作者
Evangelos K Oikonomou, Michelle C Williams, Christos P Kotanidis, Milind Y Desai, Mohamed Marwan, Alexios S Antonopoulos, Katharine E Thomas, Sheena Thomas, Ioannis Akoumianakis, Lampson M Fan, Sujatha Kesavan, Laura Herdman, Alaa Alashi, Erika Hutt Centeno, Maria Lyasheva, Brian P Griffin, Scott D Flamm, Cheerag Shirodaria, Nikant Sabharwal, Andrew Kelion, Marc R Dweck, Edwin JR Van Beek, John Deanfield, Jemma C Hopewell, Stefan Neubauer, Keith M Channon, Stephan Achenbach, David E Newby, Charalambos Antoniades
发表日期
2019/11/14
期刊
European Heart Journal
卷号
40
期号
43
页码范围
3529-3543
出版商
Oxford University Press
简介
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, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes representing inflammation, fibrosis and …
学术搜索中的文章
EK Oikonomou, MC Williams, CP Kotanidis, MY Desai… - European Heart Journal, 2019