Pericoronary adipose tissue and quantitative global non-calcified plaque characteristics from CT angiography do not differ in matched South Asian, East Asian and European-origin Caucasian patients with stable chest pain

Evaluation of peri-plaque pericoronary adipose tissue attenuation in coronary atherosclerosis using a dual-layer spectral detector CT

Purpose

This study aimed to evaluate the differences between pericoronary adipose tissue (PCAT) attenuation at different measured locations in evaluating coronary atherosclerosis using spectral computed tomography (CT) and to explore valuable imaging indicators.

Methods

A total of 330 patients with suspicious coronary atherosclerosis were enrolled and underwent coronary CT angiography with dual-layer spectral detector CT (SDCT). Proximal and peri-plaque fat attenuation index (FAI) of stenosis coronary arteries were measured using both conventional images (CIs) and virtual monoenergetic images (VMIs) ranging from 40 keV to 100 keV. The slopes of the spectral attenuation curve (λ) of proximal and peri-plaque PCAT at three different monoenergetic intervals were calculated. Additionally, peri-plaque FAI on CI and virtual non-contrast images, and effective atomic number were measured manually.

Results

A total of 231 coronary arteries with plaques and lumen stenosis were finally enrolled. Peri-plaque FAICI and FAIVMI were significantly higher in severe stenosis than in mild and moderate stenosis (p < 0.05), while peri-plaque λ, proximal FAI, and proximal λ were not statistically different. Proximal FAI, peri-plaque FAI, and peri-plaque λ were significantly higher in low-density non-calcified plaque (LD-NCP) and non-calcified plaque (NCP) than in calcified plaque (p < 0.01). Peri-plaque FAI was the highest in the LD-NCP group, while proximal FAI was the highest in the NCP group. In severe stenosis and in LD-NCP, peri-plaque FAI was significantly higher than proximal FAI (p < 0.05). The manually measured parameters related to peri-plaque PCAT attenuation had a positive correlation with the results of peri-plaque FAI measured automatically.

Conclusion

Peri-plaque PCAT has more value in assessing coronary atherosclerosis than proximal PCAT. Peri-plaque PCAT attenuation is expected to be used as a standard biomarker for evaluating plaque vulnerability and hemodynamic characteristics.

Lesion-specific pericoronary adipose tissue CT attenuation improves risk prediction of major adverse cardiovascular events in coronary artery disease

OBJECTIVES

To determine whether lesion-specific pericoronary adipose tissue CT attenuation (PCATa) is superior to PCATa around the proximal right coronary artery (PCATa-RCA) and left anterior descending artery (PCATa-LAD) for major adverse cardiovascular events (MACE) prediction in coronary artery disease (CAD).

METHODS

Six hundred and eight CAD patients who underwent coronary CTA from January 2014 to December 2018 were retrospectively included, with clinical risk factors, plaque features, lesion-specific PCATa, PCATa-RCA, and PCATa-LAD collected. MACE was defined as cardiovascular death, non-fatal myocardial infarction, unplanned revascularization, and hospitalization for unstable angina. Four models were established, encapsulating traditional factors (Model A), traditional factors and PCATa-RCA (Model B), traditional factors and PCATa-LAD (Model C), and traditional factors and lesion-specific PCATa (Model D). Prognostic performance was evaluated with C-statistic, area under receiver operator characteristic curve (AUC), and net reclassification index (NRI).

RESULTS

Lesion-specific PCATa was an independent predictor for MACE (adjusted hazard ratio = 1.108, P < .001). The C-statistic increased from 0.750 for model A to 0.762 for model B (P = .078), 0.773 for model C (P = .046), and 0.791 for model D (P = .005). The AUC increased from 0.770 for model A to 0.793 for model B (P = .027), 0.793 for model C (P = .387), and 0.820 for model D (P = .019). Compared with model A, the NRIs for models B, C, and D were 0.243 (-0.323 to 0.792, P = .392), 0.428 (-0.012 to 0.835, P = .048), and 0.708 (0.152-1.016, P = .001), respectively.

CONCLUSIONS

Lesion-specific PCATa improves risk prediction of MACE in CAD, which is better than PCATa-RCA and PCATa-LAD.

ADVANCES IN KNOWLEDGE

Lesion-specific PCATa was superior to PCATa-RCA and PCATa-LAD for MACE prediction.

