ArticleCardiology journal2025
Differentiation of non-ST-segment elevation myocardial infarction from unstable angina using coronary computed tomography angiography: the role of imaging features and pericoronary adipose tissue radiomics.
Article in Cardiology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Enhancing non-invasive differentiation in NSTE-ACS: role of PCAT radiomics.Cardiology journal · 2026Article
- Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo ascertain the diagnostic value of radiomic features of pericoronary adipose tissue (PCAT) and other coronary computed tomography angiography (CCTA) parameters for differentiating non-ST-segment-elevation myocardial infarction (NSTEMI) from unstable angina (UA).
methodsThis study included NSTEMI and UA patients (n = 102 each). The radiomic features of PCAT were selected according to the intraclass correlation coefficient, Pearson's coefficient, the t test, and least absolute shrinkage and selection operator. Six classifiers-random forest, support vector machine, naive Bayes, K-nearest neighbors, extreme gradient boosting, and light gradient boosting machine (LightGBM)-were used to build radiomics models, and the best were selected. Four CCTA parameter models, encapsulating plaque parameters (model 1), plaque parameters + fatty attenuation index (FAI) (model 2), plaque parameters + CT fractional flow reserve (CT-FFR) (model 3), and plaque parameters + CT-FFR + FAI (model 4), were constructed. Finally, we established a fusion model (nomogram) with all CCTA parameters and radiomics model scores. All models were compared regarding their performance.
resultsThe LightGBM radiomics model achieved the highest AUC. Among CCTA parameter models, only model 4 achieved a predictive performance similar to that of the radiomics model in the training and test cohorts (AUC = 0.904 vs. 0.898 and 0.860 vs. 0.877). The combined model (nomogram) showed greater predictive efficacy (AUC = 0.963, 0.910) than model 4 or the radiomics model.
conclusionThe PCAT-based radiomics model accurately distinguishes between NSTEMI and UA, with similar diagnostic performance as the model that combined all the significant CCTA parameters. The nomogram integrating CCTA parameters and the radiomic score has good clinical application prospects.
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