{
  "abstract": "Background: Accurate risk stratification following curative resection of non-metastatic clear cell renal cell carcinoma (nm-ccRCC) remains clinically challenging. This study aims to develop and validate Bayesian network (BN) models to predict survival in patients with nm-ccRCC.\n\nMethods: A dual-center retrospective cohort was randomly stratified into a training set (N = 326) and an internal validation set (N = 140). External validation was performed using an independent external cohort (N = 170). BN models were constructed based on clinicopathologic characteristics that demonstrated statistical significance in univariate analyses of the training cohort and were assessed using receiver operating characteristic (ROC) curves, confusion matrices, calibration and decision curve analysis.\n\nResults: The Bayesian analyses identified T stages as the principal determinant of 5-year overall survival (OS), while ISUP grades emerged as the dominant predictor for 3-year disease-free survival (DFS). The 5-year OS and 3-year DFS models achieved areas under the ROC curve (AUCs) of 0.878 and 0.867 in the internal validation cohort and 0.838 and 0.846 in the external validation cohort. Calibration slopes were close to unity with small intercepts across cohorts, and decision curve analysis showed net benefit exceeding that of treating all or no patients across a wide range of thresholds.\n\nConclusions: This study establishes BNs for the first time as a clinically actionable tool for postoperative risk prediction in nm-ccRCC, with promising discriminative and calibrated performance supporting their potential use in patient counseling, risk-stratified surveillance, and the selection of candidates for adjuvant therapy or clinical trials. Future prospective, multicenter studies are warranted to extend these predictions to a 5-year DFS endpoint and to refine the decision threshold for clinical translation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Urology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital",
    "Department of Infectious Disease, The First Affiliated Hospital of Fujian Medical University",
    "Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital",
    "Department of Urology, Zhangzhou Affiliated Hospital of Fujian Medical University",
    "Department of Urology, The Second Affiliated Hospital of Fujian Medical University",
    "Department of Urology, Fujian Medical University Union Hospital",
    "Department of Anesthesiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital",
    "Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Bin Zhan"
    },
    {
      "affs": [
        1
      ],
      "name": "Yachun Chen"
    },
    {
      "affs": [
        2
      ],
      "name": "Hongqian Ke"
    },
    {
      "affs": [
        3
      ],
      "name": "Sixing Huang"
    },
    {
      "affs": [
        4
      ],
      "name": "Siwei Xu"
    },
    {
      "affs": [
        3
      ],
      "name": "Yi Huang"
    },
    {
      "affs": [
        5
      ],
      "name": "Xiaobao Chen"
    },
    {
      "affs": [
        6
      ],
      "name": "Xuru Zhang"
    },
    {
      "affs": [
        7
      ],
      "name": "Yuxin Wu"
    },
    {
      "affs": [
        5
      ],
      "name": "Wei Jiang"
    },
    {
      "affs": [
        0
      ],
      "name": "Ronghui Lin"
    },
    {
      "affs": [
        3
      ],
      "name": "Tianqi Lin"
    },
    {
      "affs": [
        0
      ],
      "name": "Shushang Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5w7b64xehw1m5.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1877632",
  "fulltext_url": null,
  "id": "5w7b64xehw1m5",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1877632/full",
  "language": null,
  "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": [
    [
      "openalex",
      "2740|Renal cell carcinoma treatment"
    ]
  ],
  "openalex": "W7224131777",
  "pdf_url": "https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1877632/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/5w7b64xehw1m5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:14Z",
      "native_id": "10.3389/fonc.2026.1877632",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1877632/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2234-943X",
      "subfield": "oncology"
    }
  ],
  "title": "Analysis of prognostic factors for survival after curative resections of clear-cell renal cell carcinoma based on Bayesian networks",
  "title_text": "Analysis of prognostic factors for survival after curative resections of clear-cell renal cell carcinoma based on Bayesian networks",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:14Z",
  "url": "https://research.draconitic.com/paper/5w7b64xehw1m5",
  "venue": "Frontiers in Oncology",
  "versions": []
}