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Updated: Feb 17, 2026

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
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Risk prediction for chronic atrophic gastritis using a random forest model: A multicenter study.

Hui Cao1, Jing-Lue Han1, Hao Wu2

  • 1Department of Gastroenterology, National Clinical Research Center for Digestive Diseases (Xi'an) Jiangsu Branch, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi 214000, Jiangsu Province, China.

World Journal of Gastrointestinal Oncology
|February 16, 2026
PubMed
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Helicobacter pylori Promoted miR-196a/b-5p Expression and Accelerated Tumorigenesis of the Gastric Mucosa by Targeting IGF2BP1 and Activating PI3K-Akt Signaling Pathway.

The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology·2025
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Endoscopists and endoscopic assistants' qualifications, but not their biopsy rates, improve gastric precancerous lesions detection rate.

World journal of gastrointestinal endoscopy·2025
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Mechanistic Investigation on the Regulation of FABP1 by the IL-6/miR-603 Signaling in the Pathogenesis of Hepatocellular Carcinoma.

BioMed research international·2021
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Regulating effect of TongXie-YaoFang on colonic epithelial secretion <i>via</i> Cl<sup>-</sup> and HCO<sub>3</sub><sup>-</sup> channel.

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[The interventions effect-assessment of the workers exposed to N, N-dimethylformamide by percutaneous in a synthetic leather factory].

Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases·2011
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[The analysis of effect of Th1/Th2 cytokine in the different prognosis in severe influenza A (H1N1)].

Zhonghua shi yan he lin chuang bing du xue za zhi = Zhonghua shiyan he linchuang bingduxue zazhi = Chinese journal of experimental and clinical virology·2011

A new noninvasive model predicts chronic atrophic gastritis (CAG) risk using key factors like H. pylori infection and age. This tool aids in early identification of high-risk patients for gastric cancer prevention.

Area of Science:

  • Gastroenterology and Oncology
  • Biostatistics and Machine Learning
  • Preventive Medicine

Background:

  • Chronic atrophic gastritis (CAG) is a precancerous condition for gastric cancer (GC).
  • Current diagnostic methods (gastroscopy, biopsy) are invasive and have limitations.
  • Noninvasive tools are needed for early CAG detection.

Purpose of the Study:

  • To develop and validate a noninvasive risk prediction model for CAG.
  • To identify high-risk individuals for gastric cancer screening.

Main Methods:

  • A random forest model was built using multimodal data from 1268 subjects.
  • Features included demographics, lifestyle, H. pylori status, and serological markers (PGR).
  • Model performance was evaluated using AUC and validated in an external cohort; SHAP analysis was used for interpretation.
Keywords:
Chronic atrophic gastritisGastric cancer screeningMachine learningRandom forestRisk prediction

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Main Results:

  • The model identified six key predictors: age, H. pylori infection, PGR, smoking, alcohol, and family history.
  • The model achieved high AUCs (0.8542 training, 0.8073 testing, 0.8505 external validation).
  • H. pylori infection, age, and PGR were the most influential factors.

Conclusions:

  • The random forest model accurately predicts CAG risk noninvasively.
  • The model is interpretable and has significant clinical utility for early screening.
  • A web-based platform facilitates clinical application.