Radiomics-based CCTA models demonstrated robust performance for predicting major adverse cardiac events, yielding a pooled AUC of 0.800 (95% CI 0.732-0.868).
Meta-Analysis
Do radiomics-based models derived from CCTA accurately predict major adverse cardiac events in patients with suspected coronary lesions?
Radiomics-based CCTA models demonstrate robust performance for predicting major adverse cardiac events, highlighting their potential to enhance risk stratification.
Effect estimate: Pooled AUC 0.800 (95% CI 0.732-0.868)
INTRODUCTION Cardiovascular diseases remain a leading cause of morbidity and mortality worldwide, necessitating advanced diagnostic tools for risk stratification. Coronary computed tomography angiography (CCTA) combined with radiomics-a computational method extracting quantitative features from medical images-has emerged as a promising approach to predict major adverse cardiac events (MACEs) in patients who underwent CCTA for suspected coronary lesions. OBJECTIVES This systematic review and meta-analysis aimed to evaluate the diagnostic performance of radiomics-based models derived from CCTA for predicting MACEs. METHODOLOGY We conducted a comprehensive literature search across PubMed, Embase, and Cochrane Central. The main outcome was pooled MACEs predictability estimates, measured by pooled area under the curve (AUC), hazard ratios (HRs) and C-statistics. Subgroup analyses explored performance by radiomic features (e.g., pericoronary adipose tissue PCAT) and patient populations. Methodological quality was assessed by using the METRICS tool. RESULTS Eleven studies meeting inclusion criteria were analyzed. The pooled AUC for radiomics models was 0.800 (95% CI: 0.732-0.868; I² = 75.1%). PCAT-based models showed lower consistency (AUC: 0.777, I² = 80.7%) compared to non-PCAT models (AUC: 0.859, I² = 0%). Subgroup analyses revealed AUCs of 0.754 for coronary artery disease (CAD), 0.901 for suspected/confirmed CAD. Univariate HR was 2.54 (95% CI: 2.00-3.24), while multivariate HR was 1.34 (95% CI: 1.04-1.72). Overall, the average METRICS total score was 70.32% ± 14.20%. CONCLUSIONS Radiomics-based CCTA models demonstrate robust performance for MACE prediction, with variability tied to feature selection and patient populations. These findings highlight radiomics' potential to enhance risk stratification and guide personalized interventions.
Ramos et al. (Wed,) conducted a meta-analysis in Suspected coronary lesions. Radiomics-based CCTA models was evaluated on Major adverse cardiac events (MACEs) (Pooled AUC 0.800, 95% CI 0.732-0.868). Radiomics-based CCTA models demonstrated robust performance for predicting major adverse cardiac events, yielding a pooled AUC of 0.800 (95% CI 0.732-0.868).