{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T11:19:04Z","timestamp":1789125544738,"version":"build-2803163510"},"reference-count":61,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:00:00Z","timestamp":1758931200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:00:00Z","timestamp":1758931200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Digestive Endoscopy"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>With the widespread use of advanced endoscopic techniques such as endoscopic submucosal dissection, an increasing number of early colorectal cancer (T1 CRC) and early gastric cancer (EGC) cases are now treated with endoscopic resection as the first\u2010line approach. However, the risk of lymph node metastasis (LNM)\u2014approximately 10% in T1 CRC and 5%\u201310% in EGC\u2014necessitates additional surgical resection in high\u2010risk cases. Current guideline\u2010based risk stratification depends on pathological evaluation of the resected specimens to determine whether further surgery is needed. Yet both T1 CRC and EGC face shared challenges in LNM risk prediction, particularly in terms of accuracy and reproducibility. This review focuses on the latter. The diagnosis of key pathological risk factors, which serve as predictors of LNM, is subject to considerable interobserver variability among pathologists. One potential solution is the application of artificial intelligence (AI)\u2010assisted whole slide image (WSI) analysis, which has been gaining attention in recent studies. AI\u2010assisted models for LNM prediction in T1 CRC and EGC have shown encouraging results, suggesting that WSI\u2010based AI could offer a pathologist\u2010independent strategy to improve diagnostic consistency. However, the field remains in an early stage, with key limitations including small sample sizes and limited external validation. Additional high\u2010quality evidence will be needed to support clinical implementation. Addressing challenges such as stain standardization and image artifacts will also be critical for achieving regulatory approval and broader clinical adoption.<\/jats:p>","DOI":"10.1111\/den.70042","type":"journal-article","created":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T11:11:31Z","timestamp":1758971491000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Artificial Intelligence\u2010Assisted Whole Slide Image Analysis for Lymph Node Status Prediction in Early Colorectal and Gastric Cancer"],"prefix":"10.1111","volume":"38","author":[{"given":"Katsuro","family":"Ichimasa","sequence":"first","affiliation":[{"name":"Digestive Disease Center Showa Medical University Northern Yokohama Hospital  Yokohama Kanagawa Japan"},{"name":"Yong Loo Lin School of Medicine National University of Singapore  Singapore 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