{
  "abstract": "Introduction: Chromosomal instability (CIN) is an important biomarker for breast cancer diagnosis, prognosis, and treatment stratification. However, conventional laboratory-based CIN assessment is often time-consuming and costly, while existing deep learning methods face challenges in integrating local cellular details with broader tissue context from whole-slide images (WSIs). Methods: We propose a multi-scale cross-attention neural network (MSCAnet) for CIN status prediction from hematoxylin and eosin (H&E)-stained breast cancer WSIs. MSCAnet employs dual patch-level branches to extract complementary nuclear and tissue structural features, while a CIN-aware cross-attention module dynamically integrates multi-scale information. The model was evaluated on The Cancer Genome Atlas Breast Invasive Carcinoma dataset and further assessed on an additional human epidermal growth factor receptor 2 (HER2) status prediction task. Results: MSCAnet achieved an accuracy of 77.46% and an area under the receiver operating characteristic curve of 0.809 for CIN prediction, demonstrating the highest numerical performance among the evaluated methods. However, the overlapping 95% confidence intervals with those of the strongest baselines precluded definitive conclusions regarding statistical superiority. Visualization analyses revealed spatial heterogeneity in CIN-associated tissue patterns. In the additional HER2 prediction task, MSCAnet achieved an accuracy of 82.72%, providing preliminary evidence of its applicability across molecular biomarker prediction tasks. Conclusions: MSCAnet demonstrates the potential of multi-scale cross-attention mechanisms for predicting molecular biomarkers from routine H&E-stained WSIs. Although the results are promising, further validation on larger, independent cohorts is required to establish the model's robustness, generalizability, and clinical utility.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Intelligent Science and Technology, School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China",
    "College of Artificial Intelligence, Henan Agricultural University, Zhengzhou, Henan 450046, China",
    "College of Artificial Intelligence, Henan Agricultural University, Zhengzhou, Henan 450046, China; Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China; Corresponding author at: College of Artificial Intelligence, Henan Agricultural University, Zhengzhou, Henan 450046, China."
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ying Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Yue Hu"
    },
    {
      "affs": [
        2
      ],
      "name": "Liangliang Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/akmgf70rdwsaw.bib",
  "cited_by": 0,
  "doi": "10.1016/j.intonc.2026.100091",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S2950261626000543",
  "id": "akmgf70rdwsaw",
  "issns": [
    "2950-2616",
    "2097-7131"
  ],
  "landing_url": "https://doi.org/10.1016/j.intonc.2026.100091",
  "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",
      "R858-859.7"
    ],
    [
      "lcc",
      "RC254-282"
    ],
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7214618751",
  "pdf_url": "https://www.sciencedirect.com/science/article/pii/S2950261626000543/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": "KeAi Communications Co., Ltd.",
  "ris": "https://research.draconitic.com/paper/akmgf70rdwsaw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:53Z",
      "native_id": "32118af8e5ca4166a93b2f98499ac015",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.intonc.2026.100091"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:RC254-282",
      "subfield": "oncology"
    }
  ],
  "title": "MSCAnet: A multi-scale cross-attention network for chromosomal instability prediction in breast cancer whole-slide images",
  "title_text": "MSCAnet: A multi-scale cross-attention network for chromosomal instability prediction in breast cancer whole-slide images",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:53Z",
  "url": "https://research.draconitic.com/paper/akmgf70rdwsaw",
  "venue": "Intelligent Oncology",
  "versions": []
}