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Serum Cystatin 4 combined with routine clinical laboratory indicators: A support vector machine diagnostic model for early screening of gastrointestinal tumors

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  • Huijuan Bi
  • Lina Yin
  • Wenhao Fang
  • Jilu Shen

Abstract

Gastrointestinal tumors lack non-invasive early screening tools, with most patients diagnosed at advanced stages. Serum tumor markers combined with machine learning show promise, but single markers have low specificity. This study aimed to construct a non-invasive diagnostic model using multiple laboratory variables. A retrospective study included 214 gastrointestinal tumor patients (observation group) and 130 non-tumor individuals (control group). Thirty-eight laboratory indicators were detected, and seven core variables (HCT, Age, TP, ALB, PLT, WBC, CST4) were identified via feature selection. Eleven machine learning algorithms were used to build models, with performance evaluated by AUC, sensitivity, specificity, calibration curves, and DCA. Serum CST4 levels were significantly higher in the observation group (P

Suggested Citation

  • Huijuan Bi & Lina Yin & Wenhao Fang & Jilu Shen, 2026. "Serum Cystatin 4 combined with routine clinical laboratory indicators: A support vector machine diagnostic model for early screening of gastrointestinal tumors," PLOS ONE, Public Library of Science, vol. 21(9), pages 1-17, September.
    • Handle: RePEc:plo:pone00:0353753
    DOI: 10.1371/journal.pone.0353753
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