Eur Heart J Digit Health. 2026 Jul 14;7(6):ztag104. doi: 10.1093/ehjdh/ztag104. eCollection 2026 Jul.
ABSTRACT
Cardiovascular diseases remain as a leading cause of mortality and morbidity worldwide, with coronary artery disease (CAD) and its complications, collectively referred to as major adverse cardiovascular events (MACEs), necessitating accurate risk stratification. Coronary computed tomography angiography (CCTA) has emerged as a valuable non-invasive imaging modality, and adipose tissue characteristics derived from CCTA have shown promise as imaging biomarkers for MACE prediction. This systematic review and meta-analysis aimed to evaluate the predictive performance of artificial intelligence (AI)-driven models incorporating CCTA-derived adipose tissue radiomic features for forecasting MACEs. A systematic review and random-effects meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies evaluating predictive models developed using different AI algorithms that utilized adipose tissue radiomics as the primary predictor of MACEs in patients undergoing CCTA were included. Model performance was assessed using pooled area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Eleven studies comprising 47 244 participants were included in the analysis. AI-based models integrating adipose tissue radiomic features with clinical data consistently outperformed conventional risk assessment tools, with pooled AUCs ranging from 82.2% to 87.9%. Among the evaluated approaches, deep learning models demonstrated superior predictive performance compared with traditional machine learning and logistic regression-based models. However, considerable heterogeneity (I² > 96%) was observed across studies, reflecting variations in study design, imaging protocols, and AI methodologies. While AI-enhanced CCTA-based adipose tissue characterization demonstrates considerable potential for improving MACE risk prediction, methodological heterogeneity and limited external validation currently restrict its clinical applicability. Future research should prioritize standardized imaging and analytical methodologies, rigorous clinical and external validation, and transparent reporting to facilitate reliable integration of these AI models into routine cardiovascular risk assessment.
PMID:42453612 | PMC:PMC13367595 | DOI:10.1093/ehjdh/ztag104