Coronary plaque imaging by coronary computed tomography angiography

Ceramides and metabolic profiles of patients with acute coronary disease: a cross-sectional study

Metabolic Syndrome (MS) is a rapidly growing medical problem worldwide and is characterized by a cluster of age-related metabolic risk factors. The presence of MS increases the likelihood of developing atherosclerosis and significantly raises the morbidity/mortality rate of acute coronary syndrome (ACS) patients. Early detection of MS is crucial, and biomarkers, particularly blood-based, play a vital role in this process. This cross-sectional study focused on the investigation of certain plasma ceramides (Cer14:0, Cer16:0, Cer18:0, Cer20:0, Cer22:0, and Cer24:1) as potential blood biomarkers for MS due to their previously documented dysregulated function in MS patients. A total of 695 ACS patients were enrolled, with 286 diagnosed with MS (ACS-MS) and 409 without MS (ACS-nonMS) serving as the control group. Plasma ceramide concentrations were measured by LC-MS/MS assay and analyzed through various statistical methods. The results revealed that Cer18:0, Cer20:0, Cer22:0, and Cer24:1 were significantly correlated with the presence of MS risk factors. Upon further examination, Cer18:0 emerged as a promising biomarker for early MS detection and risk stratification, as its plasma concentration showed a significant sensitivity to minor changes in MS risk status in participants. This cross-sectional observational study was a secondary analysis of a multicenter prospective observational cohort study (Chinese Clinical Trial Registry, https://www.who.int/clinical-trials-registry-platform/network/primary-registries/chinese-clinical-trial-registry-(chictr), ChiCTR-2200056697), conducted from April 2021 to August 2022.

Automated plaque classification using computed tomography angiography and Gabor transformations

Cardiovascular diseases are the primary cause of death globally. These are often associated with atherosclerosis. This inflammation process triggers important variations in the coronary arteries (CA) and can lead to coronary artery disease (CAD). The presence of CA calcification (CAC) has recently been shown to be a strong predictor of CAD. In this clinical setting, computed tomography angiography (CTA) has begun to play a crucial role as a non-intrusive imaging method to characterize and study CA plaques. Herein, we describe an automated algorithm to classify plaque as either normal, calcified, or non-calcified using 2646 CTA images acquired from 73 patients. The automated technique is based on various features that are extracted from the Gabor transform of the acquired CTA images. Specifically, seven features are extracted from the Gabor coefficients : energy, and Kapur, Max, Rényi, Shannon, Vajda, and Yager entropies. The features were then ordered based on the F-value and input to numerous classification methods to achieve the best classification accuracy with the least number of features. Moreover, two well-known feature reduction techniques were employed, and the features acquired were also ranked according to F-value and input to several classifiers. The best classification results were obtained using all computed features without the employment of feature reduction, using a probabilistic neural network. An accuracy, positive predictive value, sensitivity, and specificity of 89.09%, 91.70%, 91.83% and 83.70% was obtained, respectively. Based on these results, it is evident that the technique can be helpful in the automated classification of plaques present in CTA images, and may become an important tool to reduce procedural costs and patient radiation dose. This could also aid clinicians in plaque diagnostics.

Combining cholesterol-lowering strategies with imaging data: a visible benefit?

Coronary artery disease is characterised by the development of atherosclerotic plaques and is associated with significant morbidity and mortality on a global level. However, many patients with atherosclerosis are asymptomatic and the prediction of acute coronary events is challenging. The role of imaging studies in characterising plaque morphology and stability is emerging as a valuable prognostic tool, while providing evidence for the beneficial effects of cholesterol-lowering therapy on plaque burden. This review provides an overview of contemporary studies describing the value of imaging strategies for atherosclerotic plaques. Coronary angiography is commonly used in the clinical setting, but requires a significant radiation dose (similar to computed tomography). Magnetic resonance imaging evaluation of coronary vessels would avoid exposure to ionising radiation, but is not yet feasible due to motion artefacts. The roles of alternative imaging techniques, including grey-scale intravascular ultrasound, optical coherence tomography and near-infrared spectroscopy have emerged in recent years. In particular, grey-scale intravascular ultrasound has been effectively applied to detect changes in plaque burden and features of plaques predictive of rupture, as well as plaque characteristics during cholesterol-lowering therapy, providing novel insights into factors that may contribute to treatment effectiveness. Challenges and limitations to the use of imaging techniques are considered in this context, along with future imaging strategies.

Computed Tomography Biomarkers of Vulnerable Coronary Plaques

An unstable plaque has a high risk of thrombosis and at the same time for a fast progression of the stenosis degree. Also, “high-risk plaque” and “thrombosis-prone plaque” are used as synonym terms for characterization of a vulnerable plaque. The imaging biomarkers for vulnerable coronary plaques are considered to be spotty calcifications, active remodeling, low-density atheroma and the presence of a ring-like attenuation pattern, also known as the napkin-ring sign. Computed cardiac tomography can determine the plaque composition by assessing the plaque density, which is measured in Hounsfield units (HU). The aim of this manuscript was to provide an update about the most frequently used biomarkers of vulnerability in a vulnerable plaque with the help of computed cardiac tomography.