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Updated: Aug 5, 2026

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
Cardiac CT for personalized phenotyping in stable coronary artery disease: toward precision medicine
Joel Lenell1, Kajetan Grodecki1,2, Jacek Kwiecinski3
1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States.
Insights
Coronary CT angiography (CCTA) now guides chronic coronary artery disease (CAD) diagnosis by assessing plaque features, not just ischemia. Advanced imaging and AI enable precise risk assessment for personalized patient management.
Area of Science:
- Cardiovascular Imaging
- Radiology
- Artificial Intelligence in Medicine
Background:
- The diagnostic approach for symptomatic chronic coronary artery disease (CAD) has shifted towards noninvasive anatomical assessment.
- Coronary CT angiography (CCTA) is now the preferred first-line modality for evaluating CAD in most patients presenting with chest pain.
- This paradigm shift emphasizes stenosis and plaque characterization over traditional ischemia testing.
Purpose of the Study:
- To review CCTA-derived plaque features and associated imaging biomarkers for risk stratification in CAD.
- To highlight the potential of these biomarkers in precision phenotyping and individualized patient management.
- To discuss future developments facilitating the clinical adoption of novel CCTA imaging biomarkers.
Main Methods:
- Review of current literature on technological advancements and landmark trials in CAD diagnostics.
- Focus on the evolution from qualitative to quantitative plaque analysis using CCTA.
- Integration of artificial intelligence (AI) for rapid quantitative plaque component acquisition.
Main Results:
- CCTA enables detailed characterization of coronary plaque features, serving as crucial clinical risk markers.
- Novel imaging biomarkers, including peri-coronary adipose tissue attenuation and epicardial adipose tissue volume, are identified.
- AI-powered software facilitates feasible, rapid quantitative plaque analysis in clinical practice.
Conclusions:
- CCTA-derived plaque features and biomarkers offer significant potential for precise CAD phenotyping.
- These advancements support individualized management strategies for patients with coronary artery disease.
- Future developments are expected to drive widespread clinical integration of these advanced imaging biomarkers.
Abstract:
Following technological developments and new landmark trials, the diagnostic work-up of symptomatic chronic coronary artery disease (CAD) has evolved. Clinical guidelines now favor noninvasive anatomical assessments by coronary CT angiography (CCTA) as the first-line modality to evaluate CAD in the majority of patients with chest pain. This shift from ischemia testing to stenosis and plaque characterization has resulted in the development of new imaging biomarkers reflecting a variety of coronary plaque features, many of which have proven to be important clinical risk markers. Consequently, there has been a transition from qualitative to semi-quantitative and fully quantitative plaque acquisitions over the entire coronary tree. With the integration of artificial intelligence, novel software enables rapid quantitative acquisitions of plaque components, making them feasible for use in clinical practice. CCTA has also enabled identification of precursor features associated with plaque development such as peri-coronary artery adipose tissue attenuation and epicardial adipose tissue volume. This review provides an overview of CCTA derived plaque features in CAD and associated imaging biomarkers of risk to highlight their potential applications in precision phenotyping and individualized management decisions. It further outlines anticipated future developments that may enable widespread clinical adoption of these novel imaging biomarkers.
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