{
  "abstract": "Accurate prognostication is crucial for the individualized management of patients with colorectal cancer (CRC), while the accuracy and interpretability of current models still need to be improved. This study aims to develop and validate an explainable multimodal model for precise survival prediction. 3381 patients with resected CRC are enrolled from three medical centers, and a multimodal deep‐learning framework integrating clinical variables and histopathological images is developed. The multimodal model exhibits significantly more robust performance than unimodal models in prognosis prediction across all of the cohorts. By stratifying patients into low‐risk and high‐risk groups based on the model‐derived risk scores, high‐risk patients exhibit worse outcomes, and the stratification remains an independent prognostic factor in multivariable Cox regression. The model also identifies patients with markedly poorer survival among the stage II/III subgroup. Adjuvant chemotherapy demonstrates benefits in high‐risk patients with stage III CRC but not in the low‐risk group. Multi‐omic analyses disclose that low‐risk tumors are characterized by enhanced adaptive immune responses, whereas high‐risk tumors exhibit stronger angiogenesis and higher exhaustion scores of T cells, reflecting a hypoxic and immunosuppressive microenvironment. These results support the potential of the multimodal model to effectively predict survival outcomes in resected CRC with great explainability.",
  "abstract_shown": true,
  "affiliations": [
    "Department of General Surgery, Peking University Third Hospital Peking University Beijing People's Republic of China",
    "Peking University Third Hospital Cancer Center Peking University Beijing People's Republic of China",
    "Beijing Key Laboratory for Interdisciplinary Research in Gastrointestinal Oncology (BLGO) Beijing People's Republic of China",
    "Department of General Surgery, The First Medical Center Chinese PLA General Hospital Beijing People's Republic of China",
    "School of Biomedical Sciences, Faculty of Medicine and Health UNSW Sydney Kensington Australia",
    "Office of DVCR Monash University Melbourne Australia",
    "Image Processing Center Beihang University Beijing People's Republic of China",
    "State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing People's Republic of China",
    "Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technology, Ministry of Education Beihang University Beijing People's Republic of China",
    "School of Clinical Medicine, Faculty of Medicine and Health UNSW Sydney Kensington Australia",
    "SREDH Consortium Sydney Australia"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Zhihan Jiang",
      "orcid": "0000-0002-7943-464X"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Xiangchao Shi"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Chenghan Su"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Yao Ma"
    },
    {
      "affs": [
        3
      ],
      "name": "Rui Li"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Menglei Chen"
    },
    {
      "affs": [
        3
      ],
      "name": "Jianxin Cui",
      "orcid": "0000-0002-7233-4590"
    },
    {
      "affs": [
        4
      ],
      "name": "Nicholas Hawkins"
    },
    {
      "affs": [
        5
      ],
      "name": "Robyn Ward"
    },
    {
      "affs": [
        6,
        7,
        8
      ],
      "name": "Xiangzhi Bai",
      "orcid": "0000-0002-6115-8237"
    },
    {
      "affs": [
        9,
        10
      ],
      "name": "Jitendra Jonnagaddala",
      "orcid": "0000-0002-9912-2344"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Wei Fu",
      "orcid": "0000-0001-5248-7891"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Xin Zhou",
      "orcid": "0000-0002-4048-4017"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/64abt085wysdz.bib",
  "cited_by": 0,
  "doi": "10.1002/advs.78417",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13710700/",
  "id": "64abt085wysdz",
  "issns": [
    "2198-3844"
  ],
  "landing_url": "https://doi.org/10.1002/advs.78417",
  "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": [
    [
      "heading",
      "Research Article"
    ],
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7224008393",
  "pdf_url": null,
  "pmcid": "PMC13710700",
  "pmid": "42934824",
  "published": "2026-10-26",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/64abt085wysdz.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:37Z",
      "native_id": "PMC13710700",
      "source": "pmc",
      "url": "https://doi.org/10.1002/advs.78417"
    }
  ],
  "subjects": [],
  "title": "An Explainable Multimodal Deep‐Learning Model Using Histopathological Image and Clinical Information for Prognosis Prediction in Colorectal Cancer: A Multi‐Center Cohort Study",
  "title_text": "An Explainable Multimodal Deep‐Learning Model Using Histopathological Image and Clinical Information for Prognosis Prediction in Colorectal Cancer: A Multi‐Center Cohort Study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:21:37Z",
  "url": "https://research.draconitic.com/paper/64abt085wysdz",
  "venue": "Advanced Science",
  "versions": []
}