A novel CT-based radiotranscriptomic signature of perivascular fat improves cardiac risk prediction

We present a new artificial intelligence-powered method to predict cardiac risk by analysing the radiomic profile of coronary PVAT, developed and validated in patient cohorts acquired in three different studies. In Study 1, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes rep-resenting inflammation, fibrosis and vascularity was linked with the radiomic features extracted from tissue CT 44 (FAI) derived from coronary computed tomography angiography. A novel radiotranscriptomic signature of PVAT texture detects additional disease-related changes to PVAT composition, including fibrotic and microvascular remodelling. The fat radiomic profile (FRP), derived by machine learning-powered radiomic analysis of PVAT remodelling, significantly improves risk prediction for adverse cardiac events beyond the current state-of-the-art. Whereas FAI changes dynamically in response to acute coronary inflammation, FRP captures persistent structural remodelling in PVAT and provides additional risk stratification in both primary and secondary prevention. adipose microvascular remodelling. analysing phenotype of perivascular adipose tissue on coronary angiography imaging. A comprehensive analysis of volumetric, attenuation-based and texture-based metrics of coronary perivascular adipose tissue on coronary CT angiography imaging carries incremental prognostic value in cardiac risk prediction and highlights the critical role of perivascular adipose tissue in human atherosclerotic cardiovascular disease. CCTA, coronary computed tomography angiography; Index;