{
  "abstract": "Background: Postoperative lower-extremity deep vein thrombosis (DVT) is a common and potentially fatal complication after radical surgery for gynecologic malignancies, yet population-specific risk tools remain limited.\n\nMethods: We retrospectively analyzed 500 consecutive patients who underwent radical surgery for cervical, endometrial, or ovarian cancer (January 2022-January 2025). The cohort was randomly divided into a training set (n=351, 70%) and a validation set (n=149, 30%) at a 7:3 ratio. Independent predictors were identified using logistic regression, and 10 machine-learning algorithms were trained and compared. Model discrimination, calibration, and clinical utility were evaluated, and the optimal model was interpreted using SHAP.\n\nResults: DVT occurred in 116 patients (23.2%). Five variables were independently associated with DVT: age, Caprini score, lymphocyte count, preoperative D-dimer, and neutrophil-to-lymphocyte ratio (NLR).Across candidate models, CatBoost achieved the optimal overall performance on the validation set, yielding an AUC of 0.816 (95% CI: 0.738–0.881), with sensitivity of 0.824 (95% CI: 0.700–1.000) and specificity of 0.730 (95% CI: 0.515–0.876), and SHAP analysis ranked the predictors by mean absolute SHAP value in the following order: lymphocyte count, Caprini score, age, preoperative D-dimer, and neutrophil-to-lymphocyte ratio (NLR).\n\nConclusion: An interpretable CatBoost-based model enables individualized postoperative DVT risk stratification after gynecologic cancer surgery and may support targeted, evidence-based thromboprophylaxis and perioperative nursing management.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Interventional Therapy I, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences , , ,",
    "Depression Disorder Diagnosis and Treatment Center, Shandong Mental Health Center , , ,",
    "Ward 1, Lung Surgery, Shandong Cancer Hospital and Institute , , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yanhong Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Congcong Shi"
    },
    {
      "affs": [
        0
      ],
      "name": "Deyan Yang"
    },
    {
      "affs": [
        2
      ],
      "name": "Xiaona Ji"
    },
    {
      "affs": [
        0
      ],
      "name": "Yingli Chu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/aqshf48m8spm1.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1813354",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661352/?tool=EBI",
  "id": "aqshf48m8spm1",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13661352",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2724|Venous Thromboembolism Diagnosis and Management"
    ]
  ],
  "openalex": "W7214945000",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661352/pdf/?tool=EBI",
  "pmcid": "PMC13661352",
  "pmid": null,
  "published": "2026-09-30",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/aqshf48m8spm1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:20Z",
      "native_id": "PMC:PMC13661352",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13661352"
    },
    {
      "first_seen": "2026-10-30T12:20:13Z",
      "native_id": "10.3389/fonc.2026.1813354",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1813354/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2234-943X",
      "subfield": "oncology"
    }
  ],
  "title": "Explainable machine-learning model for postoperative lower-extremity deep vein thrombosis after gynecologic cancer surgery",
  "title_text": "Explainable machine-learning model for postoperative lower-extremity deep vein thrombosis after gynecologic cancer surgery",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:13Z",
  "url": "https://research.draconitic.com/paper/aqshf48m8spm1",
  "venue": "Frontiers in oncology",
  "versions": []
}