Pericoronary adipose tissue attenuation on coronary computed tomography angiography associates with male sex and Indigenous Australian status

To evaluate if Indigenous Australians have higher coronary inflammation demonstrated non-invasively using pericoronary adipose tissue attenuation on coronary computed tomography angiography (CCTA). We retrospectively obtained a cohort 54 Indigenous patients age- and sex-matched to 54 non-Indigenous controls (age: 46.5 ± 13.1 years; male: n = 66) undergoing CCTA at the Royal Darwin Hospital and Monash Medical Centre. Patient groups were defined to investigate the interaction of ethnicity and sex: Indigenous + male, Indigenous + female, control + male, control + female. Semi-automated software was used to assess pericoronary adipose tissue attenuation (PCAT-a) and volume (PCAT-v). Males had significantly higher PCAT-a (- 86.7 ± 7.8 HU vs. - 91.3 ± 7.1 HU, p = 0.003) than females. Indigenous patients had significantly higher PCAT-v (1.5 ± 0.5cm3 vs. 1.3 ± 0.4cm3, p = 0.032), but only numerically higher PCAT-a (p = 0.133) than controls. There was a significant difference in PCAT-a and PCAT-v across groups defined by Indigenous status and sex (p = 0.010 and p = 0.030, respectively). Among patients with matching CCTA contrast density, multivariable linear regression analysis showed an independent association between Indigenous status and PCAT-a. Indigenous men have increased PCAT-a in an age- and sex-matched cohort. Male sex is strongly associated with increased PCAT-a. Coronary inflammation may contribute to adverse cardiovascular outcomes in Indigenous Australians, but larger studies are required to validate these findings.

Cardiometabolic predictors of high-risk CCTA phenotype in a diverse patient population

Introduction

Low-attenuation non-calcified plaque (LAP) burden and vascular inflammation by pericoronary adipose tissue (PCAT) measured from coronary CT angiography (CCTA) have shown to be predictors of cardiovascular outcomes. We aimed to investigate the relationships of cardiometabolic risk factors including lipoprotein(a) and epicardial adipose tissue (EAT) with CCTA high-risk imaging biomarkers, LAP and vascular inflammation.

Methods

The patient population consisted of consecutive patients who underwent CCTA for stable chest pain and had a complete cardiometabolic panel including lipoprotein(a). Plaque, PCAT and EAT were measured from CT using semiautomated software. Elevated LAP burden and PCAT attenuation were defined as ≥4% and ≥70.5 HU, respectively. The primary clinical end-point was a composite of myocardial infarction, revascularization or cardiovascular death.

Results

A total of 364 consecutive patients were included (median age 56 years, 64% female); the majority of patients were of Hispanic (60%), and the rest were of non-Hispanic Black (21%), non-Hispanic White (6%) and non-Hispanic Asian (4%) race/ethnicity. The prevalence of elevated LAP burden and PCAT attenuation was 31 and 18%, respectively, while only 8% had obstructive stenosis. There were significant differences in plaque characteristics among different racial/ethnic groups (p<0.001). Lipoprotein(a) correlated with LAP burden in Hispanic patients. Patients with elevated LAP were older, more likely to be have diabetes, hypertension, hyperlipidemia and smoke with higher CAC and EAT volume (all P<0.05). Patients with elevated LAP were more likely to develop the primary clinical outcome (p<0.001) but those with elevated PCAT were not (p=0.797).

Conclusion

The prevalence of LAP and PCAT attenuation were 31 and 18%, respectively. Lipoprotein(a) levels correlated with LAP burden in Hispanic patients. Age, male sex, hypertension and hyperlipidemia increased the odds of elevated LAP, which showed prognostic significance.

Non-Contrast and Contrast-Enhanced Cardiac Computed Tomography Imaging in the Diagnostic and Prognostic Evaluation of Coronary Artery Disease

In recent decades, cardiac computed tomography (CT) has emerged as a powerful non-invasive tool for risk stratification, as well as the detection and characterization of coronary artery disease (CAD), which remains the main cause of morbidity and mortality in the world. Advances in technology have favored the increasing use of cardiac CT by allowing better performance with lower radiation doses. Coronary artery calcium, as assessed by non-contrast CT, is considered to be the best marker of subclinical atherosclerosis, and its use is recommended for the refinement of risk assessment in low-to-intermediate risk individuals. In addition, coronary CT angiography (CCTA) has become a gate-keeper to invasive coronary angiography (ICA) and revascularization in patients with acute chest pain by allowing the assessment not only of the extent of lumen stenosis, but also of its hemodynamic significance if combined with the measurement of fractional flow reserve or perfusion imaging. Moreover, CCTA provides a unique incremental value over functional testing and ICA by imaging the vessel wall, thus allowing the assessment of plaque burden, composition, and instability features, in addition to perivascular adipose tissue attenuation, which is a marker of vascular inflammation. There exists the potential to identify the non-obstructive lesions at high risk of progression to plaque rupture by combining all of these measures.

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.

