# Integrated prediction of cardiovascular events by automated coronary plaque and pericoronary adipose tissue quantification from CT Angiography

> **NIH NIH R01** · CEDARS-SINAI MEDICAL CENTER · 2023 · $52,961

## Abstract

PROJECT SUMMARY
From parent grant:
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.
 In this project we propose to automate, using artificial intelligence (AI), the measurement of coronary plaque
characteristics and inflammation for patients undergoing coronary CT Angiography (CTA), a first-line noninvasive
diagnostic test for chest pain. We further propose to accurately predict future major adverse cardiac events (such
as heart attack), by integrating clinical data, CTA-measured coronary plaque and inflammation, using AI, in
prospective trials and multicenter CTA registries. 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.
Additionally:
In this research supplement, we propose 1) to compare quantitative plaque characteristics among different ethnic
groups in asymptomatic patients from a prospective multi-ethnic registry, and 2) to establish reference ranges
for coronary plaque measurements from all asymptomatic patients in Aim 1, with prognostic validation in
prospective studies.

## Key facts

- **NIH application ID:** 10694604
- **Project number:** 3R01HL148787-03S1
- **Recipient organization:** CEDARS-SINAI MEDICAL CENTER
- **Principal Investigator:** Damini Dey
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $52,961
- **Award type:** 3
- **Project period:** 2020-05-15 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10694604

## Citation

> US National Institutes of Health, RePORTER application 10694604, Integrated prediction of cardiovascular events by automated coronary plaque and pericoronary adipose tissue quantification from CT Angiography (3R01HL148787-03S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10694604. Licensed CC0.

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