{
  "abstract": "Background: BI-RADS category 4 breast lesions are suspicious and generally undergo tissue diagnosis, but malignancy risk remains heterogeneous across BI-RADS 4A, 4B, and 4C. We developed and internally validated an interpretable mammography-based model for risk stratification within this diagnostic category.\n\nMethods: This single-center retrospective diagnostic prediction study included 441 patients, each contributing one index BI-RADS 4 mammographic lesion. Outcomes were verified histopathologically in 397 lesions and by longitudinal imaging stability in 44. Three primary models and a sensitivity model excluding BI-RADS were evaluated. Internal validation used 1,000 paired patient-level bootstrap resamples in which imputation, encoding, standardization, penalty selection, LASSO selection, and coefficient estimation were repeated.\n\nResults: Of 441 lesions, 165 were malignant. Malignancy rates were 15.7% (34/216), 46.4% (70/151), and 82.4% (61/74) in BI-RADS 4A, 4B, and 4C, respectively. The final model retained BI-RADS 4B, BI-RADS 4C, architectural distortion, circularity-deficit irregularity score, contour serration score, and normalized GLCM contrast. Optimism-corrected AUCs were 0.770 (95% CI, 0.729-0.815) for BI-RADS only, 0.774 (0.733-0.814) for clinical plus BI-RADS, and 0.841 (0.809-0.877) for the full mammography-based model. Clinical variables did not improve validated performance. The full model improved corrected AUC versus BI-RADS only by 0.072 (0.028-0.108) and versus clinical plus BI-RADS by 0.067 (0.029-0.103). Its corrected calibration intercept was 0.002 (-0.209 to 0.208) and slope was 1.445 (1.140-1.753). Within-category AUCs were 0.785, 0.721, and 0.758 for BI-RADS 4A, 4B, and 4C; the pathology-only corrected AUC was 0.835 (0.799-0.871).\n\nConclusion: Structured and quantitative mammographic features improved internally validated discrimination and within-category risk stratification beyond BI-RADS subcategory alone. Because predicted risks were under-dispersed and validation was limited to one center, the model should be used only as a research-stage risk-stratification and prioritization tool, not to replace biopsy. External validation and recalibration are required before clinical implementation.",
  "abstract_shown": true,
  "affiliations": [
    "Linfen People’s Hospital"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yanheng Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Nan Ding"
    },
    {
      "affs": [
        0
      ],
      "name": "Tingting Jiao"
    },
    {
      "affs": [
        0
      ],
      "name": "Bin Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/46m1jwb6wrb0s.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1900262",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1900262/full",
  "id": "46m1jwb6wrb0s",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1900262/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",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7223667300",
  "pdf_url": "https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1900262/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/46m1jwb6wrb0s.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:13Z",
      "native_id": "10.3389/fonc.2026.1900262",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1900262/full"
    },
    {
      "first_seen": "2026-10-30T13:47:18Z",
      "native_id": "27053f0a01cc4a4f9f83b277145aef47",
      "source": "doaj",
      "url": "https://doi.org/10.3389/fonc.2026.1900262"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:RC254-282",
      "subfield": "oncology"
    }
  ],
  "title": "Development and internal validation of an interpretable mammography-based model for malignancy risk stratification in BI-RADS 4 breast lesions",
  "title_text": "Development and internal validation of an interpretable mammography-based model for malignancy risk stratification in BI-RADS 4 breast lesions",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:47:18Z",
  "url": "https://research.draconitic.com/paper/46m1jwb6wrb0s",
  "venue": "Frontiers in Oncology",
  "versions": []
}