Identifying Vulnerable CoronAry PLaqUes With Artificial IntElligence-assisted CT Angiography

Coronary Artery Disease Clinical Trial

— VALUE  

Official title:

Development and Validation of Multi-scale Deep Neural Network-Based CT Intelligent Diagnosis System for Coronary Vulnerable Plaques: A Chinese Multicenter Study

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT06025305
• Other study ID #: 2023DZKY-058-01
• Status: Enrolling by invitation
• Start date: July 1, 2023
• Est. completion date: December 31, 2025

Study information

• Verified date: October 2023
• Source: Jinling Hospital, China
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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).

Recruitment information / eligibility

• Status: Enrolling by invitation
• Enrollment: 2000
• Est. completion date: December 31, 2025
• Est. primary completion date: December 31, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Coronary Artery Disease
• Plaque, Atherosclerotic

Intervention

Diagnostic Test:
• Intravascular imaging test
Coronary artery disease patients first underwent CCTA and then intravascular imaging test within 3 months

Locations

Country	Name	City	State
China	Research Institute Of Medical Imaging Jinling Hospital	Nanjing	Jiangsu

Sponsors (1)

Lead Sponsor	Collaborator
Jinling Hospital, China

Country where clinical trial is conducted

China, 

References & Publications (3)

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. 2023 Jul 18. doi: 10.1038/s41569-023-00900-3. Online ahead of print. — View Citation

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. — View Citation

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. Pr — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Sensitivity and specificity of AI-assisted coronary CT angiography on identifying vulnerable plaques compared to intravascular imaging		1 day
Secondary	Overall coronary plaque detection rate using intravascular ultrasound as reference standard		1 day
Secondary	Total plaque volume		1 day
Secondary	minimum lumen area measurement compared to intravascular ultrasound		1 day