Cardiac Computed Tomography

Predictive value of CCTA-based pericoronary adipose tissue imaging for major adverse cardiovascular events

RATIONALE AND OBJECTIVE

To evaluate the ability of the radiomic characteristics of pericoronary adipose tissue (PCAT) as determined by coronary computed tomography angiography (CCTA) to predict the likelihood of major adverse cardiovascular events (MACEs) within the next five years.

MATERIALS AND METHODS

In this retrospective casecontrol study, the case group consisted of 210 patients with coronary artery disease (CAD) who developed MACEs within five years, and the control group consisted of 210 CAD patients without MACEs who were matched with the case group patients according to baseline characteristics. Both groups were divided into training and testing cohorts at an 8:2 ratio. After data standardization and the exclusion of features with Pearson correlation coefficients of |r| ≥ 0.9, independent logistic regression models were constructed using selected radiomics features of the proximal PCAT of the left anterior descending (LAD) artery, left circumflex (LCX) artery, and right coronary artery (RCA) via least absolute shrinkage and selection operator (LASSO) techniques. An integrated PCAT radiomics model including all three coronary arteries was also developed. Five models, including individual PCAT radiomics models for the LAD artery, LCX artery, and RCA; an integrated radiomics model; and a fat attenuation index (FAI) model, were assessed for diagnostic accuracy via receiver operating characteristic (ROC) curves, calibration curves, and decision curves.

RESULTS

Compared with the FAI model (AUC=0.564 in training, 0.518 in testing), the integrated radiomics model demonstrated superior diagnostic performance (area under the curve [AUC]=0.923 in training, 0.871 in testing). The AUC values of the integrated model were greater than those of the individual coronary radiomics models, with all the models showing goodness of fit (P > 0.05). The decision curves indicated greater clinical utility of the radiomics models than the FAI model.

CONCLUSION

PCAT radiomics models derived from CCTA data are highly valuable for predicting future MACE risk and significantly outperform the FAI model.

Diagnosis of perimenopausal coronary heart disease patients using radiomics signature of pericoronary adipose tissue based on coronary computed tomography angiography

To assess whether the radiomics signature of pericoronary adipose tissue (PCAT) from coronary computed tomography angiography (CCTA) can distinguish between perimenopausal women with coronary heart disease (CHD) and those without coronary artery disease (CAD). This single-center retrospective case-control study comprised 140 perimenopausal women with CHD presenting with chest pain who underwent CCTA within 48 h of admission. They were matched with 140 control patients presenting with chest pain but without CAD, based on age, risk factors, radiation dose and CT tube voltage. For all participants, PCAT around the proximal right coronary artery was segmented, from which radiomics features and the fat attenuation index (FAI) were extracted and analyzed. Subsequently, corresponding models were developed and internally validated using Bootstrap methods. Model performance was assessed through measures of identification, calibration, and clinical utility. Using logistic regression analysis, an integrated model that combines clinical features, fat attenuation index and radiomics parameters demonstrated enhanced discrimination ability for perimenopausal CHD (area under the curve [AUC]: 0.80, 95% confidence interval [CI]:0.740-0.845). This model outperformed both the combination of clinical features and PCAT attenuation (AUC 0.67, 95% CI 0.602-0.727) and the use of clinical features alone (AUC 0.66, 95% CI 0.603-0.732). Calibration curves for the three predictive models indicated satisfactory fit (all p > 0.05). Moreover, decision curve analysis demonstrated that the integrated model offered greater clinical benefit compared to the other two models. The CCTA-based radiomics signature derived from the PCAT model outperforms the FAI model in differentiating perimenopausal CHD patients from non-CAD individuals. Integrating PCAT radiomics with the FAI could enhance the diagnostic accuracy for perimenopausal CHD.

Cardiac CT: Competition, complimentary or confounder

Coronary CT angiography (CCTA) has been gradually adopted into clinical practice over the last two decades. CCTA has high diagnostic accuracy, prognostic value, and unique features such as assessment of plaque composition. CCTA-derived functional assessment techniques such as fractional flow reserve and CT perfusion are also available and can increase the diagnostic specificity of the modality. These properties propound CCTA as a competitor of functional testing in diagnosis of obstructive CAD, however, utilizing CCTA in a concomitant fashion to potentiate the performance of the latter can lead to better patient care and may provide more accurate prognostic information. Although multiple diagnostic challenges such as evaluation of calcified segments, stents, and small distal vessels still exist, the technologic developments in hardware as well as growing incorporation of artificial intelligence to daily practice are all set to augment the diagnostic and prognostic role of CCTA in cardiovascular disorders.

Advances in Clinical Imaging of Vascular Inflammation: A State-of-the-Art Review

Vascular inflammation is a major contributor to cardiovascular disease, particularly atherosclerotic disease, and early detection of vascular inflammation may be key to the ultimate reduction of residual cardiovascular morbidity and mortality. This review paper discusses the progress toward the clinical utility of noninvasive imaging techniques for assessing vascular inflammation, with a focus on coronary atherosclerosis. A discussion of multiple modalities is included: computed tomography (CT) imaging (the major focus of the review), cardiac magnetic resonance, ultrasound, and positron emission tomography imaging. The review covers recent progress in new technologies such as the novel CT biomarkers of coronary inflammation (eg, the perivascular fat attenuation index), new inflammation-specific tracers for positron emission tomography-CT imaging, and others. The strengths and limitations of each modality are explored, highlighting the potential for multi-modality imaging and the use of artificial intelligence image interpretation to improve both diagnostic and prognostic potential for common conditions such as coronary artery disease.

