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Machine Learning-Based Diagnostic Models for Early Gastric Cancer Using Clinical Laboratory Indicators.

Runbi Ji1,2, Ruoyu Yang1,2, Jun Yao1

  • 1The Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, 212002, People's Republic of China.

International Journal of General Medicine
|March 25, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts gastric cancer risk using blood tests and pathology. The XGBoost model demonstrated superior diagnostic performance, paving the way for clinical application.

Keywords:
clinical laboratory indicatorsdiagnostic modelearly gastric cancerglutathione reductasemachine learning

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Area of Science:

  • Oncology
  • Bioinformatics
  • Medical Diagnostics

Background:

  • Gastric cancer involves complex pathological processes with numerous clinical indicator abnormalities.
  • Machine learning offers advanced capabilities for analyzing extensive variables in disease prediction and diagnosis.

Purpose of the Study:

  • To develop and evaluate machine learning models for accurate gastric cancer diagnosis.
  • To identify key indicators contributing to gastric cancer prediction.

Main Methods:

  • Collected clinical data from gastric cancer patients (2016-2023).
  • Applied five machine learning algorithms: XGBoost, RF, SVM-RFE, LGBM, and rpart.
  • Evaluated model performance using AUROC, F1-score, sensitivity, and specificity.

Main Results:

  • XGBoost achieved the highest diagnostic performance (AUC=0.9909) when combining blood and pathological data.
  • Key diagnostic indicators included Glutathione reductase (GR), CA724, RBC, CA242, and ALB.
  • Tumor size was identified as an independent risk factor for early gastric cancer.

Conclusions:

  • Machine learning models integrating blood and pathological data enhance gastric cancer risk prediction accuracy.
  • The XGBoost model exhibits excellent diagnostic performance, supporting preclinical implementation.
  • This study provides evidence for the clinical utility of machine learning in gastric cancer diagnostics.