GutCore: An Endoscopy Foundation Model for Whole-Case Gastric Cancer Analysis

作者：Sehun KIM, Hyosoon Yoo, Seong‐Keun Yoo, Jeeyun Lee, Hyuk Lee, Yang Won Min · 发表于：medRxiv · 年份：2026 · DOI：10.64898/2026.07.01.26356993 · 研究领域：Gastric Cancer Management and Outcomes、Helicobacter pylori-related gastroenterology studies、Colorectal Cancer Screening and Detection

Abstract Background Routine endoscopy image sets contain complementary information about lesions, surrounding mucosa, anatomy, and examination context. However, most endoscopic artificial intelligence systems are developed and evaluated on selected frames for narrowly defined image-level tasks, limiting their ability to support case-level analysis. Objective To develop GutCore, an endoscopy foundation model for whole-case patient-level gastric cancer analysis, and assess whether routinely stored endoscopic image sets can support cancer detection, invasion-depth prediction, biomarker inference, and prognosis. Design GutCore was pretrained on 5.6 million de-identified endoscopic images from more than ten hospitals. We compared GutCore with general, medical, and endoscopy-specific foundation models using public image-level datasets and an internal retrospective cohort of 11,035 de-identified endoscopic examinations from Samsung Medical Center (2019–2023). Whole-case image sets were aggregated for patient-level prediction of cancer status, invasion depth, biomarker status, and overall survival. Results GutCore achieved AUCs of 0.996 for cancer detection, 0.960 for muscularis propria invasion, 0.801 for SM2-or-deeper invasion, and 0.781 for mucosal versus submucosal invasion among ESD-treated early gastric cancer cases. Biomarker prediction was strongest for EBV status and MLH1 loss and weaker for HER2 status, with AUCs of 0.861, 0.822, and 0.648, respectively. In the held-out adv...