Pericarotid Adipose Tissue is Associated with Circulatory Markers of Inflammation and Carotid Atherosclerosis

Perivascular adipose tissue plays roles in vascular inflammation and atherosclerosis. The present study aimed to evaluate the association between pericarotid fat density (PFD) and circulatory inflammatory indicators, internal carotid artery (ICA) stenosis, and vulnerable carotid plaques. We retrospectively screened 498 consecutive patients who underwent both computed tomography angiography of the neck between January 2017 and December 2020. The PFD, ICA stenosis, and vulnerable carotid plaques were analyzed using established approaches. Laboratory data including C-reactive protein (CRP) levels, lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune inflammation index (SII) were recorded. PFD was positively correlated with CRP, NLR, PLR, and SII, and negatively correlated with LMR. A higher PFD was independently associated with extracranial ICA stenosis (1.179 [1.003-1.387], P = .040) and vulnerable carotid plaques (1.046 [1.021-1.072], P = .001) after adjusting for systemic inflammatory indicators. These findings suggested higher PFD is independently associated with circulating inflammatory indicators, extracranial ICA stenosis, and vulnerable carotid plaque.

Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment

Cardiac computed tomography angiography (CCTA) is considered the standard non-invasive tool to rule-out obstructive coronary artery disease (CAD). Moreover, several imaging biomarkers have been developed on cardiac-CT imaging to assess global CAD severity and atherosclerotic burden, including coronary calcium scoring, the segment involvement score, segment stenosis score and the Leaman-score. Myocardial perfusion imaging enables the diagnosis of myocardial ischemia and microvascular damage, and the CT-based fractional flow reserve quantification allows to evaluate non-invasively hemodynamic impact of the coronary stenosis. The texture and density of the epicardial and perivascular adipose tissue, the hypodense plaque burden, the radiomic phenotyping of coronary plaques or the fat radiomic profile are novel CT imaging features emerging as biomarkers of inflammation and plaque instability, which may implement the risk stratification strategies. The ability to perform myocardial tissue characterization by extracellular volume fraction and radiomic features appears promising in predicting arrhythmogenic risk and cardiovascular events. New imaging biomarkers are expanding the potential of cardiac CT for phenotyping the individual profile of CAD involvement and opening new frontiers for the practice of more personalized medicine.

Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study

Objective

To develop and validate a hybrid model incorporating CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics signatures for predicting progression of white matter hyperintensity (WMH).

Methods

A total of 226 patients who received coronary computer tomography angiography (CCTA) and brain magnetic resonance imaging from two hospitals were divided into a training set (n = 116), an internal validation set (n = 30), and an external validation set (n = 80). Patients who experienced progression of WMH were identified from subsequent MRI results. We calculated CT-FFR and pFAI from CCTA images using semi-automated software, and segmented the pericoronary adipose tissue (PCAT) and myocardial ROI. A total of 1,073 features were extracted from each ROI, and were then refined by Elastic Net Regression. Firstly, different machine learning algorithms (Logistic Regression [LR], Support Vector Machine [SVM], Random Forest [RF], k-nearest neighbor [KNN] and eXtreme Gradient Gradient Boosting Machine [XGBoost]) were used to evaluate the effectiveness of radiomics signatures for predicting WMH progression. Then, the optimal machine learning algorithm was used to compare the predictive performance of individual and hybrid models based on independent risk factors of WMH progression. Receiver operating characteristic (ROC) curve analysis, calibration and decision curve analysis were used to evaluate predictive performance and clinical value of the different models.

Results

CT-FFR, pFAI, and radiomics signatures were independent predictors of WMH progression. Based on the machine learning algorithms, the PCAT signatures led to slightly better predictions than the myocardial signatures and showed the highest AUC value in the XGBoost algorithm for predicting WMH progression (AUC: 0.731 [95% CI: 0.603-0.838] vs.0.711 [95% CI: 0.584-0.822]). In addition, pFAI provided better predictions than CT-FFR (AUC: 0.762 [95% CI: 0.651-0.863] vs. 0.682 [95% CI: 0.547-0.799]). A hybrid model that combined CT-FFR, pFAI, and two radiomics signatures provided the best predictions of WMH progression [AUC: 0.893 (95%CI: 0.815-0.956)].

Conclusion

pFAI was more effective than CT-FFR, and PCAT signatures were more effective than myocardial signatures in predicting WMH progression. A hybrid model that combines pFAI, CT-FFR, and two radiomics signatures has potential use for identifying WMH progression.

