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| Name | Class |
|---|---|
| Jinhua Municipal Central Hospital | OTHER |
| The Second Affiliated Hospital of Fujian Medical University | OTHER |
| First Affiliated Hospital of Ningbo University | NETWORK |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients undergoing non-contrast chest CT and CCTA | A cohort of patients who underwent both non-contrast chest CT and coronary CT angiography (CCTA) within 30 days. Clearly visualized coronary segments will be analyzed at the segment level for plaque composition and ≥50% stenosis using deep learning models. Both retrospective (2015-2024) and prospective (2025) cases are included. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Deep Learning Analysis of Non-contrast Chest CT | Other | Analysis of clearly visualized coronary segments on non-contrast chest CT using deep learning models, compared with CCTA reference standard. |
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of plaque composition prediction | 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. | Baseline non-contrast chest CT to reference CCTA (within 30 days) |
| Accuracy of ≥50% stenosis prediction | 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. | Baseline non-contrast chest CT to reference CCTA (within 30 days) |
| Measure | Description | Time Frame |
|---|---|---|
| Incidence of ≥50% stenosis in non-calcified segments | Descriptive statistics of ≥50% stenosis prevalence in non-calcified coronary artery segments. | Baseline non-contrast chest CT to CCTA (within 30 days) |
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Inclusion Criteria:
Exclusion Criteria:
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Patients undergoing non-contrast chest CT and CCTA, either during outpatient or inpatient clinical care, or as part of high-risk health examinations. Both retrospective cases (2015-2024) and prospectively recruited patients (2025) are included from five medical centers in China.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yifan Guo, MD | Contact | +86-18072947783 | 20193071@zcmu.edu.cn |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The First Affiliated Hospital of Zhejiang Chinese Medical University | Recruiting | Hangzhou | Zhejiang | 310006 | China |
de-identified data available on reasonable request
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| ID | Term |
|---|---|
| D003324 | Coronary Artery Disease |
| ID | Term |
|---|---|
| D003327 | Coronary Disease |
| D017202 | Myocardial Ischemia |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
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| The First Affiliated Hospital of Ningbo University | Recruiting | Ningbo | Zhejiang | 315000 | China |
|
| D001161 |
| Arteriosclerosis |
| D001157 | Arterial Occlusive Diseases |
| D014652 | Vascular Diseases |