Coronary Artery Disease, Coronary Artery Stenosis Stent
Conditions
Keywords
Coronary Artery Disease, Non-contrast Chest CT, Coronary CT Angiography, Opportunistic Screening, Artificial Intelligence
Brief summary
Coronary artery disease (CAD) is one of the leading causes of death worldwide. Many people have early atherosclerosis without symptoms, and some may develop significant coronary stenosis before any warning signs appear. Identifying high-risk individuals at an early stage is important to prevent heart attacks and other cardiovascular events. Coronary CT angiography (CCTA) can directly evaluate plaque type and the degree of narrowing in the coronary arteries, but it is expensive, requires contrast injection, and involves higher radiation, making it unsuitable for large-scale screening. In contrast, non-contrast chest CT is widely used for health check-ups and lung disease follow-up. Such scans often provide clear views of certain coronary segments, which creates an opportunity to screen for CAD without additional cost or risk. This multicenter study aims to develop and validate deep learning models to analyze coronary calcified segments that are visible on non-contrast chest CT. Two main objectives are: (1) to predict whether calcified segments contain mixed plaque components (both calcified and non-calcified); and (2) to predict whether these segments have significant narrowing (≥50% stenosis) as determined by CCTA. The study will also describe how often ≥50% stenosis is found in non-calcified segments, in order to demonstrate their low-risk nature. The study includes retrospective data collected between 2015 and 2024, and a prospective external validation cohort starting in 2025. Approximately 1,417 patients with paired chest CT and CCTA have already been included for model development and testing. An additional 200 or more patients will be prospectively recruited for external validation. This research may provide evidence that deep learning applied to routine non-contrast chest CT can serve as an opportunistic tool for early CAD risk screening in the general population.
Detailed description
This study involves analysis of imaging data obtained from patients who undergo non-contrast chest CT and CCTA as part of their routine clinical care. No additional imaging, radiation, or intervention is performed. The institutional review board approved the study and waived the requirement for written informed consent due to minimal risk and use of de-identified data.
Interventions
OTHERDeep Learning Analysis of Non-contrast Chest CT
Analysis of clearly visualized coronary segments on non-contrast chest CT using deep learning models, compared with CCTA reference standard.
Sponsors
Yifan Guo
Jinhua Municipal Central Hospital
The Second Affiliated Hospital of Fujian Medical University
First Affiliated Hospital of Ningbo University
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. Age ≥18 years 2. Patients who underwent both non-contrast chest CT and coronary CT angiography (CCTA) within 30 days 3. Coronary segments clearly visualized on non-contrast chest CT
Exclusion criteria
1. Segments with motion artifacts, metal artifacts, or stents preventing analysis 2. Vessel lumen completely obscured by calcification (unrecognizable vascular course) 3. Inability to match coronary segment location between non-contrast chest CT and CCTA
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of plaque composition prediction | Baseline non-contrast chest CT to reference CCTA (within 30 days) | Discrimination ability of the deep learning model to classify calcified coronary segments as purely calcified or mixed plaque, using CCTA as the reference standard. Evaluated with AUC, sensitivity, specificity. |
| Accuracy of ≥50% stenosis prediction | Baseline non-contrast chest CT to reference CCTA (within 30 days) | Discrimination ability of the deep learning model to predict ≥50% luminal stenosis in calcified coronary segments, using CCTA as the reference standard. Evaluated with AUC, sensitivity, specificity, PPV, NPV. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Incidence of ≥50% stenosis in non-calcified segments | Baseline non-contrast chest CT to CCTA (within 30 days) | Descriptive statistics of ≥50% stenosis prevalence in non-calcified coronary artery segments. |
Countries
China
Contacts
CONTACTYifan Guo, MD
Outcome results
None listed