Predicting progression of white matter hyperintensity using coronary artery calcium score based on coronary CT angiography-feasibility and accuracy

Objective

Coronary artery disease (CAD) usually coexists with subclinical cerebrovascular diseases given the systematic nature of atherosclerosis. In this study, our objective was to predict the progression of white matter hyperintensity (WMH) and find its risk factors in CAD patients using the coronary artery calcium (CAC) score. We also investigated the relationship between the CAC score and the WMH volume in different brain regions.

Methods

We evaluated 137 CAD patients with WMH who underwent coronary computed tomography angiography (CCTA) and two magnetic resonance imaging (MRI) scans from March 2018 to February 2023. Patients were categorized into progressive (n = 66) and nonprogressive groups (n = 71) by the change in WMH volume from the first to the second MRI. We collected demographic, clinical, and imaging data for analysis. Independent risk factors for WMH progression were identified using logistic regression. Three models predicting WMH progression were developed and assessed. Finally, patients were divided into groups based on their total CAC score (0 to <100, 100 to 400, and > 400) to compare their WMH changes in nine brain regions.

Results

Alcohol abuse, maximum pericoronary fat attenuation index (pFAI), CT-fractional flow reserve (CT-FFR), and CAC risk grade independently predicted WMH progression (p < 0.05). The logistic regression model with all four variables performed best (training: AUC = 0.878, 95% CI: 0.790, 0.938; validation: AUC = 0.845, 95% CI: 0.734, 0.953). An increased CAC risk grade came with significantly higher WMH volume in the total brain, corpus callosum, and frontal, parietal and occipital lobes (p < 0.05).

Conclusion

This study demonstrated the application of the CCTA-derived CAC score to predict WMH progression in elderly people (≥60 years) with CAD.

Pericoronary adipose tissue attenuation is associated with non-calcified plaque burden in patients with chronic coronary syndromes

BACKGROUND

Pericoronary adipose tissue attenuation (PCAT) is a marker of inflammation of the pericoronary fat tissue, which can be assessed by coronary computed tomography angiography (CCTA). Its prognostic value was reported in previous studies. Nevertheless, the relationship between PCAT, plaque burden and coronary artery disease (CAD) severity, are not well defined.

AIM

We sought to evaluate the relationship between PCAT, CAD severity based on the CAD-RADS 2.0 score and plaque burden in patients with chronic coronary syndrome (CCS).

METHODS

Consecutive patients with a clinical indication for CCTA due to suspected or known CCS were included in our study. PCAT was measured in the proximal 4 ​cm of each of the right coronary artery (RCA), left anterior descending artery (LAD), and the left circumflex artery (LCX). The CAD-RADS 2.0 score was assessed in all patients and total, calcified, and non-calcified plaque burden was quantitatively measured.

RESULTS

868 patients (median age of 67.0 (IQR ​= ​58.0-75.0)yrs., 400 (46.1%) female) underwent CCTA between September 2020 and August 2022 due to CCS. Weak correlations were found between PCAT and the total plaque burden, as well as with the Agatston score, whereas no correlations were found between PCAT and CAD-RADS 2.0 score. Associations were also observed between the PCAT of the LAD, RCA and LCX with non-calcified plaque burden (Odds ratios of 1.22 (95%CI ​= ​1.15-1.29), 1.11 (95%CI ​= ​1.07-1.17) and 1.14 (95%CI ​= ​1.08-1.14), respectively, p ​< ​0.001 for all) which were independent of age, the Agatston score, and the CAD-RADS 2.0 score). In addition, higher PCAT were noticed with increasing number of plaques, exhibiting high-risk features per patient (p ​< ​0.05 by ANOVA for all).

CONCLUSION

PCAT exhibits significant associations with non-calcified plaque burden and plaques with high-risk features in patients undergoing CCTA for CCS. Thus, PCAT may identify high-risk patients who could benefit from more aggressive preventive therapy, which merits further investigation in future studies.

The Association of Coronary Fat Attenuation Index Quantified by Automated Software on Coronary Computed Tomography Angiography with Adverse Events in Patients with Less than Moderate Coronary Artery Stenosis

OBJECTIVE

This study analyzed the relationship between the coronary FAI on CCTA and coronary adverse events in patients with moderate coronary artery disease based on machine learning.

METHODS

A total of 172 patients with coronary artery disease with moderate or lower coronary artery stenosis were included. According to whether the patients had coronary adverse events, the patients were divided into an adverse group and a non-adverse group. The coronary FAI of patients was quantified via machine learning, and significant differences between the two groups were analyzed via t-test.

RESULTS

The age difference between the two groups was statistically significant (p < 0.001). The group that had adverse reactions was older, and there was no statistically significant difference between the two groups in terms of sex and smoking status. There was no statistical significance in the blood biochemical indexes between the two groups (p > 0.05). There was a significant difference in the FAIs between the two groups (p < 0.05), with the FAI of the defective group being greater than that of the nonperforming group. Taking the age of patients as a covariate, an analysis of covariance showed that after excluding the influence of age, the FAIs between the two groups were still significantly different (p < 0.001).

Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction

BACKGROUND

Epicardial adipose tissue (EAT) volume is a marker of visceral obesity that can be measured in coronary computed tomography angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been documented.

