{
  "abstract": "Objective: To develop and validate an interpretable machine learning (ML) model that integrates extracellular volume fraction (ECV) derived from contrast-enhanced CT with clinical and imaging features for preoperative prediction of World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade in clear cell renal cell carcinoma (ccRCC).\n\nMaterials and methods: A retrospective cohort of 263 patients with pathologically confirmed ccRCC from two institutions was analyzed. Patients from Hospital A (n=184) were randomly divided into training (n=128) and internal validation (n=56) cohorts, while patients from Hospital B (n=79) served as an independent external validation cohort. CT-derived ECV, arterial enhancement fraction (AEF), and conventional imaging features were extracted. Six machine learning models (Logistic Regression [LR], eXtreme Gradient Boosting [XGBoost], Gaussian Naive Bayes [GNB], Support Vector Machine [SVM], K-Nearest Neighbors [KNN], Light Gradient Boosting Machine [LightGBM]) were constructed, and the best-performing model was identified based on bootstrap resampling. Model interpretability was assessed using SHapley Additive exPlanations (SHAP).\n\nResults: Tumor size (low-grade: 4.99 ± 1.98 cm; high-grade: 6.20 ± 2.72 cm, P = 0.002), ECV (low-grade: 0.46 ± 0.09; high-grade: 0.57 ± 0.12, P<0.001), and AEF (low-grade: 1.37 ± 0.24; high-grade: 1.56 ± 0.25, P<0.001) were significantly higher in high-grade ccRCC, while hematocrit (HCT, low-grade: 0.41 ± 0.05; high-grade: 0.38 ± 0.06, P<0.001) was lower. The optimal LR model achieved robust performance: training cohort AUC = 0.995 (95% CI: 0.990–0.999), internal validation AUC = 0.903 (95% CI: 0.818–0.988), external test AUC = 0.857 (95% CI: 0.756–0.958). SHAP analysis showed ECV, tumor diameter, and AEF were the top 3 predictors.\n\nConclusion: An interpretable ML model incorporating CT-derived ECV and imaging features effectively predicts preoperative WHO/ISUP grade of ccRCC, providing a noninvasive tool for individualized risk stratification and clinical decision-making.",
  "abstract_shown": true,
  "affiliations": [
    "Department of medical imaging, Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University",
    "Department of Medical Imaging, Siyang First People’s Hospital"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yating Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Ying Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Genji Bai"
    },
    {
      "affs": [
        0
      ],
      "name": "Wei Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "Min Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "Wei Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/efxt76dtbsvmb.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1851636",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13710075/",
  "id": "efxt76dtbsvmb",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1851636/full",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
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  "openalex": "W7221630037",
  "pdf_url": "https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1851636/pdf",
  "pmcid": "PMC13710075",
  "pmid": "42947876",
  "published": "2026-10-13",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/efxt76dtbsvmb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:13Z",
      "native_id": "10.3389/fonc.2026.1851636",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1851636/full"
    },
    {
      "first_seen": "2026-10-31T12:21:06Z",
      "native_id": "PMC13710075",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fonc.2026.1851636"
    }
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Interpretable machine learning for preoperative prediction of WHO/ISUP grade in clear cell renal cell carcinoma using contrast-enhanced CT–derived extracellular volume fraction",
  "title_text": "Interpretable machine learning for preoperative prediction of WHO/ISUP grade in clear cell renal cell carcinoma using contrast-enhanced CT–derived extracellular volume fraction",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:21:06Z",
  "url": "https://research.draconitic.com/paper/efxt76dtbsvmb",
  "venue": "Frontiers in Oncology",
  "versions": []
}