{
  "abstract": "Background Neoadjuvant therapy (NAT) is crucial for locally advanced breast cancer, but post-NAT lymph node assessment is challenging due to histological changes. Current methods like immunohistochemistry (IHC) are labor-intensive and imprecise in distinguishing isolated tumor cells (ITCs), micro-metastases (Micro), and macro-metastases (Macro). We aimed to develop and validate an AI-driven model for precise classification of lymph node metastasis status (negative, ITC, Micro, Macro) in breast cancer patients post-NAT. Methods We used a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM) framework to analyze 7764 lymph node samples from 7 cohorts. The CLAM model identifies high-diagnostic-value subregions within whole-slide images (WSIs) and generates high-resolution interpretability heatmaps. Performance was evaluated using binary and multi-class metrics, with external validation on diverse datasets. A human-AI comparative analysis was conducted on 24 patient-derived lymph node sections. Results The AI model achieved an AUROC of 0.97 (95% CI: 0.962–0.977) in binary classification and an overall accuracy of 0.8436 (95% CI: 0.8282–0.8562) for multi-class differentiation. In the human-AI comparison, the model outperformed junior pathologists, reducing diagnostic discrepancies by 83%. Conclusion This study establishes a robust AI model that significantly improves the accuracy and efficiency of post-NAT lymph node metastasis assessment in breast cancer, automating classification and reducing pathologist workload.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Pathology, The Fourth Hospital of Hebei Medical University",
    "Medical Affairs Department, Betrue AI Lab",
    "Department of Pathology, Affiliated Hospital of Hebei University",
    "Department of Pathology, Xingtai People’s Hospital",
    "Department of Pathology, First Hospital of Qinhuangdao",
    "Department of Pathology, Handan First Hospital",
    "Department of Pathology, Cangzhou Integrated Traditional Chinese and Western Medicine Hospital",
    "Department of Pathology, The Ohio State University Wexner Medical Center"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yan Ding"
    },
    {
      "affs": [
        0
      ],
      "name": "Juan Yu"
    },
    {
      "affs": [
        1
      ],
      "name": "Min Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiangyu Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Ling Kang"
    },
    {
      "affs": [
        1
      ],
      "name": "Liujing Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jinze Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Yanan Wang"
    },
    {
      "affs": [
        3
      ],
      "name": "Xin Xu"
    },
    {
      "affs": [
        4
      ],
      "name": "Min Zhao"
    },
    {
      "affs": [
        5
      ],
      "name": "Ping Wei"
    },
    {
      "affs": [
        6
      ],
      "name": "Shuangbiao Li"
    },
    {
      "affs": [
        7
      ],
      "name": "Zaibo Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Yueping Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2ynbc35z8g4yg.bib",
  "cited_by": 0,
  "doi": "10.1186/s13058-026-02349-x",
  "fulltext_url": "https://doi.org/10.1186/s13058-026-02349-x",
  "id": "2ynbc35z8g4yg",
  "issns": [
    "1465-542X",
    "1465-5411"
  ],
  "landing_url": "https://doi.org/10.1186/s13058-026-02349-x",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RC254-282"
    ],
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7169829793",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/2ynbc35z8g4yg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:46:33Z",
      "native_id": "699472dd428e4515a61d8aad79b03352",
      "source": "doaj",
      "url": "https://doi.org/10.1186/s13058-026-02349-x"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:RC254-282",
      "subfield": "oncology"
    }
  ],
  "title": "Enhancing lymph node metastases assessment in breast cancer post-neoadjuvant therapy using artificial intelligence-driven diagnostics",
  "title_text": "Enhancing lymph node metastases assessment in breast cancer post-neoadjuvant therapy using artificial intelligence-driven diagnostics",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:46:33Z",
  "url": "https://research.draconitic.com/paper/2ynbc35z8g4yg",
  "venue": "Breast Cancer Research",
  "versions": []
}