OBJECTIVES

This study sought to develop a deep-learning network for automated quantification of EAT volume from CCTA, test it in patients who are technically challenging, and validate its prognostic value in routine clinical care.

METHODS

The deep-learning network was trained and validated to autosegment EAT volume in 3,720 CCTA scans from the ORFAN (Oxford Risk Factors and Noninvasive Imaging Study) cohort. The model was tested in patients with challenging anatomy and scan artifacts and applied to a longitudinal cohort of 253 patients post-cardiac surgery and 1,558 patients from the SCOT-HEART (Scottish Computed Tomography of the Heart) Trial, to investigate its prognostic value.

RESULTS

External validation of the deep-learning network yielded a concordance correlation coefficient of 0.970 for machine vs human. EAT volume was associated with coronary artery disease (odds ratio [OR] per SD increase in EAT volume: 1.13 [95% CI: 1.04-1.30]; P = 0.01), and atrial fibrillation (OR: 1.25 [95% CI: 1.08-1.40]; P = 0.03), after correction for risk factors (including body mass index). EAT volume predicted all-cause mortality (HR per SD: 1.28 [95% CI: 1.10-1.37]; P = 0.02), myocardial infarction (HR: 1.26 [95% CI:1.09-1.38]; P = 0.001), and stroke (HR: 1.20 [95% CI: 1.09-1.38]; P = 0.02) independently of risk factors in SCOT-HEART (5-year follow-up). It also predicted in-hospital (HR: 2.67 [95% CI: 1.26-3.73]; P ≤ 0.01) and long-term post-cardiac surgery atrial fibrillation (7-year follow-up; HR: 2.14 [95% CI: 1.19-2.97]; P ≤ 0.01).

CONCLUSIONS

Automated assessment of EAT volume is possible in CCTA, including in patients who are technically challenging; it forms a powerful marker of metabolically unhealthy visceral obesity, which could be used for cardiovascular risk stratification.

Elevated FAI Index of Pericoronary Inflammation on Coronary CT Identifies Increased Risk of Coronary Plaque Vulnerability after COVID-19 Infection

Inflammation is a key factor in the development of atherosclerosis, a disease characterized by the buildup of plaque in the arteries. COVID-19 infection is known to cause systemic inflammation, but its impact on local plaque vulnerability is unclear. Our study aimed to investigate the impact of COVID-19 infection on coronary artery disease (CAD) in patients who underwent computed tomography angiography (CCTA) for chest pain in the early stages after infection, using an AI-powered solution called CaRi-Heart^®. The study included 158 patients (mean age was 61.63 ± 10.14 years) with angina and low to intermediate clinical likelihood of CAD, with 75 having a previous COVID-19 infection and 83 without infection. The results showed that patients who had a previous COVID-19 infection had higher levels of pericoronary inflammation than those who did not have a COVID-19 infection, suggesting that COVID-19 may increase the risk of coronary plaque destabilization. This study highlights the potential long-term impact of COVID-19 on cardiovascular health, and the importance of monitoring and managing cardiovascular risk factors in patients recovering from COVID-19 infection. The AI-powered CaRi-Heart^® technology may offer a non-invasive way to detect coronary artery inflammation and plaque instability in patients with COVID-19.

Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease

Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide, and it is associated with considerable economic burden. In an ageing, multimorbid population, it has become increasingly important to develop reliable, consistent, low-risk, non-invasive means of diagnosing CAD. The evolution of multiple cardiac modalities in this field has addressed this dilemma to a large extent, not only in providing information regarding anatomical disease, as is the case with coronary computed tomography angiography (CCTA), but also in contributing critical details about functional assessment, for instance, using stress cardiac magnetic resonance (S-CMR). The field of artificial intelligence (AI) is developing at an astounding pace, especially in healthcare. In healthcare, key milestones have been achieved using AI and machine learning (ML) in various clinical settings, from smartwatches detecting arrhythmias to retinal image analysis and skin cancer prediction. In recent times, we have seen an emerging interest in developing AI-based technology in the field of cardiovascular imaging, as it is felt that ML methods have potential to overcome some limitations of current risk models by applying computer algorithms to large databases with multidimensional variables, thus enabling the inclusion of complex relationships to predict outcomes. In this paper, we review the current literature on the various applications of AI in the assessment of CAD, with a focus on multimodality imaging, followed by a discussion on future perspectives and critical challenges that this field is likely to encounter as it continues to evolve in cardiology.

The role of cardiac computed tomography in predicting adverse coronary events

Cardiac computed tomography (CCT) is now considered a first-line diagnostic test for suspected coronary artery disease (CAD) providing a non-invasive, qualitative, and quantitative assessment of the coronary arteries and pericoronary regions. CCT assesses vascular calcification and coronary lumen narrowing, measures total plaque burden, identifies plaque composition and high-risk plaque features and can even assist with hemodynamic evaluation of coronary lesions. Recent research focuses on computing coronary endothelial shear stress, a potent modulator in the development and progression of atherosclerosis, as well as differentiating an inflammatory from a non-inflammatory pericoronary artery environment using the simple measurement of pericoronary fat attenuation index. In the present review, we discuss the role of the above in the diagnosis of coronary atherosclerosis and the prediction of adverse cardiovascular events. Additionally, we review the current limitations of cardiac computed tomography as an imaging modality and highlight how rapid technological advancements can boost its capacity in predicting cardiovascular risk and guiding clinical decision-making.

