{
  "abstract": "Head and neck squamous cell carcinoma (HNSC) exhibits substantial molecular and morphological heterogeneity, yet current morphology‐based prognostic models often lack biological interpretability. We developed a transformer‐based computational framework (CTransPath) to virtually predict gene expression directly from hematoxylin and eosin (H&E) whole‐slide images. Morphology‐associated genes were identified from the TCGA‐HNSC cohort, and a 32‐gene morphology‐guided prognostic signature (MGP_HNSC32) based on H&E‐derived predicted gene expression was developed using Random Survival Forest and multivariable Cox regression modeling. In TCGA‐HNSC, the MGP_HNSC32 risk score significantly stratified patients by progression‐free interval (PFI; HR = 4.13, 95% CI: 2.84–6.02), overall survival (OS; HR = 2.42, 95% CI: 1.78–3.29), and disease‐specific survival (DSS; HR = 4.88, 95% CI: 2.95–8.06; all p < 0.001), and remained independently associated with prognosis after multivariable adjustment. High‐risk tumors were enriched for cell cycle, E2F, MYC, and epithelial‐mesenchymal transition pathways and displayed poorly differentiated histology with nuclear atypia and stromal desmoplasia. In contrast, low‐risk tumors showed keratinizing morphology and immune‐rich stroma, corresponding to differentiation‐ and metabolism‐related transcriptional programs. In the independent HANCOCK cohort (n = 701), risk scores calculated entirely from H&E‐derived predicted gene expression using the TCGA‐derived coefficients and prespecified cutoff were associated with progression‐free survival (HR = 1.40, 95% CI: 1.03–1.90, p = 0.034). However, the continuous risk score was not significantly associated with progression‐free survival (HR = 1.001, 95% CI: 0.999–1.003, p = 0.328). MGP_HNSC32 links histomorphology to transcriptional programs and demonstrates preliminary prognostic associations using routine H&E images. The H&E‐based gene‐expression prediction component should be considered proof‐of‐concept, and further independent and prospective validation is required before clinical implementation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Otorhinolaryngology Second Affiliated Hospital of Zhejiang University School of Medicine Hangzhou China",
    "Department of Radiation Oncology Second Affiliated Hospital of Zhejiang University School of Medicine Hangzhou China",
    "Department of Pathology The Second Affiliated Hospital, Zhejiang University School of Medicine Hangzhou China",
    "Department of Otorhinolaryngology, Head and Neck Surgery University Hospital Heidelberg Heidelberg Germany",
    "Division Radiooncology/Radiobiology German Cancer Research Center (DKFZ) Heidelberg Germany",
    "Artificial Intelligence Research Center, Zhejiang Key Laboratory of Medical Epigenetics, Department of Cell Biology, School of Basic Medical Sciences Hangzhou Normal University Hangzhou China",
    "Jinshiyan Biotechnology Yongkang China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Di Zhao"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhiheng Huang"
    },
    {
      "affs": [
        1
      ],
      "name": "Jing Xu",
      "orcid": "0000-0003-4592-0057"
    },
    {
      "affs": [
        2
      ],
      "name": "Zheng Zhang"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Jochen Hess",
      "orcid": "0000-0003-3493-1711"
    },
    {
      "affs": [
        3,
        5,
        6
      ],
      "name": "Bohai Feng",
      "orcid": "0000-0003-0498-4230"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4v3wx5mxdc5y3.bib",
  "cited_by": 0,
  "doi": "10.1002/cam4.72427",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13696845/",
  "id": "4v3wx5mxdc5y3",
  "issns": [
    "2045-7634"
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  "landing_url": "https://doi.org/10.1002/cam4.72427",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Artificial Intelligence in Cancer Care"
    ],
    [
      "heading",
      "RESEARCH ARTICLE"
    ],
    [
      "heading",
      "Research Article"
    ],
    [
      "lcc",
      "RC254-282"
    ],
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      "openalex",
      "1702|AI in cancer detection"
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  "openalex": "W7223576091",
  "pdf_url": "https://europepmc.org/articles/PMC13696845?pdf=render",
  "pmcid": "PMC13696845",
  "pmid": "42928519",
  "published": "2026-10-25",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/4v3wx5mxdc5y3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:07Z",
      "native_id": "PMC13696845",
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      "url": "https://doi.org/10.1002/cam4.72427"
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    {
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  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:RC254-282",
      "subfield": "oncology"
    }
  ],
  "title": "MGP_HNSC32: A H&E Morphology‐Derived 32‐Gene Prognostic Signature in Head and Neck Squamous Cell Carcinoma",
  "title_text": "MGP_HNSC32: A H&E Morphology‐Derived 32‐Gene Prognostic Signature in Head and Neck Squamous Cell Carcinoma",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:47:20Z",
  "url": "https://research.draconitic.com/paper/4v3wx5mxdc5y3",
  "venue": "Cancer Medicine",
  "versions": []
}