ArticleFrontiers in neurology
Identification of symptomatic carotid plaque by CTA-based radiomics: a multicenter study.
Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Plaque-level machine-learning prediction of carotid plaque vulnerability on computed tomography angiography.Neuroradiology · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: To develop and validate a combined model integrating traditional clinical characteristics, imaging features and radiomic features based on head and neck computed tomography angiography (CTA) to predict ischemic events in ipsilateral cerebral vessels. Methods: In this multicenter retrospective study, 223 patients from 3 independent centers were divided into training set ( Results: Univariate analysis and multivariate logistic regression analysis showed that platelet distribution width (PDW) (odds ratio [OR] = 0.88; 95% confidence interval [CI], 0.80-0.97) and plaque ulceration (OR = 5.67; 95% CI, 2.86-11.23) were independently related to symptomatic plaque. Twelve radiomic features significantly related to symptomatic plaque were selected. The combined model demonstrated superior performance compared with both the radiomic model and the traditional model, the AUCs of the training set and internal test set were 0.819(95% CI: 0.749-0.888) and 0.785(95% CI: 0.620-0.950), and also demonstrated robust performance in external validation set (AUC: 0.868; 95% CI: 0.765-0.970). Conclusion: The Combined model demonstrated the highest diagnostic performance in identifying symptomatic plaque, which helps clinicians to analyze patients' condition more comprehensively and provides additional value for identifying high-risk individuals and improving prognosis.
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