ReviewMedical sciences (Basel, Switzerland)2026
Vulnerable Coronary Plaques on CT Angiography: Imaging Biomarkers, Artificial Intelligence, and Emerging Technologies for Risk Stratification.
Review in Medical sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
15 authors.
Funding
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Abstract
backgroundCoronary artery disease remains the leading cause of morbidity and mortality worldwide. Increasing evidence has demonstrated that many acute coronary events originate not from severely obstructive lesions but from structurally unstable atherosclerotic plaques. Consequently, the identification of vulnerable plaques has become a major objective in contemporary cardiovascular imaging. Coronary computed tomography angiography (CCTA) has evolved from a modality primarily focused on luminal stenosis assessment into a comprehensive non-invasive technique capable of evaluating plaque morphology, composition, and overall coronary atherosclerotic burden.
resultsOur review summarizes current evidence regarding the role of CCTA in the detection and characterization of vulnerable coronary plaques. Particular emphasis is placed on imaging biomarkers associated with plaque instability, including low-attenuation plaque, positive remodeling, spotty calcifications, and the napkin-ring sign, which have been consistently linked to an increased risk of adverse cardiovascular events. Advances in quantitative plaque analysis have further enhanced the ability of CCTA to assess plaque burden and monitor disease progression. Emerging technologies are rapidly expanding the capabilities of coronary CT imaging. Spectral CT techniques and photon-counting detector CT offer improved tissue characterization and enhanced visualization of plaque components. In parallel, artificial intelligence and machine learning algorithms are enabling automated plaque detection, segmentation, and risk prediction, facilitating more precise and reproducible analysis of large imaging datasets.
conclusionsDespite these advances, limitations remain, including restricted spatial resolution for microstructural plaque assessment and the limited ability of conventional CT to directly evaluate biological processes such as inflammation. Future developments integrating advanced CT technologies, artificial intelligence, and multimodality imaging are expected to further improve the non-invasive identification of vulnerable plaques and personalized cardiovascular risk stratification.
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