{
  "abstract": "Objective: To develop and validate an integrated model combining multiphase CT radiomics features, clinical data, and conventional morphological CT signs for the preoperative differentiation of benign and malignant thyroid nodules.\n\nMethods: We retrospectively enrolled patients with surgically confirmed thyroid nodules who underwent preoperative contrast-enhanced thyroid CT examination at two centers. Patients from center 1 were randomly allocated to a training cohort and an internal validation cohort at an 8:2 ratio; patients from center 2 served as an external validation cohort. Clinical characteristics and morphological features of nodules were assessed. Radiomics features were extracted from all three CT phases, and four categories of models were constructed: clinical model, single-phase CT radiomics models, multiphase CT radiomics model, and combined clinical-radiomics model. Model performance and interpretability were evaluated using receiver operating characteristic (ROC) curve analysis, calibration curves, decision curve analysis (DCA), and SHapley Additive exPlanations (SHAP).\n\nResults: The multiphase CT radiomics model yielded areas under the ROC curve (AUCs) of 0.919, 0.836, and 0.787 in the training, internal validation, and external validation cohorts, respectively. Among the single-phase models, the venous-phase model achieved the best external validation performance (AUC = 0.815). The combined model, integrating clinical and morphological features with multiphase radiomics features, achieved the highest overall diagnostic performance, with an external validation AUC of 0.865 (sensitivity = 74.0%, specificity = 84.5%), significantly outperforming both the clinical model (AUC = 0.800) and the multiphase radiomics model (AUC = 0.787). It also demonstrated favorable calibration and greater clinical net benefit.\n\nConclusion: Multiphase CT radiomics enables effective differentiation of benign and malignant thyroid nodules. Integration of clinical and morphological information further improves diagnostic performance, providing an objective, noninvasive tool for preoperative risk stratification of incidentally detected thyroid nodules on CT.",
  "abstract_shown": true,
  "affiliations": [
    "Jinhua Graduate Joint Training Base, Zhejiang Chinese Medical University",
    "Department of Radiology, Wenzhou Medical University Affiliated Quzhou Hospital (Quzhou People’s Hospital)",
    "Department of Quzhou Key Laboratory of Respiratory Mechanics in Critical Care Medicine, Wenzhou Medical University Affiliated Quzhou Hospital (Quzhou People’s Hospital)",
    "Department of Radiology, Ningbo No. 2 Hospital"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Ning Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Jianan Tong"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Guozheng Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Shufeng Xu"
    },
    {
      "affs": [
        1
      ],
      "name": "Weitao Huang"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiaowei Han"
    },
    {
      "affs": [
        3
      ],
      "name": "Jingfeng Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Xisong Zhu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/edjvq75pv6ymz.bib",
  "cited_by": 0,
  "doi": "10.3389/fendo.2026.1928263",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13716650/?tool=EBI",
  "id": "edjvq75pv6ymz",
  "issns": [
    "1664-2392"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fendo.2026.1928263/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"
    ],
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
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  "openalex": "W7221956804",
  "pdf_url": "https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1928263/pdf",
  "pmcid": "PMC13716650",
  "pmid": "42959093",
  "published": "2026-10-15",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/edjvq75pv6ymz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:20Z",
      "native_id": "10.3389/fendo.2026.1928263",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fendo.2026.1928263/full"
    },
    {
      "first_seen": "2026-10-31T00:20:37Z",
      "native_id": "PMC:PMC13716650",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13716650"
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    {
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      "url": "https://europepmc.org/article/MED/42959093"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
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  ],
  "title": "Computed tomography multi-phase radiomics models for differentiating benign and malignant thyroid nodules",
  "title_text": "Computed tomography multi-phase radiomics models for differentiating benign and malignant thyroid nodules",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T12:21:08Z",
  "url": "https://research.draconitic.com/paper/edjvq75pv6ymz",
  "venue": "Frontiers in Endocrinology",
  "versions": []
}