A prospective multicenter trial of deep learning auto-segmentation for organs at risk in thoracic radiotherapy.
Authors
Affiliations (11)
Affiliations (11)
- Department of Radiation Oncology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
- Department of Oncology, Institute of Integrative Oncology, Tianjin Union Medical Center, Nankai University School of Medicine, Tianjin, China.
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
- Perception Vision Medical Technology (PVmed), Guangzhou, China.
- Institute of Radiation Oncology, Cancer Center, Hubei Key Laboratory of Precision Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Department of Radiation Oncology, The Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China.
- Department of Radiation Oncology, The Cancer Center of The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong, China.
- Department of Thoracic Oncology, The Cancer Center of The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong, China.
- School of Pharmacy, Faculty of Medicine, Macau University of Science and Technology, Macau, China.
- Department of Radiation Therapy, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China.
- Department of Radiation Oncology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China. [email protected].
Abstract
Widespread clinical implementation of rapidly evolving auto-segmentation tools remains constrained by a scarcity of high-quality prospective evidence. Here we show the results of a prospective, multicenter, observational trial (NCT05787522) evaluating the clinical performance of a deep learning model (iCurveE) for artificial intelligence (AI)-assisted delineation of organs at risk (OARs) in thoracic and breast cancer radiotherapy. Computed tomography images from 500 patients across five centers are annotated by 37 physicians using manual, AI-generated, and AI-assisted methods. Eleven thoracic OARs are evaluated based on the primary endpoints of volumetric Dice similarity coefficient (vDSC) and contouring time, alongside secondary metrics including 95% Hausdorff Distance (HD95). We prospectively annotate 2,483 OAR sets (27,043 OARs): 993 manual, 497 AI-generated, and 993 AI-assisted. AI-assisted delineation achieves significantly better vDSC (mean, 0.902) and HD95 (mean, 5.20 mm) than manual delineation (mean vDSC, 0.857; mean HD95, 8.01 mm; p < 0.0001) while improving time efficiency by 81.63% (median: 10.0 vs. 55.0 min; p < 0.0001). AI-assisted delineation reduces performance variability across centers and physicians with varying expertise. This study validates the clinical applicability of AI-assisted delineation in improving delineation performance and promoting healthcare equity.