Plaque Character and Progression According to the Location of Coronary Atherosclerotic Plaque

Application of Quantitative Assessment of Coronary Atherosclerosis by Coronary Computed Tomographic Angiography

Coronary computed tomography angiography (CCTA) has emerged as a pivotal tool for diagnosing and risk-stratifying patients with suspected coronary artery disease (CAD). Recent advancements in image analysis and artificial intelligence (AI) techniques have enabled the comprehensive quantitative analysis of coronary atherosclerosis. Fully quantitative assessments of coronary stenosis and lumen attenuation have improved the accuracy of assessing stenosis severity and predicting hemodynamically significant lesions. In addition to stenosis evaluation, quantitative plaque analysis plays a crucial role in predicting and monitoring CAD progression. Studies have demonstrated that the quantitative assessment of plaque subtypes based on CT attenuation provides a nuanced understanding of plaque characteristics and their association with cardiovascular events. Quantitative analysis of serial CCTA scans offers a unique perspective on the impact of medical therapies on plaque modification. However, challenges such as time-intensive analyses and variability in software platforms still need to be addressed for broader clinical implementation. The paradigm of CCTA has shifted towards comprehensive quantitative plaque analysis facilitated by technological advancements. As these methods continue to evolve, their integration into routine clinical practice has the potential to enhance risk assessment and guide individualized patient management. This article reviews the evolving landscape of quantitative plaque analysis in CCTA and explores its applications and limitations.

Correlation of pericoronary adipose tissue CT attenuation values of plaques and periplaques with plaque characteristics

AIM

To investigate the relationship between different plaque characteristics and pericoronary adipose tissue (PCAT) computed tomography (CT) attenuation values for plaques and periplaques.

MATERIALS AND METHODS

The data from 188 eligible patients with stable coronary heart disease (280 lesions) who underwent coronary CT angiography between March 2021 and November 2021 were collected retrospectively. All PCAT CT attenuation values of plaques and periplaques (the area within 5 and 10 mm proximal and distal to the plaque) were calculated, and multiple linear regression was used to assess their correlation with different plaque characteristics.

RESULTS

PCAT CT attenuation of plaques and periplaques was higher in non-calcified plaques (-73.38 ± 10.41 HU, -76.77 ± 10.86 HU, 79.33 ± 11.13 HU, -75.67 ± 11.24 HU, -78.63 ± 12.09 HU) and mixed plaques (-76.83 ± 8.11 HU, -79 [-85, -68.5] HU, -78.55 ± 11 HU, -78.76 ± 9.9 HU, -78.79 ± 11.06 HU) than in calcified plaques (-86.96 ± 10 HU, -84 [-92, -76] HU, -84.14 ± 11.08 HU, -84.91 ± 11.41 HU, -84.59 ± 11.69 HU; all p<0.05) and higher in distal segment plaques than in proximal segment plaques (all p<0.05). Plaque PCAT CT attenuation was lower in plaques with minimal stenosis than in plaques with mild or moderate stenosis (p<0.05). The significant determinants of PCAT CT attenuation values of plaques and periplaques were non-calcified plaques, mixed plaques, and plaques located in the distal segment (all p<0.05).

CONCLUSIONS

PCAT CT attenuation values in both plaques and periplaques were related to plaque type and location.

Association between plaque localization in proximal coronary segments and MACE outcomes in patients with mild CAC: Results from the EISNER study

Objective

Coronary artery calcium score (CAC) is a validated tool to predict and reclassify cardiovascular risk. Additional metrics such as regional distribution and extent of CAC over Agatston CAC score may allow further risk stratification. In this study, we evaluate the prognostic significance of proximal CAC involvement in asymptomatic population from the prospective EISNER (Early-Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) registry, focusing on patients with mild CAC (score 1-99).

Methods

This study included a total of 2,047 adult asymptomatic subject who underwent baseline CAC scan and 14-year follow-up for MACE, defined as myocardial infarction, late revascularization, or cardiac death. Proximal involvement was defined as presence of CAC in the LM, proximal LAD, LCX or RCA. CAC was categorized as 0, 1-99, and ≥100.

Results

1,090 (53.2%) subjects had no CAC, 576 (28.1%) had CAC 1-99, and 381 (18.7%) had CAC ≥100. Proximal involvement was seen in 67.2% of subjects with CAC 1-99 and 97.3% of subjects with CAC ≥100. In the CAC 1-99 category, the presence of proximal CAC was associated with increased MACE risk after adjustment for CAC score, CAC extent and conventional risk factors compared to those without proximal CAC (HR: 2.84 95% CI: 1.29-6.25, p=0.009).

Conclusion

In asymptomatic subjects with CAC scores of 1-99, the presence and extent of proximal CAC plaques provides strong independent prognostic information in predicting MACE

Correlation between quantification of myocardial area at risk and ischemic burden at cardiac computed tomography

Purpose

This study aims to investigate the correlation between myocardial area at risk at coronary computed tomography angiography (CCTA) and the ischemic burden derived from myocardial computed tomography perfusion (CTP) by using the 17-segment model.

Methods

Forty-two patients with chest pain complaints who underwent a combined CCTA and CTP protocol were identified. Patients with reversible ischemia at CTP and at least one stenosis of ≥ 50% at CCTA were selected. Myocardial area at risk was calculated using a Voronoi-based segmentation algorithm at CCTA and was defined as the sum of all territories related to a ≥ 50% stenosis as a percentage of the total left ventricular (LV) mass. The latter was calculated using LV contours which were automatically drawn using a machine learning algorithm. Subsequently, the ischemic burden was defined as the number of segments demonstrating relative hypoperfusion as a percentage of the total amount of segments (=17). Finally, correlations were tested between the myocardial area at risk and the ischemic burden using Pearson’s correlation coefficient.

Results

A total of 77 coronary lesions were assessed. Average myocardial area at risk and ischemic burden for all lesions was 59% and 23%, respectively. Correlations for ≥ 50% and ≥ 70% stenosis based myocardial area at risk compared to ischemic burden were moderate (r = 0.564; p < 0.01) and good (r = 0.708; p < 0.01), respectively.

Conclusion

The relation between myocardial area at risk as calculated by using a Voronoi-based algorithm at CCTA and ischemic burden as assessed by CTP is dependent on stenosis severity.