Computed tomography and nuclear medicine for the assessment of coronary inflammation: clinical applications and perspectives

There is increasing evidence that in patients with atherosclerotic cardiovascular disease (ASCVD) under optimal medical therapy, a persisting dysregulation of the lipid and glucose metabolism, associated with adipose tissue dysfunction and inflammation, predicts a substantial residual risk of disease progression and cardiovascular events. Despite the inflammatory nature of ASCVD, circulating biomarkers such as high-sensitivity C-reactive protein and interleukins may lack specificity for vascular inflammation. As known, dysfunctional epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT) produce pro-inflammatory mediators and promote cellular tissue infiltration triggering further pro-inflammatory mechanisms. The consequent tissue modifications determine the attenuation of PCAT as assessed and measured by coronary computed tomography angiography (CCTA). Recently, relevant studies have demonstrated a correlation between EAT and PCAT and obstructive coronary artery disease, inflammatory plaque status and coronary flow reserve (CFR). In parallel, CFR is well recognized as a marker of coronary vasomotor function that incorporates the haemodynamic effects of epicardial, diffuse and small-vessel disease on myocardial tissue perfusion. An inverse relationship between EAT volume and coronary vascular function and the association of PCAT attenuation and impaired CFR have already been reported. Moreover, many studies demonstrated that 18F-FDG PET is able to detect PCAT inflammation in patients with coronary atherosclerosis. Importantly, the perivascular FAI (fat attenuation index) showed incremental value for the prediction of adverse clinical events beyond traditional risk factors and CCTA indices by providing a quantitative measure of coronary inflammation. As an indicator of increased cardiac mortality, it could guide early targeted primary prevention in a wide spectrum of patients. In this review, we summarize the current evidence regarding the clinical applications and perspectives of EAT and PCAT assessment performed by CCTA and the prognostic information derived by nuclear medicine.

Evaluation of pericoronary adipose tissue attenuation on CT

Pericoronary adipose tissue (PCAT) is the fat deposit surrounding coronary arteries. Although PCAT is part of the larger epicardial adipose tissue (EAT) depot, it has different pathophysiological features and roles in the atherosclerosis process. While EAT evaluation has been studied for years, PCAT evaluation is a relatively new concept. PCAT, especially the mean attenuation derived from CT images may be used to evaluate the inflammatory status of coronary arteries non-invasively. The most commonly used measure, PCAT_MA, is the mean attenuation of adipose tissue of 3 mm thickness around the proximal right coronary artery with a length of 40 mm. PCAT_MA can be analyzed on a per-lesion, per-vessel or per-patient basis. Apart from PCAT_MA, other measures for PCAT have been studied, such as thickness, and volume. Studies have shown associations between PCAT_MA and anatomical and functional severity of coronary artery disease. PCAT_MA is associated with plaque components and high-risk plaque features, and can discriminate patients with flow obstructing stenosis and myocardial infarction. Whether PCAT_MA has value on an individual patient basis remains to be determined. Furthermore, CT imaging settings, such as kV levels and clinical factors such as age and sex affect PCAT_MA measurements, which complicate implementation in clinical practice. For PCAT_MA to be widely implemented, a standardized methodology is needed. This review gives an overview of reported PCAT methodologies used in current literature and the potential use cases in clinical practice.

The effect of patient and imaging characteristics on coronary CT angiography assessed pericoronary adipose tissue attenuation and gradient

BACKGROUND

Coronary CT angiography (CCTA) pericoronary adipose tissue (PCAT) markers are promising indicators of inflammation.

OBJECTIVE

To determine the effect of patient and imaging parameters on the associations between non-calcified plaque (NCP) and PCAT attenuation and gradient.

METHODS

This was a single-center, retrospective analysis of consecutive patients with stable chest pain who underwent CCTA and had zero calcium scores. CCTA images were evaluated for the presence of NCP, obstructive stenosis, segment stenosis and involvement score (SSS, SIS), and high-risk plaque (HRP). PCAT markers were assessed using semi-automated software. Uni- and multivariable regression models correcting for patient and imaging characteristics between plaque and PCAT markers were evaluated.

RESULTS

Overall, 1652 patients had zero calcium score (mean age: 51 years ​± ​11 [SD], 871 women); PCAT attenuation values ranged between -123 HU and -51 HU, and 649 patients had plaque. In univariable analysis, the presence of NCP, SSS, SIS, and HRP were associated with PCAT attenuation (2, 1, 1, 6 HU; respectively; p ​< ​.001 all); while obstructive stenosis was not (1 HU, p ​= ​.58). In multivariable analysis, none of the plaque markers were associated with PCAT attenuation (0 HU p ​= ​.93, 0 HU p ​= ​.39, 1 HU p ​= ​.18, 2 HU p ​= ​.10, 1 HU p ​= ​.71, respectively), while patient and imaging characteristics showed significant associations, such as: male sex (1 HU, p ​= ​.003), heart rate [1/min] (-0.2 HU, p ​< ​.001), 120 ​kVp (8 HU, p ​< ​.001) and pixel spacing [mm³] (32 HU, p ​< ​.001). Similar results were observed for PCAT gradient.

