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Идёт набор NCT07181512

Deep Learning CAD Screening on Chest CT

— Наблюдательное Coronary Artery Disease Coronary Artery Stenosis Stent

Ориентир для пациента и семьи

Простыми словами

Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.

Что изучают
В протоколе указаны: Deep Learning Analysis of Non-contrast Chest CT.
Кому может быть актуально
Состояния в реестре: Coronary Artery Disease, Coronary Artery Stenosis Stent. Базовые параметры: от 18 лет · Все.
Что важно проверить
Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
Где проводится
Китай
Следующий шаг
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Официальное название

Deep Learning-Based Opportunistic Screening of Coronary Artery Disease on Non-Contrast Chest CT: A Multicenter Study

Обзор

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.

Подробное описание

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.

Вмешательства

  • Другое Deep 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.

Первичные конечные точки

  • Accuracy of plaque composition prediction [Срок оценки: Baseline non-contrast chest CT to reference CCTA (within 30 days)]
  • Accuracy of ≥50% stenosis prediction [Срок оценки: Baseline non-contrast chest CT to reference CCTA (within 30 days)]
Вторичные конечные точки (1)
  • Incidence of ≥50% stenosis in non-calcified segments [Срок оценки: Baseline non-contrast chest CT to CCTA (within 30 days)]

Критерии участия

Критерии включения

  • Age ≥18 years
  • Patients who underwent both non-contrast chest CT and coronary CT angiography (CCTA) within 30 days
  • Coronary segments clearly visualized on non-contrast chest CT

Критерии исключения

  • Segments with motion artifacts, metal artifacts, or stents preventing analysis
  • Vessel lumen completely obscured by calcification (unrecognizable vascular course)
  • Inability to match coronary segment location between non-contrast chest CT and CCTA

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Модель наблюдения
Когортное

Центры проведения

Китай · 2 центра
  • The First Affiliated Hospital of Zhejiang Chinese Medical University — Ханчжоу
  • The First Affiliated Hospital of Ningbo University — Ningbo

Идентификаторы

NCT: NCT07181512 · CAD-AI-2025-V1.0

Первоисточники (государственные реестры)

Открыть это исследование на ClinicalTrials.gov ↗