Pericoronary fat radiomics on coronary CT angiography for predicting major adverse cardiac events: a systematic review and meta-analysis.
Combining radiomics with clinical and imaging data best predicts major adverse cardiac events
Pericoronary fat radiomics on coronary CT angiography for predicting major adverse cardiac events: a systematic review and meta-analysis.
Pericoronary adipose tissue (pericoronary adipose tissue) reflects local coronary inflammation and microstructural changes and may predict major adverse cardiac events (major adverse cardiac events).
This study evaluated the diagnostic performance of pericoronary adipose tissue radiomics for major adverse cardiac events prediction.
Ten retrospective studies were included.
Radiomics-only models showed moderate performance (sensitivity 0.70, specificity 0.74, area under the curve (AUC) 0.78).
Combined models improved discrimination, with radiomics + clinical (AUC 0.80) and radiomics + imaging (sensitivity 0.89; diagnostic odds ratio (LnDOR) 2.93).
Radiomics models showed higher AUC than clinical (ΔAUC = 0.05) and imaging models (ΔAUC = 0.18), with inconsistent sensitivity and specificity differences.
Mean Radiomics Quality Score was 18/36, and overall evidence certainty was moderate.
Pericoronary adipose tissue radiomics derived from CCTA shows moderate predictive performance for major adverse cardiac events in patients with coronary artery disease and may provide incremental value over conventional clinical and imaging models.
Standardized radiomics pipelines and multicenter prospective validation are required for clinical translation.
KEY POINTS: Question Can quantitative analysis of pericoronary adipose tissue on coronary computed tomography angiography improve the prediction of major adverse cardiac events beyond clinical risk factors?