Advancements in non-invasive imaging of atherosclerosis: Future perspectives

Improved ASCVD Screening in Diabetes: a Focus on Scoring Models and Detection Techniques

Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of morbidity and mortality in individuals with type 2 diabetes mellitus (T2DM). Diabetes accelerates the progression of atherosclerosis through key mechanisms such as insulin resistance, hyperglycemia, dyslipidemia, chronic inflammation, and oxidative stress, significantly increasing the risk of coronary artery disease, stroke, and heart failure. Traditional risk assessment models and treatment strategies often fall short in fully addressing these complexities, leaving a substantial residual cardiovascular risk in diabetic patients. This review focuses on the need for enhanced screening protocols in diabetic populations, examining advanced risk scoring models and detection techniques aimed at improving early identification and management of ASCVD. Also, this study examines the pathophysiological links between diabetes and atherosclerosis, emphasizing the need for enhanced screening protocols. Emerging tools, such as non-invasive imaging techniques (e.g., coronary artery calcium scoring, CCTA) and biomarkers (e.g., polygenic risk scores), offer promise for improved early detection and risk stratification. Additionally, newer therapeutic strategies targeting inflammation and insulin resistance are being explored to mitigate cardiovascular risks in this population. Given the significant cardiovascular risk associated with diabetes, particularly T2DM, these advancements are crucial in reducing morbidity and mortality related to atherosclerotic events.

Long-term clinical significance of the presence of puff-chandelier ruptures detected by non-obstructive aortic angioscopy

Although studies have reported various patterns of atherosclerotic aortic plaques (AAPs) detected by non-obstructive aortic angioscopy (NOA), the long-term outcomes associated with AAPs such as puff-chandelier rupture atheromatous plaque (PCR), remain unclear. This study investigated the long-term prognostic significance of AAPs detected by NOA in patients who underwent percutaneous coronary intervention (PCI). This retrospective multicenter cohort study included 167 patients who underwent PCI and NOA. The association between AAPs and the incidence of major adverse cardiac events (MACE) were assessed. MACE was categorized into: MACE1, including cardiac death, myocardial infarction, stroke, and ischemia-driven unplanned revascularization; and MACE2, including cardiac death, myocardial infarction, and stroke. There were no NOA-related complications. Of all AAPs, only PCR showed a significant prognostic value during the follow-up period (mean follow-up period: 6.3 years [range 5.9-6.6]). In multivariable Cox proportional hazards analysis, PCR was an independent predictor of MACE (MACE1; HR 1.91, 95% CI 1.04-3.49, P = 0.04, MACE2; hazard ratio [HR] 4.52, 95% confidence interval [CI] 1.23-16.57, P = 0.02). Kaplan-Meier analysis revealed that PCR was significantly associated with MACE. NOA during PCI is safe and feasible. Detecting PCR by NOA may provide reliable information for identifying patients at high risk of subsequent long-term adverse events after PCI.

Chronic Coronary Artery Disease: Wall Disease vs. Lumenopathy

Acute and chronic coronary artery disease (CAD) are interconnected, representing two facets of the same condition. Chronic CAD exhibits a dynamic nature, manifesting as stable or acute ischemia, or both. Myocardial ischemia can be transient and reversible. The genesis of CAD involves diverse anatomical and functional mechanisms, including endothelial dysfunction, arteriolar remodeling, capillary rarefaction, and perivascular fibrosis, though no single factor explains its heterogeneity. Chronic CAD is often stable but may present as symptomatic or asymptomatic (e.g., in diabetes) and affect various coronary compartments (epicardial or microcirculation). This complexity necessitates a reappraisal of our approach, as pathophysiological mechanisms vary and often overlap. A comprehensive exploration of these mechanisms using advanced diagnostic techniques can aid in identifying the dynamic processes underlying CAD. The disease may present as obstructive or non-obstructive, stable or unstable, underscoring its diversity. The primary source of CAD lies in the arterial wall, emphasizing the need for research on its components, such as the endothelium and vascular smooth muscle cells, and factors disrupting arterial homeostasis. Shifting focus from arterial luminal status to the arterial wall can provide insights into the genesis of atheromatous plaques, enabling earlier interventions to prevent their development and progression.

Diagnostic Methods of Atherosclerotic Plaque and the Assessment of Its Prognostic Significance-A Narrative Review

Cardiovascular diseases (CVD) are a leading cause of death. The most notable cause of CVD is an atherosclerotic plaque. The aim of this review is to provide an overview of different diagnostic methods for atherosclerotic plaque relevant to the assessment of cardiovascular risk. The methods can be divided into invasive and non-invasive. This review focuses on non-invasive with attention paid to ultrasonography, contrast-enhanced ultrasonography, intravascular ultrasonography, and assessment of intima-media complex, coronary computed tomography angiography, and magnetic resonance. In the review, we discuss a number of Artificial Intelligence technologies that support plaque imaging.

Addressing the Contrast Media Recognition Challenge: A Fully Automated Machine Learning Approach for Predicting Contrast Phases in CT Imaging

OBJECTIVES

Accurately acquiring and assigning different contrast-enhanced phases in computed tomography (CT) is relevant for clinicians and for artificial intelligence orchestration to select the most appropriate series for analysis. However, this information is commonly extracted from the CT metadata, which is often wrong. This study aimed at developing an automatic pipeline for classifying intravenous (IV) contrast phases and additionally for identifying contrast media in the gastrointestinal tract (GIT).

MATERIALS AND METHODS

This retrospective study used 1200 CT scans collected at the investigating institution between January 4, 2016 and September 12, 2022, and 240 CT scans from multiple centers from The Cancer Imaging Archive for external validation. The open-source segmentation algorithm TotalSegmentator was used to identify regions of interest (pulmonary artery, aorta, stomach, portal/splenic vein, liver, portal vein/hepatic veins, inferior vena cava, duodenum, small bowel, colon, left/right kidney, urinary bladder), and machine learning classifiers were trained with 5-fold cross-validation to classify IV contrast phases (noncontrast, pulmonary arterial, arterial, venous, and urographic) and GIT contrast enhancement. The performance of the ensembles was evaluated using the receiver operating characteristic area under the curve (AUC) and 95% confidence intervals (CIs).

RESULTS

For the IV phase classification task, the following AUC scores were obtained for the internal test set: 99.59% [95% CI, 99.58-99.63] for the noncontrast phase, 99.50% [95% CI, 99.49-99.52] for the pulmonary-arterial phase, 99.13% [95% CI, 99.10-99.15] for the arterial phase, 99.8% [95% CI, 99.79-99.81] for the venous phase, and 99.7% [95% CI, 99.68-99.7] for the urographic phase. For the external dataset, a mean AUC of 97.33% [95% CI, 97.27-97.35] and 97.38% [95% CI, 97.34-97.41] was achieved for all contrast phases for the first and second annotators, respectively. Contrast media in the GIT could be identified with an AUC of 99.90% [95% CI, 99.89-99.9] in the internal dataset, whereas in the external dataset, an AUC of 99.73% [95% CI, 99.71-99.73] and 99.31% [95% CI, 99.27-99.33] was achieved with the first and second annotator, respectively.

CONCLUSIONS

The integration of open-source segmentation networks and classifiers effectively classified contrast phases and identified GIT contrast enhancement using anatomical landmarks.