Key result
Nomogram integrating CCTA and PCAT radiomics differentiates NSTEMI from unstable angina with an AUC of 0.91.
Why the study?
The study was conducted to ascertain the diagnostic value of pericoronary adipose tissue radiomic features and other CCTA parameters for differentiating NSTEMI from unstable angina.
Does a PCAT-based radiomics model and fusion nomogram improve the diagnostic differentiation of NSTEMI from unstable angina compared to conventional CCTA parameters?
Case-Control (n=204)
No
Does a PCAT-based radiomics model and fusion nomogram improve the diagnostic differentiation of NSTEMI from unstable angina compared to conventional CCTA parameters?
Absolute Event Rate: 0.91% vs 0.86%
p-value: p=0.084
A fusion nomogram combining CCTA parameters and pericoronary adipose tissue radiomics provides high diagnostic accuracy for differentiating NSTEMI from unstable angina.
May aid NSTEMI vs unstable angina differentiation on CCTA; leaves open prospective validation before clinical use.
BACKGROUND: To 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). METHODS: This 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. RESULTS: The 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. CONCLUSION: The 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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Yang et al. (2025) conducted a case-control in Non-ST-segment elevation myocardial infarction (NSTEMI) vs unstable angina (UA) (n=204). Nomogram integrating CCTA parameters and PCAT radiomic score vs. CCTA parameter model (Model 4) was evaluated on Area under the ROC curve (AUC) for differentiating NSTEMI from UA in the test cohort (95% CI 0.804-1.000, p=0.084). A nomogram integrating coronary computed tomography angiography parameters and pericoronary adipose tissue radiomic scores accurately differentiated NSTEMI from unstable angina with an AUC of 0.910.
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