and clinical applicability of the automatic segmentation model were assessed using geometric indexes including Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD), as well as dose-volume parameters (DVPs) including the minimum dose to 0.1, 1.0, and 2.0 cm3 of OARs that received the highest irradiation doses (D0.1 cm3, D1.0 cm3, and D2.0 cm3). Results The 3D nnU-Net model yielded mean DSC values of 0.90, 0.85, 0.88, and 0.95, respectively for the segmentations of the rectum, colon, small bowel, and bladder, all of which were better than those of the 3D U-Net and V-Net models. The differences among the three models were statistically significant (F = 21.78, 24.33, 36.00, 20.11, P < 0.001). The 3D nnU-Net exhibited statistically significant differences in HD values for the colon, small intestine, and bladder segmentations among the three method (F = 17.33, 24.11, 6.33, P < 0.05). The 3D nnU-Net model yielded lower MSD values for the segmentations of all organs compared to the control model, with statistically significant differences (F = 29.78, 27.11, 27.11, 14.78, P < 0.001). No statistically significant difference was found in all DVPs between the 3D nnU-Net model-based and manual segmentations (P > 0.05). Bland-Altman analysis demonstrated great consistency between the 3D nnU-Net and manual segmentations. Conclusions The 3D nnU-Net-based model exhibits high geometric accuracy and dosimetric consistency with manual segmentation of OARs in brachytherapy, holding potential to improve clinical efficiency. |
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