{
  "id": 17500,
  "url": "https://www.jmir.org/2026/1/e94837",
  "title": "Development and Validation of an Interpretable Machine Learning Model for Staging Helicobacter pylori–Initiated Intestinal-Type Gastric Cancer in the Correa Cascade: Cross-Sectional Study",
  "summary": "Background: Gastric cancer (GC) is one of the most common malignant tumors worldwide, with –associated intestinal-type gastric cancer (IGC) being the most prevalent subtype, accounting for approximately 85% of cases. Because most patients are diagnosed at intermediate or advanced stages, early screening and accurate stage stratification of IGC progression remain major clinical challenges. Objective: This study aimed to develop an interpretable machine learning (ML) model that leverages routine l",
  "authors": "Jiawei Tang, Huijin Chen, Wenwen Zhang, Alfred Chin Yen Tay, Barry J Marshall, Cong Ma, Liang Wang",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-07T23:00:08.000Z",
  "fetched_at": "2026-08-08T05:10:34.355Z",
  "source_slug": "x-jmir-journal-of-medical-internet-researc",
  "source_name": "JMIR (Journal of Medical Internet Research)",
  "source_homepage": "https://www.jmir.org",
  "ethics_ai_record_url": "https://ethics.ai/record/17500",
  "original_url": "https://www.jmir.org/2026/1/e94837",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}