A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography

BACKGROUND: Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction. METHODS AND RESULTS: 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,…

• WT
  Wellcome Trust

  Awards: FS/14/78/31020, CH/09/002, WT103782AIA
• EA
  Edinburgh and Lothians Health Foundation
• SG
  Scottish Government

  Award: CZH/4/588
• NI
  National Institute for Health and Care Research
• BH
  British Heart Foundation

  Awards: CH/09/002, FS/14/55/30806, FS/16/15/32047, TG/16/3/32687, FS/14/78, CH/16/1/32013, FS/14/78/31020, RE/13/3/30183, WT103782AIA
• UO
  University of Edinburgh

  Award: RE/13/3/30183
• ES
  European Society of Cardiology
• EA
  Engineering and Physical Sciences Research Council

  Award: 2119518