AI-based large-scale screening of gastric cancer from noncontrast CT imaging.
Authors
Affiliations (39)
Affiliations (39)
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
- Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou, China.
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China.
- DAMO Academy, Alibaba Group, Washington, DC, USA.
- Hupan Laboratory, Hangzhou, China.
- DAMO Academy, Alibaba Group, Hangzhou, China.
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
- Department of Gastric Surgery, Liaoning Cancer Hospital, Shenyang, China.
- Department of Gastrointestinal surgery, Ningbo Second Hospital, Ningbo, China.
- Department of Gastrointestinal surgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- Department of Radiology, First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.
- Department of Urology, Wenzhou People's Hospital, Wenzhou, China.
- Department of Urology, Pingyang People's Hospital, Wenzhou, China.
- Department of Radiology, Longgang People's Hospital, Wenzhou, China.
- Department of Radiology, Yuyao People's Hospital, Ningbo, China.
- Department of Radiology, Cixi People's Hospital, Ningbo, China.
- Department of Radiology, Second Affiliated Hospital of Anhui Medical University, Hefei, China.
- Department of Gastric Surgery, Fujian Cancer Hospital, Fuzhou, China.
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China.
- Department of Hematology, First People's Hospital of Linping District, Hangzhou, China.
- Department of Radiology, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.
- Department of Gastrointestinal Surgery, The People's Hospital of Qiannan, Duyun, China.
- Department of Gastrointestinal Surgery, Huzhou People's Hospital, Huzhou, China.
- Department of Radiation Oncology, Taizhou Cancer Hospital, Wenling, China.
- Department of Gastrointestinal Surgery, Yinzhou People's Hospital, Ningbo, China.
- Department of Radiology, Fenghua People's Hospital, Ningbo, China.
- Department of Nuclear Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
- School of Communication and Electronic Engineering, East China Normal University, Shanghai, China.
- Department of Orthopedics, Fenghua People's Hospital, Ningbo, China. [email protected].
- Department of Radiology, Pingyang People's Hospital, Wenzhou, China. [email protected].
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. [email protected].
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. [email protected].
- Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou, China. [email protected].
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China. [email protected].
- DAMO Academy, Alibaba Group, Washington, DC, USA. [email protected].
- Hupan Laboratory, Hangzhou, China. [email protected].
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. [email protected].
- Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou, China. [email protected].
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China. [email protected].
Abstract
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 .