A Study of the Fluid–Structure Interaction of the Plaque Circumferential Distribution in the Left Coronary Artery

Atherosclerotic plaques within the coronary arteries can prevent blood from flowing to downstream tissues, causing coronary heart disease and a myocardial infarction over time. The degree of stenosis is an important reference point during percutaneous coronary intervention (PCI). However, clinically, patients with the same degree of stenosis exhibit different degrees of disease severity. To investigate the connection between this phenomenon and the plaque circumferential distribution, in this paper, four models with different plaque circumferential locations were made based on the CT data. The blood in the coronary arteries was simulated using the fluid–structure interaction method in ANSYS Workbench software. The results showed that the risk of plaque rupture was less affected by the circumferential distribution of plaque, and the distribution of blood in each branch was affected by the circumferential distribution of plaque. Low TAWSS areas were found posterior to the plaque, and the TAWSS < 0.4 Pa area was ranked from highest to lowest in each model species: plaque on the side away from the left circumflex branch, plaque on the side away from the heart; plaque on the side close to the heart; and plaque on the side close to the left circumflex branch. The same trend was also found in the OSI. It was concluded that the circumferential distribution of plaques affects their further development. This finding will be useful for clinical treatment.

Cardiac computed tomography radiomics: a narrative review of current status and future directions

BACKGROUND AND OBJECTIVE

In an era of profound growth of medical data and rapid development of advanced imaging modalities, precision medicine increasingly requires further expansion of what can be interpreted from medical images. However, the current interpretation of cardiac computed tomography (CT) images mainly depends on subjective and qualitative analysis. Radiomics uses advanced image analysis to extract numerous quantitative features from digital images that are unrecognizable to the naked eye. Visualization of these features can reveal underlying connections between image phenotyping and biological characteristics and support clinical outcomes. Although research into radiomics on cardiovascular disease began only recently, several studies have indicated its potential clinical value in assessing future cardiac risk and guiding prevention and management strategies. Our review aimed to summarize the current applications of cardiac CT radiomics in the cardiovascular field and discuss its advantages, challenges, and future directions.

METHODS

We searched for English-language articles published between January 2010 and August 2021 in the databases of PubMed, Embase, and Google Scholar. The keywords used in the search included computed tomography or CT, radiomics, cardiovascular or cardiac.

KEY CONTENT AND FINDINGS

The current applications of radiomics in cardiac CT were found to mainly involve research into coronary plaques, perivascular adipose tissue (PVAT), myocardial tissue, and intracardiac lesions. Related findings on cardiac CT radiomics suggested the technique can assist the identification of vulnerable plaques or patients, improve cardiac risk prediction and stratification, discriminate myocardial pathology and etiologies behind intracardiac lesions, and offer new perspective and development prospects to personalized cardiovascular medicine.

CONCLUSIONS

Cardiac CT radiomics can gather additional disease-related information at a microstructural level and establish a link between imaging phenotyping and tissue pathology or biology alone. Therefore, cardiac CT radiomics has significant clinical implications, including a contribution to clinical decision-making. Along with advancements in cardiac CT imaging, cardiac CT radiomics is expected to provide more precise phenotyping of cardiovascular disease for patients and doctors, which can improve diagnostic, prognostic, and therapeutic decision making in the future.

TNF-α is a Novel Biomarker for Predicting Plaque Rupture in Patients with ST-Segment Elevation Myocardial Infarction

Background and Aims

Patients with plaque rupture (PR) present with different cardiovascular risks, clinical strategies, and outcomes from those with plaque erosion (PE). However, there are lack of noninvasive biomarkers to distinguish PE from PR.

Methods

A prospective analysis of 382 patients with ST-segment elevation myocardial infarction (STEMI) was conducted. Of these patients, 262 and 120 presented with PR and PE, respectively. An additional 83 patients diagnosed with stable angina pectoris were enrolled as control group. Peripheral blood monocytes were collected pre-percutaneous coronary intervention and used to evaluate the mRNA expression of IL-4, IL-10, IL-1β, and TNF-α in all patients.

Results

STEMI patients had higher IL-4, IL-10, IL-1β, and TNF-α expression than the control patients. The mRNA levels of IL-4, IL-1β, and TNF-α were significantly higher in PR patients than PE; however, no significant difference was observed in IL-10 between PE and PR. The areas under the receiver-operating characteristic curves for IL-4, IL-1β, and TNF-α for PR versus PE were 0.685, 0.747, and 0.895, respectively. At the cut-off value of 2.52, TNF-α demonstrated a sensitivity of 70.61% and specificity of 93.33% for discriminating PR from PE patients. When added to the model of established clinical risk factors, TNF-α significantly improved the predictive accuracy of PR. Multivariable logistic regression analysis indicated that TNF-α mRNA level was independently associated with PR (odds ratio, 3.09; 95% confidence interval, 2.29–4.16; p < 0.001).

