NCT07181512 Deep Learning CAD Screening on Chest CT
| NCT ID | NCT07181512 |
| Status | Recruiting |
| Phase | — |
| Sponsor | Yifan Guo |
| Condition | Coronary Artery Disease |
| Study Type | OBSERVATIONAL |
| Enrollment | 200 participants |
| Start Date | 2025-09-01 |
| Primary Completion | 2026-04-20 |
Eligibility & Interventions
Eligibility Fast-Check
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What to Expect as a Participant
This is an observational study. You will not receive an experimental treatment; researchers will collect data based on your existing condition or standard treatment.
This trial targets 200 participants in total. It began in 2025-09-01 with a primary completion date of 2026-04-20.
⚠ This information is for research awareness only. Always consult your physician before joining any clinical trial. Participation is voluntary and you may withdraw at any time.
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.
Eligibility Criteria
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
Contact & Investigator
Frequently Asked Questions
Who can join the NCT07181512 clinical trial?
This trial is open to participants of all sexes, aged 18 Years or older, studying Coronary Artery Disease. Full inclusion and exclusion criteria are listed in the Eligibility Criteria section. Always confirm your eligibility with the research team before applying.
Is NCT07181512 currently recruiting?
Yes, NCT07181512 is actively recruiting participants. Contact the research team at 20193071@zcmu.edu.cn for enrollment information.
Where is the NCT07181512 trial being conducted?
This trial is being conducted at Hangzhou, China, Ningbo, China.
Who is sponsoring the NCT07181512 clinical trial?
NCT07181512 is sponsored by Yifan Guo. The trial plans to enroll 200 participants.