{
  "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 < 0.001, AUC = 0.706). The SVM model showed the best performance: AUC = 0.85 (95% CI: 0.78–0.92), sensitivity = 95.4% (95% CI: 0.87–0.98), specificity = 61.5% (95% CI: 0.52–0.71), PPV = 80.5% (95% CI: 0.74–0.86), NPV = 88.9% (95% CI: 0.81–0.94) in the test set. Calibration curves demonstrated high consistency (deviation < 5%), and DCA confirmed superior net benefits. SHapley Additive exPlanations (SHAP) analysis identified HCT, CST4, and Age as top contributors. This internally validated tool demonstrated promising preliminary performance for gastrointestinal tumor screening in high-risk populations (sensitivity 95.4%, specificity 61.5%). Due to its modest specificity, it is suitable as an auxiliary risk stratification tool for high-risk individuals but not for general population screening. External prospective multi-center validation is mandatory before clinical application.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Clinical Laboratory, Anhui Provincial Public Health Clinical Center, Hefei, Anhui, China",
    "Department of Clinical Laboratory, The First Affiliated Hospital of Anhui Medical University North District, Hefei, Anhui, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Huijuan Bi"
    },
    {
      "affs": [
        1
      ],
      "name": "Lina Yin"
    },
    {
      "name": "Wenhao Fang"
    },
    {
      "name": "Jilu Shen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2hthetwem4jby.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0353753",
  "fulltext_url": null,
  "id": "2hthetwem4jby",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0353753",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3605|Artificial Intelligence in Healthcare"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Learning/Learning curves"
    ],
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      "plos",
      "/Biology and life sciences/Neuroscience/Learning and memory/Learning/Learning curves"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Learning/Learning curves"
    ],
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      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
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      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Machine learning algorithms"
    ],
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      "plos",
      "/Medicine and health sciences/Diagnostic medicine"
    ],
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      "plos",
      "/Medicine and health sciences/Diagnostic medicine/Cancer detection and diagnosis"
    ],
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      "plos",
      "/Medicine and health sciences/Oncology/Cancer detection and diagnosis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Colorectal cancer"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Gastrointestinal tumors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Gastrointestinal tumors/Gastric cancer"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Applied mathematics/Algorithms/Machine learning algorithms"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms/Machine learning algorithms"
    ],
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      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Learning/Learning curves"
    ]
  ],
  "openalex": "W7213871346",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0353753&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/2hthetwem4jby.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1371/journal.pone.0353753",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0353753"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
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    {
      "field": "medicine",
      "origin": "plos:oncology",
      "subfield": "oncology"
    }
  ],
  "title": "Serum Cystatin 4 combined with routine clinical laboratory indicators: A support vector machine diagnostic model for early screening of gastrointestinal tumors",
  "title_text": "Serum Cystatin 4 combined with routine clinical laboratory indicators: A support vector machine diagnostic model for early screening of gastrointestinal tumors",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/2hthetwem4jby",
  "venue": "PLOS One",
  "versions": []
}