AI-based large-scale screening of gastric cancer from noncontrast CT imaging.
СтатьяМногоцентровое исследованиеНаблюдательное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Hu Can, Xia Yingda, Zheng Zhilin, Cao Mengxuan, Zheng Guoliang, Chen Shangqi, Sun Jiancheng, Chen Wujie, Zheng Qi, Pan Siwei, Zhang Yanqiang, Chen Jiahui, Yu Pengfei, Xu Jingli, Xu Jianwei, Qiu Zhongwei, Lin Tiancheng, Yun Boxiang, Yao Jiawen, Guo Wenchao, Gao Chen, Kong Xianghui, Chen Keda, Wen Zhengle, Zhu Guanxia, Qiao Jinfang, Pan Yibo, Li Huan, Gong Xijun, Ye Zaisheng, Ao Weiqun, Zhang Lei, Yan Xing, Tong Yahan, Yang Xinxin, Zheng Xiaozhong, Fan Shufeng, Cao Jielu, Yan Cheng, Xie Kangjie, Zhang Shengjie, Wang Yao, Zheng Lin, Wu Yingjie, Ge Zufeng, Tian Xiyuan, Zhang Xin, Wang Yan, Zhang Ruolan, Wei Yizhou, Zhu Weiwei, Zhang Jianfeng, Qiu Hanjun, Su Miaoguang, Shi Lei, Xu Zhiyuan, Zhang Ling, Cheng Xiangdong
Аннотация
Early detection through screening is critical for reducing gastric cancer (GC) mortality. However, in most high-prevalence regions, large-scale screening remains challenging due to limited resources, low compliance and suboptimal detection rate of upper endoscopic screening. Therefore, there is an urgent need for more efficient screening protocols. Noncontrast computed tomography (CT), routinely performed for clinical purposes, presents a promising avenue for large-scale designed or opportunistic screening. Here we developed the Gastric Cancer Risk Assessment Procedure with Artificial Intelligence (GRAPE), leveraging noncontrast CT and deep learning to identify GC. Our study comprised three phases. First, we developed GRAPE using a cohort from 2 centers in China (3,470 GC and 3,250 non-GC cases) and validated its performance on an internal validation set (1,298 cases, area under curve = 0.970) and an independent external cohort from 16 centers (18,160 cases, area under curve = 0.927). Subgroup analysis showed that the detection rate of GRAPE increased with advancing T stage but was independent of tumor location. Next, we compared the interpretations of GRAPE with those of radiologists and assessed its potential in assisting diagnostic interpretation. Reader studies demonstrated that GRAPE significantly outperformed radiologists, improving sensitivity by 21.8% and specificity by 14.0%, particularly in early-stage GC. Finally, we evaluated GRAPE in real-world opportunistic screening using 78,593 consecutive noncontrast CT scans from a comprehensive cancer center and 2 independent regional hospitals. GRAPE identified persons at high risk with GC detection rates of 24.5% and 17.7% in 2 regional hospitals, with 23.2% and 26.8% of detected cases in T1/T2 stage. Additionally, GRAPE detected GC cases that radiologists had initially missed, enabling earlier diagnosis of GC during follow-up for other diseases. In conclusion, GRAPE demonstrates strong potential for large-scale GC screening, offering a feasible and effective approach for early detection. ClinicalTrials.gov registration: NCT06614179 .