Identifying Vulnerable CoronAry PLaqUes With Artificial IntElligence-assisted CT Angiography

NCT ID: NCT06025305

Last Updated: 2023-10-18

Basic Information

• Recruitment Status: ENROLLING_BY_INVITATION
• Total Enrollment: 2000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-07-01
• Study Completion Date: 2025-12-31

Brief Summary

The goal of this observational study is to develop an automatic whole-process AI model to detect, quantify, and characterize plaques using coronary CT angiography in coronary artery disease patients. The main questions it aims to answer are:

1. Whether the AI model enables to detect and quantify coronary plaques compared with intravascular ultrasound or expert readers;

2. Whether the AI model enables to identify vulnerable plaques using intravascular ultrasound or optical coherence tomography as the reference standard.

3. Whether the AI model enables to predict future adverse cardiac events in a large cohort of 10,000 patients with non-obstructive CAD.

Detailed Description

Coronary artery disease (CAD) remains the leading cause of death worldwide. Atherosclerotic plaques play a pivotal role in CAD-related patient mortality. Thus, the detection, quantification, and characterization of coronary plaques are clinically significant for early prevention and interventions for CAD.

Coronary CT angiography (CCTA) has emerged as a robust noninvasive tool for the evaluation of CAD. In clinical practice, the coronary plaque assessment is performed by a time-consuming manual process dependent on the clinician's experience and subjective visual interpretation. With the development of artificial intelligence, many automatic computer-aided methods have been proposed to post-process the CCTA images. However, previously proposed algorithms of plaque evaluation were not developed based on intravascular ultrasound (IVUS) or optical coherence tomography (OCT), which were regarded as the gold reference for plaque evaluation. Thus, we aimed to develop a deep learning model in a whole-process automatic and intelligent system on CCTA to detect, quantify, and characterize plaques using IVUS or OCT as reference standard. Then we will work on the validation in different clinical scenarios: (1) Validation of the accuracy of the new deep learning model; (2) Prognosis of the model in different populations with CAD.

The main questions it aims to answer are:

1. Whether the AI model enables to detect and quantify coronary plaques compared with intravascular ultrasound or expert readers;

2. Whether the AI model enables to identify vulnerable plaques using IVUS or OCT as the reference standard.

3. Whether the AI model enables to predict future adverse cardiac events in a large cohort of 10,000 patients with non-obstructive coronary artery disease (China CT-FFR study 2).

Conditions

Coronary Artery Disease; Plaque, Atherosclerotic

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Patients who underwent coronary CT angiography and intravascular ultrasound within 3 months

Intravascular imaging test

Intervention Type: DIAGNOSTIC_TEST

Coronary artery disease patients first underwent CCTA and then intravascular imaging test within 3 months

Patients who underwent coronary CT angiography and optical coherence tomography within 3 months

Intravascular imaging test

Intervention Type: DIAGNOSTIC_TEST

Coronary artery disease patients first underwent CCTA and then intravascular imaging test within 3 months

Interventions

Intravascular imaging test

Coronary artery disease patients first underwent CCTA and then intravascular imaging test within 3 months

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Intravascular imaging (including intravascular ultrasound or optical coherence tomography) was performed within 3 months after CCTA;
• No change in medications or clinical symptoms during CCTA and intravascular imaging examinations;
• Coronary artery diameter stenosis of 30% to 90% on invasive coronary imaging.

Exclusion Criteria

• Image quality of CCTA or intravascular US was inadequate to analyze;
• Intravascular imaging was performed after percutaneous coronary intervention (PCI) or pre-dilation of the target lesions;
• Lesions could not be co-registered between CCTA and intravascular US;
• Missing CCTA or intravascular US data

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Jinling Hospital, China

OTHER

Sponsor Role: lead

Responsible Party

Zhang longjiang,MD

Director, Head of Radiology, Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Longjiang Zhang, MD

Role: STUDY_CHAIR

Jinling Hospital, Medical School of Nanjing University, Nanjing,China

Locations

Research Institute Of Medical Imaging Jinling Hospital

Nanjing, Jiangsu, China

Countries

China

References

Follmer B, Williams MC, Dey D, Arbab-Zadeh A, Maurovich-Horvat P, Volleberg RHJA, Rueckert D, Schnabel JA, Newby DE, Dweck MR, Guagliumi G, Falk V, Vazquez Mezquita AJ, Biavati F, Isgum I, Dewey M. Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries. Nat Rev Cardiol. 2024 Jan;21(1):51-64. doi: 10.1038/s41569-023-00900-3. Epub 2023 Jul 18.

Reference Type: BACKGROUND

PMID: 37464183 (View on PubMed)

Gaba P, Gersh BJ, Muller J, Narula J, Stone GW. Evolving concepts of the vulnerable atherosclerotic plaque and the vulnerable patient: implications for patient care and future research. Nat Rev Cardiol. 2023 Mar;20(3):181-196. doi: 10.1038/s41569-022-00769-8. Epub 2022 Sep 23.

Reference Type: BACKGROUND

PMID: 36151312 (View on PubMed)

Zhou F, Chen Q, Luo X, Cao W, Li Z, Zhang B, Schoepf UJ, Gill CE, Guo L, Gao H, Li Q, Shi Y, Tang T, Liu X, Wu H, Wang D, Xu F, Jin D, Huang S, Li H, Pan C, Gu H, Xie L, Wang X, Ye J, Jiang J, Zhao H, Fang X, Xu Y, Xing W, Li X, Yin X, Lu GM, Zhang LJ. Prognostic Value of Coronary CT Angiography-Derived Fractional Flow Reserve in Non-obstructive Coronary Artery Disease: A Prospective Multicenter Observational Study. Front Cardiovasc Med. 2022 Jan 31;8:778010. doi: 10.3389/fcvm.2021.778010. eCollection 2021.

Reference Type: RESULT

PMID: 35174219 (View on PubMed)

Other Identifiers

2023DZKY-058-01

Identifier Type: -

Identifier Source: org_study_id