ArticleInternational journal of surgery (London, England)2026
Integration of clinical, pathological, and endoscopic data improves prediction for lymph node metastasis in early gastric cancer: a retrospective multicenter study.
Article in International journal of surgery (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- AI Revolution in Upper GI: Endoscopic Diagnosis of Gastric Cancers and Premalignant Neoplasms.Diseases (Basel, Switzerland) · 2026Review
- Development and Validation of an Individualized Nomogram to Identify Undifferentiated-Predominant Mixed-Type Early Gastric Cancer.Journal of digestive diseasesObservational
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Authors and funding
17 authors.
Funding
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Abstract
backgroundAccurate prediction of lymph node metastasis (LNM) is crucial for treatment decisions in early gastric cancer (EGC). Current preoperative methods for LNM prediction are often insufficient. PATIENTS AND
methodsThis multicenter retrospective study enrolled 605 EGC patients who underwent endoscopic submucosal dissection (ESD) and/or surgery across five institutions between 2013 and 2023. We developed a deep learning model, LNMate, to predict LNM before ESD and evaluated its impact on endoscopists' diagnostic performance. We also conducted immunohistochemical (IHC) analysis on tissue samples from 32 patients. Additionally, we constructed a deep learning-based endoscopic nomogram (DLEN) to estimate LNM risk after ESD, validated through comparison with the eCura system.
resultsLNMate showed high predictive performance with area under the curves (AUCs) ranging from 0.843 (95% confidence interval [CI], 0.782-0.904) to 0.875 (95% CI, 0.814-0.936). With LNMate assistance, endoscopists improved diagnostic accuracy by 13.8%, particularly in specificity (mean increase of 0.17, P = 0.03). IHC analysis showed associations between CD8+ and CD20+ cell enrichment with non-LNM predictions, and CD68+ macrophage infiltration with LNM. The DLEN outperformed the eCura system (AUC, 0.91 [95% CI, 0.86-0.97] vs. 0.71 [95% CI, 0.58-0.83], P < 0.01), reducing oversurgery by 24.2%, with no false negatives.
conclusionsThe deep learning-based intelligent system for the entire treatment process of EGC showed excellent performance in predicting LNM, offering valuable decision support for both endoscopists in pre-ESD treatment planning and surgeons in post-ESD surgical decision-making.
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