Identifying Vulnerable CoronAry PLaqUes With Artificial IntElligence-assisted CT Angiography

Official title

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

Overview

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. 4. Whether the AI model enables to influnece downstream clincial decision-making.

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; (3) Impact of the model on guiding clincial therapies.

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). 4. Whether the AI model enables to influnece downstream clincial decision-making in real-world clincial practice.

Interventions

• Diagnostic test Intravascular imaging test
  Coronary artery disease patients first underwent CCTA and then intravascular imaging test within 3 months.
• Diagnostic test Coronary plaque assessment
  Plaques on coronary CT angiography (CCTA) were quantified and characterized using the developed AI model.

Primary outcome measures

• Sensitivity and specificity of AI-assisted coronary CT angiography on identifying vulnerable plaques compared to intravascular imaging [Time frame: 1 day]

Secondary outcome measures (3)

• Overall coronary plaque detection rate using intravascular ultrasound as reference standard [Time frame: 1 day]
• Total plaque volume [Time frame: 1 day]
• Changes in medical management following the addition of the AI model compared with routine CCTA results alone. [Time frame: 90 days]

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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Cohort

Study locations

China · 1 center

• Research Institute Of Medical Imaging Jinling Hospital — Nanjing

Publications

• 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 PMID 37464183
• 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. PMID 36151312
• 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 O PMID 35174219
• Chen Q, Zhou F, Xing W, Xu Y, Hu S, Pan T, Cao W, Guo L, Shi Y, Luo S, Xu L, Zhang J, Zhang S, Zheng C, Yang Z, Qiao HY, Guo B, Liu T, Xu P, Xu W, Zhong J, Xie G, Tao X, Lu G, Tang CX, Zhang JJ, Zhang LJ; China VALUE Study Group. A Fully Automated Deep Learning Model for Quantifying Coronary Plaque at Coronary CT Angiography. Radiology. 2026 Apr;319(1):e251967. doi: 10.1148/radiol.251967. PMID 42012347

Identifiers

NCT: NCT06025305 · 2023DZKY-058-01

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