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Rationale and Objectives: To compare the prediction performance of the epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT) radiomics models based on coronary computed tomography angiography for major adverse cardiovascular events (MACE) within 3 years., Materials and Methods: Our study included 288 patients (144 with MACE and 144 without MACE within 3 years) by matching age, gender, body mass index, and medication intake. Patients were randomly assigned either to the training (n = 201) or validation cohort (n = 87). A total of 184 radiomics features were extracted from EAT and PCAT images. Spearman's rank correlation coefficient and the gradient boosting decision tree algorithm were performed for feature selection. Five models were established based on PCAT or EAT radiomics features and clinical factors, including PCAT, EAT, clinical, PCAT-clinical, and EAT-clinical model (M PCAT , M EAT , M clinical , M PCAT-clinical , and M EAT-clinical ). Receiver operating characteristic curves, calibration curves, and the decision curve analysis were plotted to evaluate the model performance., Results: The M PCAT achieved an area under the curve (AUC) of 0.703 in the validation cohort, which was better than M EAT with AUC of 0.538. The M PCAT-clinical showed better performance (AUC = 0.781) in predicting MACE than the M clinical (AUC = 0.748) or M EAT-clinical (AUC = 0.745)., Conclusion: Our results showed that the PCAT was better than the EAT in both single modality and combined models, and the M PCAT-clinical had the most significant clinical value in predicting the occurrence of MACE within 3 years., (Copyright © 2022. Published by Elsevier Inc.)