{
  "abstract": "Background: Accurate etiological differentiation between hepatitis B virus (HBV)-related cirrhosis and schistosomiasis-related cirrhosis remains clinically important in endemic regions because these conditions differ substantially in their underlying pathogenesis, clinical management, and prognosis. A non-invasive imaging approach may provide complementary information for etiological assessment. To develop a CT-based clinical-radiomics model for the non-invasive differentiation of HBV-related and schistosomiasis-related cirrhosis and to evaluate its diagnostic performance in a multicenter cohort.\n\nMethods: This retrospective multicenter study included 143 patients with HBV-related or schistosomiasis-related cirrhosis from three institutions between August 2019 and December 2021. Radiomics features were extracted from non-contrast CT images, and a radiomics signature (Rad-score) was established using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression. Clinical and integrated clinical-radiomics models were subsequently developed using multivariable logistic regression. Model performance was evaluated using receiver operating characteristic analysis, calibration curves, and decision curve analysis (DCA).\n\nResults: The Rad-score was significantly lower in patients with HBV-related cirrhosis than in those with schistosomiasis-related cirrhosis (both P < 0.001 in the training and internal validation cohorts). The integrated clinical-radiomics model demonstrated excellent diagnostic performance, with area under the receiver operating characteristic curve (AUC) values of 0.990 in the training cohort, 0.920 in the internal validation cohort, and 0.782 in the external validation cohort. Compared with the clinical model alone, both the radiomics and integrated models demonstrated more consistent performance across the internal and external validation cohorts, while the integrated model showed the greatest overall clinical utility on DCA.\n\nConclusion: The proposed CT-based clinical-radiomics model provides a promising non-invasive approach for differentiating HBV-related and schistosomiasis-related cirrhosis. As a complementary imaging tool, it may assist etiological assessment in endemic regions when interpreted alongside routine clinical and laboratory evaluation. Further prospective multicenter studies are warranted to validate its clinical utility.\n\nSupplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12880-026-02693-6.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1277 Jiefang Avenue, Wuhan 430022 Hubei, P. R. China",
    "Department of Radiology, Chinese PLA General Hospital of Central Theater Command, Wuhan, China",
    "Department of Radiology, The People’s Hospital of Honghu, Honghu, China",
    "Hubei Provincial Clinical Medical Research Center for Medical Imaging, Wuhan, Hubei China",
    "Department of Radiology, The First Affiliated Hospital of USTC (Anhui Provincial Hospital), Hefei, China",
    "Key Laboratory of Biological Targeted Therapy, the Ministry of Education, Wuhan, China",
    "Department of Radiology, Honghu Fourth People’s Hospital, Honghu, China",
    "Regenerative Medicine Research Center, West China Hospital, Chengdu, China",
    "Department of Ultrasound, The People’s Hospital of Honghu, Honghu, China",
    "Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
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        0,
        1,
        2,
        3
      ],
      "name": "Junjie Tong"
    },
    {
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        0,
        4
      ],
      "name": "Xiaojun Xu"
    },
    {
      "affs": [
        0,
        3,
        5
      ],
      "name": "Fan Hu"
    },
    {
      "affs": [
        6
      ],
      "name": "Zhengquan Wang"
    },
    {
      "affs": [
        7
      ],
      "name": "Ling Ma"
    },
    {
      "affs": [
        8
      ],
      "name": "Jieming Li"
    },
    {
      "affs": [
        3,
        9
      ],
      "name": "Xin Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Zhong Yang"
    },
    {
      "affs": [
        0,
        3,
        5
      ],
      "name": "Xiaotian Xia",
      "orcid": "0000-0002-3917-4361"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ay6pqtzcj6ejy.bib",
  "cited_by": 0,
  "doi": "10.1186/s12880-026-02693-6",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13711848/",
  "id": "ay6pqtzcj6ejy",
  "issns": [
    "1471-2342"
  ],
  "landing_url": "https://doi.org/10.1186/s12880-026-02693-6",
  "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",
      "Research"
    ],
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      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
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  "openalex": "W7204830298",
  "pdf_url": null,
  "pmcid": "PMC13711848",
  "pmid": "42934564",
  "published": "2026-08-31",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/ay6pqtzcj6ejy.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:22:33Z",
      "native_id": "PMC13711848",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s12880-026-02693-6"
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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-based radiomics combined with clinical features for non-invasive differentiation of hepatitis B and schistosomiasis cirrhosis: a multicenter study",
  "title_text": "CT-based radiomics combined with clinical features for non-invasive differentiation of hepatitis B and schistosomiasis cirrhosis: a multicenter study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:22:33Z",
  "url": "https://research.draconitic.com/paper/ay6pqtzcj6ejy",
  "venue": "BMC Medical Imaging",
  "versions": []
}