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

Evangelos K. Oikonomou(University of Oxford), Charalambos Antoniades(University of Oxford), Alexios S. Antonopoulos(National and Kapodistrian University of Athens), Keith M. Channon(University of Oxford), Nikant Sabharwal(Oxford University Hospitals NHS Trust), Sujatha Kesavan(John Radcliffe Hospital), Christos P. Kotanidis(University of Oxford), Mohamed Marwan(Friedrich-Alexander-Universität Erlangen-Nürnberg), Sheena Thomas(University of Oxford), Milind Y. Desai(Cleveland Clinic), Scott D. Flamm(Cleveland Clinic Lerner College of Medicine), Michelle C. Williams(University of Edinburgh), Stephan Achenbach(Friedrich-Alexander-Universität Erlangen-Nürnberg), L Herdman(University of Oxford), Katharine Thomas(University of Oxford), Alaa Alashi(Cleveland Clinic), Cheerag Shirodaria(Oxford University Hospitals NHS Trust), Marc R. Dweck(University of Edinburgh), Erika Hutt(Cleveland Clinic), Maria Lyasheva(John Radcliffe Hospital), Edwin J.R. van Beek(University of Edinburgh), David E. Newby(University of Edinburgh), Stefan Neubauer(John Radcliffe Hospital), Andrew Kelion(John Radcliffe Hospital), Brian P. Griffin(Cleveland Clinic), Lampson Fan(John Radcliffe Hospital), Jemma C. Hopewell(Unknown), John Deanfield(Universidad de Londres), Ioannis Akoumianakis(John Radcliffe Hospital)
European Heart Journal
August 7, 2019
Cited by 503


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