Emerging quantitative CCTA imaging biomarkers for cardiovascular risk stratification: a narrative review

Cardiac computed tomography angiography (CCTA) has evolved beyond anatomical stenosis assessment into a comprehensive platform for cardiovascular and cardiometabolic risk stratification. Advances in postprocessing and artificial intelligence now enable automated quantification of multiple imaging biomarkers from a single acquisition, including coronary plaque characteristics, CT-derived fractional flow reserve (FFR-CT), epicardial adipose tissue (EAT), pericoronary adipose tissue (PCAT), and hepatic steatosis.

In this narrative review, we synthesize current imaging biomarkers, evaluate their individual and combined prognostic value, and propose a conceptual multimarker framework for cardiovascular risk stratification-recognizing that several domains remain investigational and are not yet ready for routine, biomarker-guided management.

Quantitative plaque analysis identifies high-risk features - including low-attenuation plaque, positive remodeling, and napkin-ring sign - that independently predict major adverse cardiovascular events (MACE) beyond stenosis severity. FFR-CT carries a Class 2a guideline recommendation for intermediate lesions and demonstrates superior vessel-level diagnostic accuracy compared with SPECT and comparable performance to PET in head-to-head trials. EAT volume and density independently predict incident coronary heart disease, atrial fibrillation, and all-cause mortality across large prospective cohorts. Pericoronary fat attenuation index (FAI) reflects local coronary inflammation, independently predicts MACE after adjustment for conventional risk factors and coronary calcium, and decreases in response to high-dose statin therapy. Hepatic steatosis, identifiable from the same noncontrast acquisition used for calcium scoring, is associated with a 64% increased odds of cardiovascular events in a meta-analysis exceeding 34,000 adults and predicts both plaque progression and high-risk plaque features in longitudinal registries. Emerging multimarker models combining these domains demonstrate incremental discriminatory value beyond individual imaging biomarkers or traditional clinical risk scores.

CCTA provides a pragmatic, multidimensional framework that integrates anatomic, functional, inflammatory, and metabolic information from a single noninvasive examination. While standardization, longitudinal validation, and equitable representation in research cohorts remain unresolved challenges, ongoing advances in AI-driven image analysis and multiomics integration may, if validated in prospective outcome studies, support the future translation of quantitative CCTA imaging biomarkers into more personalized cardiovascular care.