PROJECT SUMMARY
Coronary artery disease remains the leading cause of death worldwide, and more than half of the individuals
suffering myocardial infarction (heart attacks) have no premonitory symptoms. Studies of patients with
coronary artery disease have traditionally focused only on the severity of narrowing (stenosis) of the coronary
arteries by atherosclerotic plaques, rather than the adverse features of coronary plaques which are
predisposed to rupture and precipitate myocardial infarction. Coronary CT Angiography (CTA) is a noninvasive
test that allows assessment of both coronary stenosis and plaque characteristics. Currently, however, CTA is
interpreted visually for stenosis. Quantitative measurements of CTA stenosis severity and plaque features are
not part of current clinical routine.
We propose to develop novel image processing algorithms for fully automated, robust quantification of
coronary plaque features from CTA. We also propose to automatically quantify the characteristics of adipose
tissue around the coronary arteries (pericoronary adipose tissue, PCAT), which have been shown to
differentiate rupture-prone, high-risk coronary plaques from stable ones. We propose to apply machine
learning methods to efficiently combine stenosis, plaque and PCAT features, along with patient clinical data,
into a new integrated risk score for the prediction of future adverse cardiovascular events. We will evaluate this
risk score in the real-world, prospective, landmark SCOT-HEART trial (including all 2073 patients in the
CTA arm of the trial), with added external validation in large multicenter patient registries, with available CTA
scans, clinical data, and followup for cardiovascular events (fatal and non-fatal myocardial infarction and
cardiovascular death in a grand total of 7844 patients). We propose three specific aims:
1) To refine, expand and automate measurements of coronary plaque and lumen for the entire coronary artery
tree, and to standardize measurement of plaque changes in serial CTA;
2) To evaluate the prognostic value of automatically-quantified plaque features and PCAT characteristics for
the prediction of future MACE in the prospective SCOT-HEART trial and multicenter CTA registries;
3) To develop and evaluate with full external validation a new automated patient risk score—combining
patient clinical data, CTA-measured quantitative plaque features and PCAT characteristics, using machine
learning—for the prediction of future MACE events in the prospective SCOT-HEART trial and multicenter CTA
registries.
The proposed work will enable automated, multi-faceted and reproducible analysis of plaque, stenosis and
PCAT from CTA, combined with objective risk scores reflecting likelihood of adverse cardiovascular events.
This work will provide a novel, personalized, real-world paradigm that objectively and accurately identifies
individual patients at risk of future cardiovascular events, from routine CTA imaging.
