ArticleFrontiers in cardiovascular medicine2024
Identification of patients with unstable angina based on coronary CT angiography: the application of pericoronary adipose tissue radiomics.
Article in Frontiers in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Correlation Between Fat Attenuation Index and Major Adverse Cardiovascular Events: A Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2026Pooled it
- AI-driven healthcare: a trend toward better healthcare or the emergence of public health burden.Frontiers in digital health · 2026Article
- Computed tomography-based assessment of pericoronary adipose tissue in cardiovascular diseases: Diagnostic and prognostic implications.World journal of radiology · 2025Review
- The Impact of Preoperative Risk Factors on Delayed Discharge in Day Surgery: A Meta-Analysis.Healthcare (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Objective: To explore whether radiomics analysis of pericoronary adipose tissue (PCAT) captured by coronary computed tomography angiography (CCTA) could discriminate unstable angina (UA) from stable angina (SA). Methods: In this single-center retrospective case-control study, coronary CT images and clinical data from 240 angina patients were collected and analyzed. Patients with unstable angina ( Results: In both training and validation cohorts, the integrated model (AUC = 0.87, 0.74) demonstrated superior discriminatory ability compared to the FAI model (AUC = 0.68, 0.51), clinical feature model (AUC = 0.84, 0.67), and radiomic model (AUC = 0.85, 0.73). The nomogram derived from the combined radiomic and clinical features exhibited excellent performance in diagnosing and predicting unstable angina. Calibration curves showed good fit for all four machine learning models. Decision curve analysis indicated that the integrated model provided better clinical benefit than the other three models. Conclusions: CCTA-based radiomics signature of PCAT is better than the FAI model in identifying unstable angina and stable angina. The integrated model constructed by combining radiomics and clinical features could further improve the diagnosis and differentiation ability of unstable angina.
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Registered trials
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