Conclusion

The inflammatory response of peripheral blood mononuclear cells in patients with PR was higher than that in patients with PE. TNF-α may be a potential biomarker for predicting PR that could facilitate risk stratification and management in STEMI patients.

Coronary Computed Tomography Angiography Assessment of High-Risk Plaques in Predicting Acute Coronary Syndrome

Coronary computed tomography angiography (CCTA) is a comprehensive, non-invasive and cost-effective imaging assessment approach, which can provide the ability to identify the characteristics and morphology of high-risk atherosclerotic plaques associated with acute coronary syndrome (ACS). The development of CCTA and latest advances in emerging technologies, such as computational fluid dynamics (CFD), have made it possible not only to identify the morphological characteristics of high-risk plaques non-invasively, but also to assess the hemodynamic parameters, the environment surrounding coronaries and so on, which may help to predict the risk of ACS. In this review, we present how CCTA was used to characterize the composition and morphology of high-risk plaques prone to ACS and the current role of CCTA, including emerging CCTA technologies, advanced analysis, and characterization techniques in prognosticating the occurrence of ACS.

Differential progression of coronary atherosclerosis according to plaque composition: a cluster analysis of PARADIGM registry data

Patient-specific phenotyping of coronary atherosclerosis would facilitate personalized risk assessment and preventive treatment. We explored whether unsupervised cluster analysis can categorize patients with coronary atherosclerosis according to their plaque composition, and determined how these differing plaque composition profiles impact plaque progression. Patients with coronary atherosclerotic plaque (n = 947; median age, 62 years; 59% male) were enrolled from a prospective multi-national registry of consecutive patients who underwent serial coronary computed tomography angiography (median inter-scan duration, 3.3 years). K-means clustering applied to the percent volume of each plaque component and identified 4 clusters of patients with distinct plaque composition. Cluster 1 (n = 52), which comprised mainly fibro-fatty plaque with a significant necrotic core (median, 55.7% and 16.0% of the total plaque volume, respectively), showed the least total plaque volume (PV) progression (+ 23.3 mm³), with necrotic core and fibro-fatty PV regression (− 5.7 mm³ and − 5.6 mm³, respectively). Cluster 2 (n = 219), which contained largely fibro-fatty (39.2%) and fibrous plaque (46.8%), showed fibro-fatty PV regression (− 2.4 mm³). Cluster 3 (n = 376), which comprised mostly fibrous (62.7%) and calcified plaque (23.6%), showed increasingly prominent calcified PV progression (+ 21.4 mm³). Cluster 4 (n = 300), which comprised mostly calcified plaque (58.7%), demonstrated the greatest total PV increase (+ 50.7mm³), predominantly increasing in calcified PV (+ 35.9 mm³). Multivariable analysis showed higher risk for plaque progression in Clusters 3 and 4, and higher risk for adverse cardiac events in Clusters 2, 3, and 4 compared to that in Cluster 1. Unsupervised clustering algorithms may uniquely characterize patient phenotypes with varied atherosclerotic plaque profiles, yielding distinct patterns of progressive disease and outcome.

Association between carotid artery perivascular fat density and cerebral small vessel disease

Studies aiming to identify the significance of the carotid artery perivascular fat density are limited. The present study investigated the distribution pattern of pericarotid fat and its association with imaging markers of cerebral small vessel disease (CSVD). In total, 572 subjects who underwent both neck computed tomography angiography and cranial magnetic resonance imaging were analyzed. The pericarotid fat density near the origin of the internal carotid artery (ICA) and imaging markers of CSVD, such as lacunes, white matter hyperintensities (WMHs) and dilated perivascular spaces (PVSs), were assessed. We found that an increased pericarotid fat density was associated with the presence of lacunes and a higher WMH grade in all subjects, but in the patients with acute ischemic stroke, there was a difference only among the WMH grades. There was no significant difference in the pericarotid fat density in different grades of PVSs. The patients with acute ischemic stroke had a significantly higher mean pericarotid fat density than those without stroke. In conclusion, our study provides evidence suggesting that an increased pericarotid fat density is associated with the presence and degree of WMHs and lacunes. Our findings suggested that features that appear to extend beyond the vessel lumen of the ICA may be linked to CSVD.

Hypercholesterolemia and Lymphatic Defects: The Chicken or the Egg?

