Supplementary Material for: Diagnostic value of artificial intelligence-based pathology diagnosis system in lymphatic metastasis of gastric cancer

Abstract
Introduction Gastric cancer 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 gastric cancer (GC). The aim of this meta-analysis is 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 Meta-Disc 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.

Abstract
Introduction 胃癌仍是全球癌症相关死亡的主要原因之一，淋巴结转移（LNM）是其独立预后因素。然而，胃癌（GC）淋巴结转移的病理诊断仍面临挑战。本荟萃分析旨在系统评估人工智能（AI）通过全切片病理图像检测胃癌淋巴结转移的准确性。
Methods 截至2024年3月24日，研究人员在PubMed、Web of Science、Cochrane Library和CNKI数据库中全面检索了关于AI诊断胃癌淋巴结转移的病理研究。采用Meta-Disc 1.4、Review Manager 5.4和Stata SE 17.0软件对纳入数据进行荟萃分析，计算总灵敏度、特异性等诊断指标。评估了AI的整体诊断性能，并通过Meta回归分析探究了异质性来源。
Results 共纳入7篇文献，涉及1669例胃癌患者。分析结果显示，AI诊断胃癌淋巴结转移的灵敏度为0.90（95%置信区间：0.84-0.94），特异性为0.95（95%置信区间：0.91-0.98），且研究间存在显著异质性。曲线下面积为0.97，表明其具有优异的诊断价值。Meta回归分析显示，样本量和研究中心数量是异质性的来源。
Conclusion 通过全切片病理图像诊断胃癌淋巴结转移的AI具有较高准确性，对改进诊疗策略具有重要临床意义。