Pan-mediastinal neoplasm diagnosis via nationwide federated learning: a multicentre cohort study

作者：Ruijie Tang, Hengrui Liang, Yuchen Guo, Zhigang Li, Zhichao Liu, Lin Xu, Zeping Yan, Jun Liu, Xin Xu, Wenlong Shao, Shuben Li, Wenhua Liang, Wei Wang, Fei Cui, Huanghe He, Chao Yang, Long Jiang, Haixuan Wang, Huai Chen, Chenguang Guo, Haipeng Zhang, Zebin Gao, Yuwei He, Xiangru Chen, Lei Zhao, Hong Yu, Jian Hu, Jiangang Zhao, Bin Li, Ci Yin, Wenjie Mao, Wanli Lin, Yujie Xie, Jixian Liu, Xiaoqiang Li, Dingwang Wu, Qinghua Hou, Yongbing Chen, Donglai Chen, Yuhang Xue, Yi Liang, Wen‐Fang Tang, Qi Wang, Encheng Li, Hongxu Liu, Guan Wang, Pingwen Yu, Chun Chen, Bin Zheng, Hao Chen, Zhe Zhang, Lunqing Wang, Ailin Wang, Zongqi Li, Junke Fu, Guangjian Zhang, Jia Zhang, Bohao Liu, Jian Zhao, Boyun Deng, Yongtao Han, Xuefeng Leng, Zhiyu Li, Man Zhang, Changling Liu, Tianhu Wang, Zhilin Luo, Chenglin Yang, Xiaotong Guo, Kai Ma, Lixu Wang, Wenjun Jiang, Xu Han, Qing Wang, Kun Qiao, Zhaohua Xia, Shuo Zheng, Chenyang Xu, Jidong Peng, Shilong Wu, Zhifeng Zhang, Haoda Huang, Dazhi Pang, Qiao Liu, Jinglong Li, Xueru Ding, Xiang Liu, Liucheng Zhong, Yutong Lu, Feng Xu, Qionghai Dai, Jianxing He · 发表于：The Lancet Digital Health · 年份：2023 · DOI：10.1016/s2589-7500(23)00106-1 · 被引用次数：17 · 研究领域：COVID-19 diagnosis using AI、Lung Cancer Diagnosis and Treatment、AI in cancer detection

BACKGROUND: Mediastinal neoplasms are typical thoracic diseases with increasing incidence in the general global population and can lead to poor prognosis. In clinical practice, the mediastinum's complex anatomic structures and intertype confusion among different mediastinal neoplasm pathologies severely hinder accurate diagnosis. To solve these difficulties, we organised a multicentre national collaboration on the basis of privacy-secured federated learning and developed CAIMEN, an efficient chest CT-based artificial intelligence (AI) mediastinal neoplasm diagnosis system. METHODS: In this multicentre cohort study, 7825 mediastinal neoplasm cases and 796 normal controls were collected from 24 centres in China to develop CAIMEN. We further enhanced CAIMEN with several novel algorithms in a multiview, knowledge-transferred, multilevel decision-making pattern. CAIMEN was tested by internal (929 cases at 15 centres), external (1216 cases at five centres and a real-world cohort of 11 162 cases), and human-AI (60 positive cases from four centres and radiologists from 15 institutions) test sets to evaluate its detection, segmentation, and classification performance. FINDINGS: In the external test experiments, the area under the receiver operating characteristic curve for detecting mediastinal neoplasms of CAIMEN was 0·973 (95% CI 0·969-0·977). In the real-world cohort, CAIMEN detected 13 false-negative cases confirmed by radiologists. The dice score for segmenting mediastinal neopla...