A combined pericoronary adipose tissue radiomics model distinguished NSTEMI from unstable angina, achieving an AUC of 0.898 (95% CI 0.802-0.993) in the internal validation cohort.
Case-Control (n=216)
Can CCTA-based radiomics features of epicardial and pericoronary adipose tissue distinguish NSTEMI from unstable angina?
A combined CCTA-based pericoronary adipose tissue radiomics model demonstrates high discriminatory ability to distinguish NSTEMI from unstable angina.
Effect estimate: AUC 0.898 (95% CI 0.802-0.993)
Objectives: This study aimed to ascertain if the radiomics features of epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT) based on coronary computed tomography angiography (CCTA) could identify non-ST-segment elevation myocardial infarction (NSTEMI) from unstable angina (UA). Materials and methods: This retrospective case-control study included 108 patients with NSTEMI and 108 controls with UA. All patients were separated into training cohort (n = 116), internal validation cohort 1 (n = 50), and internal validation cohort 2 (n = 50) based on the time order of admission. The internal validation cohort 1 used the same scanner and scan parameters as the training cohort, while the internal validation cohort 2 used different canners and scan parameters than the training cohort. The EAT and PCAT radiomics features selected by maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were adopted to build logistic regression models. Finally, we developed an EAT radiomics model, three vessel-based (right coronary artery RCA, left anterior descending artery LAD, and left circumflex artery LCX) PCAT radiomics models, and a combined model by combining the three PCAT radiomics models. Discrimination, calibration, and clinical application were employed to assess the performance of all models. Results: Eight radiomics features of EAT, sixteen of RCA-PCAT, fifteen of LAD-PCAT, and eighteen of LCX-PCAT were selected and used to construct radiomics models. The area under the curves (AUCs) of the EAT, RCA-PCAT, LAD-PCAT, LCX-PCAT and the combined models were 0.708 (95% CI: 0.614-0.802), 0.833 (95% CI:0.759-0.906), 0.720 (95% CI:0.628-0.813), 0.713 (95% CI:0.619-0.807), 0.889 (95% CI:0.832-0.946) in the training cohort, 0.693 (95% CI:0.546-0.840), 0.837 (95% CI: 0.729-0.945), 0.766 (95% CI: 0.625-0.907), 0.675 (95% CI: 0.521-0.829), 0.898 (95% CI: 0.802-0.993) in the internal validation cohort 1, and 0.691 (0.535-0.847), 0.822 (0.701-0.944), 0.760 (0.621-0.899), 0.674 (0.517-0.830), 0.866 (0.769-0.963) in the internal validation cohort 2, respectively. Conclusion: Compared with the RCA-PCAT radiomics model, the EAT radiomics model had a limited ability to discriminate between NSTEMI and UA. The combination of the three vessel-based PCAT radiomics may have the potential to distinguish between NSTEMI and UA.
Wang et al. (Sun,) conducted a case-control in Non-ST-segment elevation myocardial infarction (NSTEMI) and unstable angina (UA) (n=216). Epicardial and pericoronary adipose tissue (EAT and PCAT) radiomics models was evaluated on Discrimination between NSTEMI and UA (AUC 0.898, 95% CI 0.802-0.993). A combined pericoronary adipose tissue radiomics model distinguished NSTEMI from unstable angina, achieving an AUC of 0.898 (95% CI 0.802-0.993) in the internal validation cohort.