{
  "abstract": "Background: Spontaneous intracranial vertebrobasilar artery dissection (sIVBD) has heterogeneous clinical presentations, and conventional clinical and morphologic assessment may not fully characterize lesion heterogeneity. We aimed to develop and internally validate CTA-based radiomics models for differentiating symptomatic from asymptomatic unruptured sIVBD and to evaluate the incremental contribution of radiomics beyond conventional clinical and CTA features.\n\nMethods: This retrospective single-center study included 173 patients with unruptured sIVBD, comprising 56 symptomatic and 117 asymptomatic patients. Patients were stratified into a development cohort ( n = 138) and an independent internal validation cohort ( n = 35). Radiomic features were extracted from subtraction CTA images after three-dimensional lesion segmentation. Feature reproducibility was assessed using inter-reader intraclass correlation coefficients. Four models were developed: a conventional CTA model (M1), a clinical plus conventional CTA model (M2), a radiomics XGBoost model (M3), and a combined model integrating M2 predictors with a cross-fitted radiomics probability (M4). Primary development-cohort evaluation used five-times-repeated five-fold stratified nested cross-validation. Discrimination, calibration, Brier score, and decision-curve analysis were assessed.\n\nResults: Of 851 radiomic features, 722 (84.8%) met the prespecified reproducibility criterion. In patient-averaged out-of-fold evaluation, AUCs were 0.630 for M1, 0.681 for M2, 0.676 for M3, and 0.723 for M4. Compared with M2, M4 increased AUC by 0.0416 (95% CI, −0.0130 to 0.0962; p = 0.136) and reduced the Brier score by 0.0101 (95% bootstrap CI, −0.0208 to 0.0006). Across 25 outer folds, M4 had the highest mean AUC (0.715) and the lowest between-fold variability. In the independent internal validation cohort, AUCs were 0.814, 0.807, 0.803, and 0.966 for M1 through M4, respectively.\n\nConclusion: A combined model integrating clinical variables, conventional CTA features, and cross-fitted radiomics information showed the best overall performance for differentiating symptomatic from asymptomatic unruptured sIVBD. Radiomics may provide complementary quantitative information beyond conventional assessment.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jia Jiang"
    },
    {
      "affs": [
        0
      ],
      "name": "Sheng Bi"
    },
    {
      "affs": [
        0
      ],
      "name": "Siming Gui"
    },
    {
      "affs": [
        0
      ],
      "name": "Lu Zheng"
    },
    {
      "affs": [
        0
      ],
      "name": "Youxiang Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Qinglin Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a3th8dvwtaaee.bib",
  "cited_by": 0,
  "doi": "10.3389/fneur.2026.1883223",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fneur.2026.1883223/full",
  "id": "a3th8dvwtaaee",
  "issns": [
    "1664-2295"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fneur.2026.1883223/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",
      "RC346-429"
    ],
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7223285098",
  "pdf_url": "https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1883223/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/a3th8dvwtaaee.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:16Z",
      "native_id": "10.3389/fneur.2026.1883223",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fneur.2026.1883223/full"
    },
    {
      "first_seen": "2026-10-30T13:43:12Z",
      "native_id": "b32fc02554cc4d26bb23b9ac8628d10d",
      "source": "doaj",
      "url": "https://doi.org/10.3389/fneur.2026.1883223"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Development and internal validation of CTA-based radiomics models for differentiating symptomatic from asymptomatic unruptured spontaneous intracranial vertebrobasilar artery dissection",
  "title_text": "Development and internal validation of CTA-based radiomics models for differentiating symptomatic from asymptomatic unruptured spontaneous intracranial vertebrobasilar artery dissection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:12Z",
  "url": "https://research.draconitic.com/paper/a3th8dvwtaaee",
  "venue": "Frontiers in Neurology",
  "versions": []
}