{
  "abstract": "Rationale and objectives: This study aims to develop a framework integrating dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based multi-channel deep learning combined with clinical modeling for the non-invasive preoperative discrimination between hepatocellular carcinoma (HCC) and mass-forming intrahepatic cholangiocarcinoma (MF-ICC).\n\nMethods: The internal development cohort comprised 334 surgically confirmed patients, including 231 patients with HCC and 103 with MF-ICC, and was divided at the patient level into a training cohort ( n = 233) and an internal validation cohort ( n = 101). The independent external validation cohort included 50 patients, comprising 40 with HCC and 10 with MF-ICC. We constructed separate two-dimensional deep learning models based on arterial phase, portal venous phase, and delayed phase images and then developed fusion models (2D model). A multi-channel deep learning model was further developed by directly inputting the three-phase images stacked along the channel dimension. Moreover, a clinical model was built based on independent predictors identified through multivariate analysis, which was ultimately integrated with the best-performing deep learning model. Model performance was evaluated and compared.\n\nResults: Deep learning model directly feeding the three-phase images as a multi-channel input achieved better discrimination (area under the curve [AUC] = 0.814 in the internal validation cohort) than all single-phase and corresponding fusion models. The comprehensive 2D + multi-channel + clinical model showed the best performance for the differentiation of HCC from MF-ICC, achieving an AUC of 0.887 (95% CI, 0.819–0.954) in the internal validation cohort, and generalized well to the external validation cohort (AUC = 0.908; 95% CI, 0.811–1.000). However, no significant differences in AUCs were found among the models after correction for multiple comparisons.\n\nConclusion: The fusion model integrating clinical predictors with DCE-MRI multi-phase deep learning feature may provide a valuable tool for preoperatively differentiating HCC from MF-ICC.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Medical Imaging, Xinxiang Central Hospital",
    "Department of Nuclear Medicine, The First Affiliated Hospital of Henan Medical University",
    "Department of Magnetic Resonance, The Second Hospital and Clinical Medical School, Lanzhou University",
    "Gansu Province Clinical Research Center for Functional and Molecular Imaging",
    "Gansu Medical MRI Equipment Application Industry Technology Center",
    "Department of Magnetic Resonance, The First Affiliated Hospital of Henan Medical University",
    "Interventional Diagnostic and Therapeutic Center, Zhongnan Hospital of Wuhan University",
    "Department of Radiology, Huaxi MR Research Center (HMRRC), Institute of Radiology and Medical Imaging, West China Hospital of Sichuan University",
    "Psychoradiology Key Laboratory of Sichuan Province, West China Hospital of Sichuan University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Huijie Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Lijuan Guo"
    },
    {
      "affs": [
        2,
        3,
        4
      ],
      "name": "Fei Jia"
    },
    {
      "affs": [
        5
      ],
      "name": "Wenling Liu"
    },
    {
      "affs": [
        5
      ],
      "name": "Meng Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Feng Li"
    },
    {
      "affs": [
        6
      ],
      "name": "Jun Zhou"
    },
    {
      "affs": [
        7,
        8
      ],
      "name": "Baolin Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dc879q8nq01c7.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1936989",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1936989/full",
  "id": "dc879q8nq01c7",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1936989/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",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7223287013",
  "pdf_url": "https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1936989/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/dc879q8nq01c7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:13Z",
      "native_id": "10.3389/fonc.2026.1936989",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1936989/full"
    },
    {
      "first_seen": "2026-10-30T13:43:11Z",
      "native_id": "419cb8be34eb4329b60b67a8cdcc8e3c",
      "source": "doaj",
      "url": "https://doi.org/10.3389/fonc.2026.1936989"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Dynamic contrast-enhanced MRI deep learning model combined with clinical model for noninvasive differentiation of hepatocellular carcinoma from mass-forming intrahepatic cholangiocarcinoma",
  "title_text": "Dynamic contrast-enhanced MRI deep learning model combined with clinical model for noninvasive differentiation of hepatocellular carcinoma from mass-forming intrahepatic cholangiocarcinoma",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:11Z",
  "url": "https://research.draconitic.com/paper/dc879q8nq01c7",
  "venue": "Frontiers in Oncology",
  "versions": []
}