{
  "abstract": "Background: This study developed and validated an machine-learning-based radiomics model integrating tumor habitat radiomics, peritumoral radiomics, and clinical features to predict progression-free survival (PFS) following repeat transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC).\n\nMethods: This retrospective multicenter study included 270 patients with HCC. Patients from Center 1 were assigned to training (n = 94) and internal validation (n = 41) cohorts, while Centers 2 (n = 82) and 3 (n = 53) served as external validation cohorts. Tumor habitats and 5-mm peritumoral regions were generated from contrast-enhanced CT images. Radiomics features were selected using univariate Cox and LASSO-Cox regression. A fusion model and HPC risk score were constructed by integrating selected radiomics signatures and clinical variables. Performance was assessed using time-dependent ROC, C-index, calibration, decision curve analysis, and Kaplan–Meier analyses.\n\nResults: The optimal habitat cluster number was K = 2. Eleven habitat features, three peritumoral features, and alpha-fetoprotein were retained in the fusion model. The model achieved AUCs of 0.923, 0.921, and 0.897 for 3-, 6-, and 9-month progression-free survival in the training cohort, and 0.932, 0.863, and 0.916 in the internal validation cohort, respectively; all external AUCs exceeded 0.82. The fusion model showed higher C-index values than the clinical model across cohorts. High-risk patients had significantly shorter PFS than low-risk patients (all P < 0.001). Calibration and decision curve analyses demonstrated good agreement and clinical utility.\n\nConclusion: The CT-based fusion model and HPC risk score effectively predicted progression risk in HCC.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Interventional Radiology, The Second Affiliated Hospital of Harbin Medical University",
    "Department of Central Sterile Supply, The Second Affiliated Hospital of Harbin Medical University",
    "Department of Hepatobiliary Surgery, The First Affiliated Hospital of Harbin Medical University",
    "Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
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        0
      ],
      "name": "Fei Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Yue Hu"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhong-Xing Shi"
    },
    {
      "affs": [
        0
      ],
      "name": "Xin-Yu He"
    },
    {
      "affs": [
        2
      ],
      "name": "Yu-Hang Sui"
    },
    {
      "affs": [
        2
      ],
      "name": "Fu-Kai Wen"
    },
    {
      "affs": [
        3
      ],
      "name": "Jia-Shan Zhan"
    },
    {
      "affs": [
        3
      ],
      "name": "Ning-Jing Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "Xue-Jing Han"
    },
    {
      "affs": [
        0
      ],
      "name": "Jia-Hui Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jia-Yi Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "Wen Lv"
    },
    {
      "affs": [
        0
      ],
      "name": "Hong-Fei Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Li-Ming Cui"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6dxq5ptm0yjet.bib",
  "cited_by": 0,
  "doi": "10.3389/fphar.2026.1942002",
  "fulltext_url": "https://europepmc.org/articles/PMC13713701",
  "id": "6dxq5ptm0yjet",
  "issns": [
    "1663-9812"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fphar.2026.1942002/full",
  "language": "eng",
  "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",
      "Original Research"
    ],
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      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
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  "pdf_url": "https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1942002/pdf",
  "pmcid": "PMC13713701",
  "pmid": "42953006",
  "published": "2026-10-14",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/6dxq5ptm0yjet.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:06Z",
      "native_id": "10.3389/fphar.2026.1942002",
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      "url": "https://www.frontiersin.org/articles/10.3389/fphar.2026.1942002/full"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
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  ],
  "title": "CT habitat and peritumoral radiomics for predicting progression after repeat TACE in hepatocellular carcinoma: a multicenter study",
  "title_text": "CT habitat and peritumoral radiomics for predicting progression after repeat TACE in hepatocellular carcinoma: a multicenter study",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:21:19Z",
  "url": "https://research.draconitic.com/paper/6dxq5ptm0yjet",
  "venue": "Frontiers in Pharmacology",
  "versions": []
}