文献类型：期刊文献

英文题名：Diagnostic Value of Artificial Intelligence-Based Pathology Diagnosis System in Lymphatic Metastasis of Gastric Cancer

作者：Zhao, Tiantian[1];Wu, Qiong[2];Zhu, Chenglou[2,3];Ma, Hong[4];Da, Mingxu[1,2,3]

第一作者：赵婷婷

通信作者：Da, M[1];Da, M[2];Da, M[3];Ma, H[4]

机构：[1]Gansu Univ Chinese Med, Sch Clin Med 1, Lanzhou, Peoples R China;[2]Lanzhou Univ, Sch Clin Med 1, Lanzhou, Peoples R China;[3]Gansu Prov Hosp, Dept Surg Oncol, Lanzhou, Peoples R China;[4]Gansu Prov Hosp, Dept Intervent Med, Lanzhou, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Univ Chinese Med, Sch Clin Med 1, Lanzhou, Peoples R China;[2]corresponding author), Lanzhou Univ, Sch Clin Med 1, Lanzhou, Peoples R China;[3]corresponding author), Gansu Prov Hosp, Dept Surg Oncol, Lanzhou, Peoples R China;[4]corresponding author), Gansu Prov Hosp, Dept Intervent Med, Lanzhou, Peoples R China.|[10735]甘肃中医药大学;

年份：2024

外文期刊名：ONCOLOGY

收录：;Scopus(收录号：2-s2.0-85216778884);WOS:【SCI-EXPANDED(收录号:WOS:001398111100001)】；

基金：The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article. This work was supported by the Natural Science Foundation of Gansu Province (Grant No. 23JRRA1317).

语种：英文

外文关键词：Artificial intelligence; Gastric cancer; Pathological diagnosis; Lymphatic metastasis; Meta-analysis

摘要：Introduction: Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, with lymph node metastasis (LNM) being an independent prognostic factor. However, there are still challenges in the pathological diagnosis of LNM in GC. The aim of this meta-analysis was to systematically evaluate the accuracy of artificial intelligence (AI) in detecting LNM in GC from whole-slide pathological images. Methods: As of March 24, 2024, a comprehensive search for studies on the pathological diagnosis of GC LNM AI was performed in the databases of PubMed, Web of Science, Cochrane Library, and CNKI. Meta-analysis of the included data was performed using MetaDiSc 1.4, Review Manager 5.4, and Stata SE 17.0 software to calculate diagnostic metrics such as overall sensitivity and specificity. The overall diagnostic performance of the AI was assessed. Meta-regression analysis explored sources of heterogeneity. Results: A total of 7 articles involving 1,669 GC patients were included. The analysis showed that AI had a sensitivity of 0.90 (95% CI: 0.84-0.94) and a specificity of 0.95 (95% CI: 0.91-0.98) for the diagnosis of GC LNM, with significant heterogeneity across studies. The area under the curve was 0.97, indicating an excellent diagnostic value. Meta-regression analysis showed that the sample size and the number of study centers contributed to the heterogeneity. Conclusion: AI for diagnosing LNM in GC from whole-slide pathological images demonstrates high accuracy, offering significant clinical implications for improving diagnosis and treatment strategies. (c) 2024 S. Karger AG, Basel