CT-based auto-segmentation of multiple target volumes for all-in-one radiotherapy in rectal cancer patients.
Authors
Affiliations (14)
Affiliations (14)
- Cancer Center, Peking University Third Hospital, Beijing, 100191, China.
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, 100191, China.
- Beijing Key Laboratory for Interdisciplinary Research in Gastrointestinal Oncology (BLGO), Peking University Third Hospital, Beijing, 100191, China.
- Shanghai United Imaging Healthcare Co., Ltd, Shanghai, 201807, China.
- Department of Radiation Therapy, Peking University People's Hospital, Beijing, 100044, China.
- Department of Radiation Oncology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100005, China.
- Department of Oncology, Chengyang People's Hospital, Qingdao, 266109, China.
- Cancer Center, Peking University Third Hospital, Beijing, 100191, China. [email protected].
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, 100191, China. [email protected].
- Beijing Key Laboratory for Interdisciplinary Research in Gastrointestinal Oncology (BLGO), Peking University Third Hospital, Beijing, 100191, China. [email protected].
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, 100191, China. [email protected].
- Cancer Center, Peking University Third Hospital, Beijing, 100191, China. [email protected].
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, 100191, China. [email protected].
- Beijing Key Laboratory for Interdisciplinary Research in Gastrointestinal Oncology (BLGO), Peking University Third Hospital, Beijing, 100191, China. [email protected].
Abstract
This study aimed to evaluate the clinical feasibility and performance of CT-based auto-segmentation models integrated into an All-in-One radiotherapy workflow for rectal cancer. This study included 312 rectal cancer patients, with 272 used to train three nnU-Net models for CTV45, CTV50, and GTV segmentation, and 40 for evaluation across one internal (<i>n</i> = 10), one clinical AIO (<i>n</i> = 10), and two external cohorts (<i>n</i> = 10 each). Segmentation accuracy (DSC, HD, HD95, ASSD, ASD) and time efficiency were assessed. In the internal testing set, mean DSC of CTV45, CTV50, and GTV were 0.90, 0.86, and 0.71; HD were 17.08, 25.48, and 79.59 mm; HD 95 were 4.89, 7.33, and 56.49 mm; ASSD were 1.23, 1.90, and 6.69 mm; and ASD were 1.24, 1.58, and 11.61 mm. Auto-segmentation reduced manual delineation time by 63.3–88.3% (<i>p</i> < 0.0001). In clinical practice, average DSC of CTV45, CTV50 and GTV were 0.93, 0.88, and 0.78; HD were 13.56, 23.84, and 35.38 mm; HD 95 were 3.33, 6.46, and 21.34 mm; ASSD were 0.78, 1.49, and 3.30 mm; and ASD were 0.74, 1.18, and 2.13 mm. The results from the multi-center testing also showed applicability of these models, since the average DSC of CTV45 and GTV were 0.84 and 0.80 respectively. The models demonstrated high accuracy and clinical utility, effectively streamlining target volume delineation and reducing manual workload in routine practice. The study protocol was approved by the Institutional Review Board of Peking University Third Hospital (Approval No. (2024) Medical Ethics Review No. 182-01).