{
  "abstract": "Background: Accurate preoperative differentiation of muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC) is central to treatment selection. We evaluated whether a nomogram integrating multiparametric MRI (mpMRI) radiomics, VI-RADS, and routinely recorded clinical-radiologic features improves prediction of muscle invasion.\n\nMethods: This single-center retrospective study included 350 consecutive patients with histologically confirmed bladder urothelial carcinoma who underwent preoperative 3.0-T mpMRI between January 2019 and December 2024. Patients were split chronologically into a training cohort (n = 245) and a temporal validation cohort (n = 105). Radiomics features were extracted from T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps. Reproducibility filtering, univariable screening, collinearity pruning, and least-absolute-shrinkage-and-selection-operator regression were performed using the training cohort only. Clinical-radiologic, radiomics, and combined logistic models were evaluated by discrimination, calibration, Brier score, and decision curve analysis.\n\nResults: MIBC was present in 78 of 245 training patients (31.8%) and 33 of 105 validation patients (31.4%). Rad-score, VI-RADS score, tumor size, hydronephrosis, and absence of a tumor stalk remained independently associated with muscle invasion. The combined nomogram achieved AUCs of 0.929 (95% CI 0.895-0.963) in training and 0.897 (95% CI 0.839-0.955) in validation, compared with 0.708 for the clinical-radiologic model, 0.798 for VI-RADS, and 0.838 for the radiomics model in validation. Among the 59 patients with a research VI-RADS score of exactly 3, the combined nomogram achieved AUCs of 0.871 (95% CI 0.751-0.991) in training and 0.835 (95% CI 0.625-0.999) in validation; the pooled descriptive AUC was 0.864 (95% CI 0.764-0.964). Because VI-RADS was constant within this subgroup, an informative VI-RADS-alone ROC curve could not be estimated. Validation calibration intercept and slope for the combined model were 0.06 (95% CI -0.20 to 0.32) and 0.91 (95% CI 0.70-1.13), respectively.\n\nConclusion: A combined mpMRI radiomics-clinical-radiologic nomogram showed strong temporal-validation performance for preoperative MIBC prediction, with an exploratory signal of utility in VI-RADS 3 lesions. External multicenter validation is required before clinical implementation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Radiology, PLA Rocket Force Characteristic Medical Center",
    "Department of Urology, PLA Rocket Force Characteristic Medical Center",
    "Department of Radiology, Qianjiang Central Hospital"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Lei Qu"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhihui Qu"
    },
    {
      "affs": [
        1
      ],
      "name": "Xuerong Ye"
    },
    {
      "affs": [
        2
      ],
      "name": "Gang Yuan"
    },
    {
      "affs": [
        0
      ],
      "name": "Liran Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ffj904byb96f0.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1949830",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1949830/full",
  "id": "ffj904byb96f0",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1949830/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": [
    [
      "lcc",
      "RC254-282"
    ],
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7223704359",
  "pdf_url": "https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1949830/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/ffj904byb96f0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:13Z",
      "native_id": "10.3389/fonc.2026.1949830",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1949830/full"
    },
    {
      "first_seen": "2026-10-30T13:47:18Z",
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      "url": "https://doi.org/10.3389/fonc.2026.1949830"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Multiparametric MRI radiomics combined with clinical-radiologic features improves preoperative prediction of muscle invasion in bladder cancer",
  "title_text": "Multiparametric MRI radiomics combined with clinical-radiologic features improves preoperative prediction of muscle invasion in bladder cancer",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:47:18Z",
  "url": "https://research.draconitic.com/paper/ffj904byb96f0",
  "venue": "Frontiers in Oncology",
  "versions": []
}