Lymphatic vessels are necessary for maintaining tissue fluid balance, trafficking of immune cells, and transport of dietary lipids. Growing evidence suggest that lymphatic functions are limited under hypercholesterolemic conditions, which is closely related to atherosclerotic development involving the coronary and other large arteries. Indeed, ablation of lymphatic systems by Chy-mutation as well as depletion of lymphangiogenic factors, including vascular endothelial growth factor-C and -D, in mice perturbs lipoprotein composition to augment hypercholesterolemia. Several investigations have reported that periarterial microlymphatics were attracted by atheroma-derived lymphangiogenic factors, which facilitated lymphatic invasion into the intima of atherosclerotic lesions, thereby modifying immune cell trafficking. In contrast to the lipomodulatory and immunomodulatory roles of the lymphatic systems, the critical drivers of lymphangiogenesis and the details of lymphatic insults under hypercholesterolemic conditions have not been fully elucidated. Interestingly, cholesterol-lowering trials enable hypercholesterolemic prevention of lymphatic drainage in mice; however, a causal relationship between hypercholesterolemia and lymphatic defects remains elusive. In this review, the contribution of aberrant lymphangiogenesis and lymphatic cholesterol transport to hypercholesterolemic atherosclerosis was highlighted. The causal relationship between hypercholesterolemia and lymphatic insults as well as the current achievements in the field were discussed.

Imaging and Targeting Coronary Artery Inflammation

Significance: Coronary artery disease (CAD) continues to be a leading cause of morbidity and mortality across the world despite significant progress in the prevention, diagnosis, and treatment of atherosclerotic disease. Recent Advances: The focus of the cardiovascular community has shifted toward seeking a better understanding of the inflammatory mechanisms driving residual CAD risk that is not modulated by current therapies. Significant progress has been achieved in revealing both proinflammatory and anti-inflammatory mechanisms, and how shift of the balance in favor of the former can drive the development of disease. Critical Issues: Advances in the noninvasive detection of coronary artery inflammation have been forthcoming. These advances include multiple imaging modalities, with novel applications of computed tomography both with and without positron emission tomography, and experimental ultrasound techniques. These advances will enable better selection of patients for anti-inflammatory treatments and assessment of treatment response. The rapid advancement in pharmaceutical design has enabled the production of specific antibodies against inflammatory pathways of atherosclerosis, with modest success to date. The pursuit of demonstrating the efficacy and safety of novel anti-inflammatory and/or proinflammatory resolution therapies for atherosclerotic CAD has become a major focus. Future Directions: This review seeks to provide an update of the latest evidence of all three of these highly related but disparate areas of inquiry: Our current understanding of the key mechanisms by which inflammation contributes to coronary artery atherosclerosis, the evidence for noninvasive assessment of coronary artery inflammation, and finally, the evidence for targeted therapies to treat coronary inflammation for the reduction of CAD risk. Antioxid. Redox Signal. 34, 1217-1243.

The Emerging Role of CT-Based Imaging in Adipose Tissue and Coronary Inflammation

A large body of evidence arising from recent randomized clinical trials demonstrate the association of vascular inflammatory mediators with coronary artery disease (CAD). Vascular inflammation localized in the coronary arteries leads to an increased risk of CAD-related events, and produces unique biological alterations to local cardiac adipose tissue depots. Coronary computed tomography angiography (CTA) provides a means of mapping inflammatory changes to both epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT) as independent markers of coronary risk. Radiodensity or attenuation of PCAT on coronary CTA, notably, provides indirect quantification of coronary inflammation and is emerging as a promising non-invasive imaging implement. An increasing number of observational studies have shown robust associations between PCAT attenuation and major coronary events, including acute coronary syndrome, and 'vulnerable' atherosclerotic plaque phenotypes that are associated with an increased risk of the said events. This review outlines the biological characteristics of both EAT and PCAT and provides an overview of the current literature on PCAT attenuation as a surrogate marker of coronary inflammation.

Coronary Microvascular Dysfunction

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Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications

Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE^® and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.

The Napkin-Ring Sign – the Story Behind Invasive Coronary Angiography

Coronary artery disease (CAD) represents one of the leading causes of morbidity and mortality across Europe. Most of the patients do not experience any warning sign before the coronary event develops, therefore screening this group of patients is essential to prevent major cardiac events. Coronary computed tomography angiography (CCTA) offers a noninvasive approach of the coronary arteries, providing information not only on the presence and severity of the coronary stenosis, but is also able to characterize the structure of the coronary wall. CCTA allows complex evaluation of the extension of CAD, and by assessing the structure of the atherosclerotic plaque, it can identify its degree of vulnerability. The napkin-ring sign (NRS) represents a ring-like attenuation of the non-calcified portion of the coronary lesion and has a high specificity (96–100%) for the identification of thin cap fibroatheroma (TCFA) or culprit lesion in acute coronary syndromes (ACS). It is also an independent predictor for ACS events and the strongest predictor for future ACS. Modern CCTA can provide submillimeter isotropic spatial resolution. Thus, CT attenuation-based tissue interpretation enables the assessment of total coronary plaque burden and individual plaque components, with a similar accuracy as intravascular ultrasoud-based investigations. This review aims to present the important role of CCTA as a potent screening tool for patients with CAD, and the current evidences in the detection and quantification of vulnerable plaques.

