{
  "abstract": "Background: Deep-learning (DL) models may extract nodal-risk information from breast or axillary ultrasound, but the relevant studies differ in anatomical input, preprocessing, reference standard, and validation design.\n\nMethods: We updated PubMed, Web of Science Core Collection, and Cochrane CENTRAL, with supplementary OpenAlex and Crossref searches and repeated backward and forward citation searching through 24 August 2026. Eligible studies evaluated a deep-learning model using ultrasound as the sole imaging modality for pretreatment binary axillary or sentinel nodal status in breast cancer, used surgical pathology as the reference standard, and reported an independent patient-level validation cohort with an exact or uniquely reconstructable 2 × 2 table. One cohort per study was selected using a prespecified hierarchy. Sensitivity and specificity were synthesized with a bivariate random-effects model and an HSROC curve.\n\nResults: Eleven studies comprising 1,027 validation patients were included. Pooled sensitivity was 0.75 (95% CI 0.66–0.81; 95% prediction interval 0.50–0.90) and pooled specificity was 0.89 (95% CI 0.83–0.93; prediction interval 0.66–0.97). The summary HSROC area was 0.81. The positive likelihood ratio was 6.60 (95% CI 4.29–10.42), the negative likelihood ratio was 0.29 (0.21–0.38), and the diagnostic odds ratio was 23.12 (12.89–41.50). Descriptive univariate I² values were 64.2% for sensitivity and 73.8% for specificity. Deeks’ test did not indicate funnel-plot asymmetry (P = 0.677).\n\nConclusions: Ultrasound-based DL models showed moderate sensitivity and high specificity in selected validation cohorts. Wide prediction intervals, heterogeneous anatomical inputs and preprocessing, incompletely reported definitions of nodal positivity, and the geographic concentration of studies limit immediate clinical transportability. These models should be evaluated as adjuncts to, rather than replacements for, pathological axillary staging.\n\nSystematic review registration: https://www.crd.york.ac.uk/PROSPERO/ , identifier CRD42024589342.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Ultrasound, the First Affiliated Hospital of Shihezi University",
    "Tongji Hospital, Affiliated to Tongji Medical College, Huazhong University of Science and Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tianxin Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaowu Yuan"
    },
    {
      "affs": [
        0
      ],
      "name": "Jinli Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Yuchen He"
    },
    {
      "affs": [
        0
      ],
      "name": "Yan Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaojun Yan"
    },
    {
      "affs": [
        0
      ],
      "name": "Chunli Cao"
    },
    {
      "affs": [
        0
      ],
      "name": "Ming Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Wenxiao Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Jun Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Xinwu Cui"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bm6kfdmr0g76b.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1925423",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1925423/full",
  "id": "bm6kfdmr0g76b",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1925423/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": "W7223321063",
  "pdf_url": "https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1925423/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/bm6kfdmr0g76b.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:13Z",
      "native_id": "10.3389/fonc.2026.1925423",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1925423/full"
    },
    {
      "first_seen": "2026-10-30T13:43:11Z",
      "native_id": "65bc37064c564faaad27baab876a6a53",
      "source": "doaj",
      "url": "https://doi.org/10.3389/fonc.2026.1925423"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:RC254-282",
      "subfield": "oncology"
    }
  ],
  "title": "Diagnostic performance of deep learning models using ultrasound for preoperative axillary nodal status in breast cancer: a systematic review and meta-analysis",
  "title_text": "Diagnostic performance of deep learning models using ultrasound for preoperative axillary nodal status in breast cancer: a systematic review and meta-analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:11Z",
  "url": "https://research.draconitic.com/paper/bm6kfdmr0g76b",
  "venue": "Frontiers in Oncology",
  "versions": []
}