VALUE: Identifying Vulnerable CoronAry PLaqUes With Artificial IntElligence-assisted CT Angiography
Study Details
Study Description
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:
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whether the AI model enables to detect, quantify coronary plaques compared with intravascular ultrasound or expert readers;
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whether the AI model enables to identify vulnerable plaques using intravascular ultrasound or optical coherence tomography as the referece standard.
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whether the AI model enables to predict future adverse cardiac events in a large cohort of 10,000 patients with non-obstructive CAD.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
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, previous 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 developed 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: (i) Validation of the accuracy of the new deep learning model; (ii) Prognosis of the model in different populations with CAD.
The main questions it aims to answer are:
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whether the AI model enables to detect, quantify coronary plaques compared with intravascular ultrasound or expert readers;
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whether the AI model enables to identify vulnerable plaques using IVUS or OCT as the reference standard.
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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).
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Patients who underwent coronary CT angiography and intravascular ultrasound within 3 months
|
Diagnostic Test: Intravascular imaging test
Coronary artery disease patients first underwent CCTA and then intravascular imaging test within 3 months
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| Patients who underwent coronary CT angiography and optical coherence tomography within 3 months
|
Diagnostic Test: Intravascular imaging test
Coronary artery disease patients first underwent CCTA and then intravascular imaging test within 3 months
|
Outcome Measures
Primary Outcome Measures
- Sensitivity and specificity of AI-assisted coronary CT angiography on identifying vulnerable plaques compared to intravascular imaging [1 day]
Secondary Outcome Measures
- Overall coronary plaque detection rate using intravascular ultrasound as reference standard [1 day]
- Total plaque volume [1 day]
- minimum lumen area measurement compared to intravascular ultrasound [1 day]
Eligibility Criteria
Criteria
Inclusion Criteria:
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Intravascular imaging (including intravascular ultrasound or optical coherence tomography) was performed within 3 months after CCTA;
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No change in medications or clinical symptoms during CCTA and intravascular imaging examinations;
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Coronary artery diameter stenosis of 30% to 90% on invasive coronary imaging.
Exclusion Criteria:
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Image quality of CCTA or intravascular US was inadequate to analyze;
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Intravascular imaging was performed after percutaneous coronary intervention (PCI) or pre-dilation of the target lesions;
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Lesions could not be co-registered between CCTA and intravascular US;
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Missing CCTA or intravascular US data
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Research Institute Of Medical Imaging Jinling Hospital | Nanjing | Jiangsu | China | 210018 |
Sponsors and Collaborators
- Jinling Hospital, China
Investigators
- Study Chair: Longjiang Zhang, MD, Jinling Hospital, Medical School of Nanjing University, Nanjing,China
Study Documents (Full-Text)
None provided.More Information
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. 2023 Jul 18. doi: 10.1038/s41569-023-00900-3. Online ahead of print.
- 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.
- 2023DZKY-058-01