Assessing Cardiovascular Risk by Using the Fat Attenuation Index in Coronary CT Angiography

Coronary CT angiography (CCTA) has evolved into a first-line diagnostic test for the investigation of chest pain. Despite advances toward standardizing the reporting of CCTA through the Coronary Artery Disease Reporting and Data System (or CAD-RADS) tool, the prognostic value of CCTA in the earliest stages of atherosclerosis remains limited. Translational work on the bidirectional interplay between the coronary arteries and the perivascular adipose tissue (PVAT) has highlighted PVAT as an in vivo molecular sensor of coronary inflammation. Coronary inflammation is dynamically associated with phenotypic changes in its adjacent PVAT, which can now be detected as perivascular attenuation gradients at CCTA. These gradients are captured and quantified through the fat attenuation index (FAI), a CCTA-based biomarker of coronary inflammation. FAI carries significant prognostic value in both primary and secondary prevention (patients with and without established coronary artery disease) and offers a significant improvement in cardiac risk discrimination beyond traditional risk factors, such as coronary calcium, high-risk plaque features, or the extent of coronary atherosclerosis. Thanks to its dynamic nature, FAI may be used as a marker of disease activity, with observational studies further suggesting that it tracks the response to anti-inflammatory interventions. Finally, radiotranscriptomic studies have revealed complementary radiomic patterns of PVAT, which detect more permanent adverse fibrotic and vascular PVAT remodeling, further expanding the value of PVAT phenotyping as an important readout in modern CCTA analysis. © RSNA, 2021.

Network Medicine Approach in Prevention and Personalized Treatment of Dyslipidemias

Dyslipidemias can affect molecular networks underlying the metabolic homeostasis and vascular function leading to atherogenesis at early stages of development. Since disease-related proteins often interact with each other in functional modules, many advanced network-oriented algorithms were applied to patient-derived big data to identify the complex gene-environment interactions underlying the early pathophysiology of dyslipidemias and atherosclerosis. Both the proprotein convertase subtilisin/kexin type 7 (PCSK7) and collagen type 1 alpha 1 chain (COL1A1) genes arose from the application of TFfit and WGCNA algorithms, respectively, as potential useful therapeutic targets in prevention of dyslipidemias. Moreover, the Seed Connector algorithm (SCA) algorithm suggested a putative role of the neuropilin-1 (NRP1) protein as drug target, whereas a regression network analysis reported that niacin may provide benefits in mixed dyslipidemias. Dyslipidemias are highly heterogeneous at the clinical level; thus, it would be helpful to overcome traditional evidence-based paradigm toward a personalized risk assessment and therapy. Network Medicine uses omics data, artificial intelligence (AI), imaging tools, and clinical information to design personalized therapy of dyslipidemias and atherosclerosis. Recently, a novel non-invasive AI-derived biomarker, named Fat Attenuation Index (FAI™) has been established to early detect clinical signs of atherosclerosis. Moreover, an integrated AI-radiomics approach can detect fibrosis and microvascular remodeling improving the customized risk assessment. Here, we offer a network-based roadmap ranging from novel molecular pathways to digital therapeutics which can improve personalized therapy of dyslipidemias.

Recent Advances in Vascular Imaging

Recent advances in vascular imaging have enabled us to uncover the underlying mechanisms of vascular diseases both ex vivo and in vivo. In the past decade, efforts have been made to establish various methodologies for evaluation of atherosclerotic plaque progression and vascular inflammatory changes in addition to biomarkers and clinical manifestations. Several recent publications in Arteriosclerosis, Thrombosis, and Vascular Biology highlighted the essential roles of in vivo and ex vivo vascular imaging, including magnetic resonance image, computed tomography, positron emission tomography/scintigraphy, ultrasonography, intravascular ultrasound, and most recently, optical coherence tomography, all of which can be used in bench and clinical studies at relative ease. With new methods proposed in several landmark studies, these clinically available imaging modalities will be used in the near future. Moreover, future development of intravascular imaging modalities, such as optical coherence tomography-intravascular ultrasound, optical coherence tomography-near-infrared autofluorescence, polarized-sensitive optical coherence tomography, and micro-optical coherence tomography, are anticipated for better management of patients with cardiovascular disease. In this review article, we will overview recent advances in vascular imaging and ongoing works for future developments.

From CT to artificial intelligence for complex assessment of plaque-associated risk

The recent technological developments in the field of cardiac imaging have established coronary computed tomography angiography (CCTA) as a first-line diagnostic tool in patients with suspected coronary artery disease (CAD). CCTA offers robust information on the overall coronary circulation and luminal stenosis, also providing the ability to assess the composition, morphology, and vulnerability of atherosclerotic plaques. In addition, the perivascular adipose tissue (PVAT) has recently emerged as a marker of increased cardiovascular risk. The addition of PVAT quantification to standard CCTA imaging may provide the ability to extract information on local inflammation, for an individualized approach in coronary risk stratification. The development of image post-processing tools over the past several years allowed CCTA to provide a significant amount of data that can be incorporated into machine learning (ML) applications. ML algorithms that use radiomic features extracted from CCTA are still at an early stage. However, the recent development of artificial intelligence will probably bring major changes in the way we integrate clinical, biological, and imaging information, for a complex risk stratification and individualized therapeutic decision making in patients with CAD. This review aims to present the current evidence on the complex role of CCTA in the detection and quantification of vulnerable plaques and the associated coronary inflammation, also describing the most recent developments in the radiomics-based machine learning approach for complex assessment of plaque-associated risk.