# Multi-modality evaluation of high-risk coronary atherosclerotic plaque

> **NIH NIH K01** · CASE WESTERN RESERVE UNIVERSITY · 2024 · $129,612

## Abstract

Multi-modality evaluation of high-risk coronary atherosclerotic plaque
Summary
Non-invasive, quantitative assessment of coronary atherosclerosis will lead to improved, personalized patient
management for the leading cause of death in the US — cardiovascular disease. Detecting high-risk lesions at
the earliest stages of coronary artery disease would facilitate timely medical interventions to hinder the pro-
gression of coronary atherosclerosis and prevent catastrophic complications. Intravascular imaging modalities
such as intravascular optical coherence tomography (IVOCT) have been used to identify the presence and
characteristics of coronary atherosclerosis. IVOCT, with its high resolution and contrast, is recognized as the
best method for identifying local high-risk lesions (e.g., thin cap fibroatheroma). However, intravascular imag-
ing is invasive, limiting its applicability, especially for patients early in the disease process. Coronary computed
tomography angiography (CCTA) is the only non-invasive imaging modality allowing the assessment of luminal
stenosis as well as plaque morphology. We will develop new AI methods for CCTA evaluations of high-risk
coronary atherosclerotic plaque by comparing them to concurrently-acquired, high-resolution/contrast IVOCT,
deemed the best method to assess high-risk plaque. To enable a new, non-invasive evaluation of atheroscle-
rosis, we will register CCTA images to concurrently-acquired IVOCT images and determine image features in
CCTA that associate with and predict high-risk plaques as seen in IVOCT. In addition to this concurrent deter-
mination of high-risk plaque in CCTA, we will take what we learn and apply it to the long-term prediction of ma-
jor adverse cardiovascular events (MACE). Specifically, we will: 1) Create highly automated methods for as-
sessing high-risk plaques seen microscopically in IVOCT and develop CCTA feature candidates suggestive of
high-risk plaque; 2) Use novel IVOCT to CCTA registration to associate segmental CCTA features to IVOCT-
defined high-risk plaque features and to create a CCTA classification model for high-risk plaque; and 3) Apply
the most promising segmental CCTA features to predict long-term adverse outcomes in CCTA data. With suc-
cess, our research will lead to decision support software for the prediction of MACE, facilitating revasculariza-
tion strategies, therapeutic decisions, and furthering precision medicine approaches. The project team will build
on expertise in cardiology, machine learning, biostatistics, and advanced image analysis of IVOCT and CCTA.
 In this project, I will build upon my experience in machine/deep learning analysis of intravascular images to
include new training in image analytics of CCTA data, biostatistics, and bioinformatics of metabolomics and
genomics, providing me with a foundation for a future career in cardiovascular disease. For example, I will be
well situated to integrate CCTA image analytics and genomics to further my und...

## Key facts

- **NIH application ID:** 10984628
- **Project number:** 1K01HL171795-01A1
- **Recipient organization:** CASE WESTERN RESERVE UNIVERSITY
- **Principal Investigator:** Juhwan Lee
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $129,612
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10984628

## Citation

> US National Institutes of Health, RePORTER application 10984628, Multi-modality evaluation of high-risk coronary atherosclerotic plaque (1K01HL171795-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10984628. Licensed CC0.

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