CONCLUSION

PCAT markers were significantly associated with NCP, however the associations did not persist following correction for patient and imaging characteristics.

Association of epicardial adipose tissue with coronary CT angiography plaque parameters on cardiovascular outcome in patients with and without diabetes mellitus

BACKGROUND AND AIMS

We aimed to evaluate the association of epicardial adipose tissue (EAT) with coronary CT angiography (CCTA) plaque parameters on cardiovascular outcome in patients with and without diabetes mellitus.

METHODS

Data of 353 patients (62.9 ± 10.4 years, 62% male), who underwent CCTA as part of their clinical workup for the evaluation of suspected or known CAD, were retrospectively analyzed. EAT volume and plaque parameters from CCTA were compared in patients with diabetes (n = 63) and without diabetes (n = 290). Follow-up was performed to record adverse cardiovascular events. The predictive value to detect adverse cardiovascular events was assessed using concordance indices (CIs) and multivariable Cox proportional hazards analysis.

RESULTS

In total, 33 events occurred after a median follow-up of 5.1 years. In patients with diabetes, EAT volume and plaque parameters were significantly higher than in patients without diabetes (all p < 0.05). A multivariable model demonstrated an incrementally improved C-index of 0.84 (95%CI 0.80-0.88) over the Framingham risk score and single measures alone. In multivariable Cox regression analysis EAT volume (Hazard ratio[HR] 1.21, p = 0.022), obstructive CAD (HR 1.18, p = 0.042), and ≥2 high-risk plaque features (HR 2.13, p = 0.031) were associated with events in patients with diabetes and obstructive CAD (HR 1.88, p = 0.017), and Agatston calcium score (HR 1.009, p = 0.039) in patients without diabetes.

CONCLUSIONS

EAT, as a biomarker of inflammation, and plaque parameters, as an extent of atherosclerotic CAD, are higher in patients with diabetes and are associated with increased adverse cardiovascular outcomes. These parameters may help identify patients at high risk with need for more aggressive therapeutic and preventive care.

Predicting coronary artery calcified plaques using perivascular fat CT radiomics features and clinical risk factors

OBJECTIVE

The purpose of this study was to develop a combined radiomics model to predict coronary plaque texture using perivascular fat CT radiomics features combined with clinical risk factors.

METHODS

The data of 200 patients with coronary plaques were retrospectively analyzed and randomly divided into a training group and a validation group at a ratio of 7:3. In the training group, The best feature set was selected by using the maximum correlation minimum redundancy method and the least absolute shrinkage and selection operator. Radiomics models were built based on different machine learning algorithms. The clinical risk factors were then screened using univariate logistic regression analysis. and finally a combined radiomics model was developed using multivariate logistic regression analysis to combine the best performing radiomics model with clinical risk factors and validated in the validation group. The efficacy of the model was assessed by a receiver operating characteristic curve, the consistency of the nomogram was assessed using calibration curves, and the clinical usefulness of the nomogram was assessed using decision curve analysis.

RESULTS

Twelve radiomics features were used by different machine learning algorithms to construct the radiomics model. Finally, the random forest algorithm built the best radiomics model in terms of efficacy, and this was combined with age to construct a combined radiomics model. The area under curve for the training and validation group were 0.98 (95% confidence interval, 0.95-1.00) and 0.97 (95% confidence interval, 0.92-1.00) with sensitivities of 0.92 and 0.86 and specificities of 0.99 and 1, respectively. The calibration curve demonstrated that the nomogram had good consistency, and the decision curve analysis demonstrated that the nomogram had high clinical utility.

CONCLUSIONS

The combined radiomics model established based on CT radiomics features and clinical risk factors has high value in predicting coronary artery calcified plaque and can provide a reference for clinical decision-making.

Non-contrast CT-based radiomic signature of pericoronary adipose tissue for screening non-calcified plaque

Objective. To develop two combined clinical-radiomics models of pericoronary adipose tissue (PCAT) for the presence and characterization of non-calcified plaques on non-contrast CT scan. Approach. Altogether, 431 patients undergoing Coronary Computed Tomography Angiography from March 2019 to June 2021 who had complete data were enrolled, including 173 patients with non-calcified plaques of the right coronary artery(RCA) and 258 with no abnormality. PCAT was segmented around the proximal RCA on non-contrast CT scan (calcium score acquisition). Two best models were established by screening features and classifiers respectively using FeAture Explorer software. Model 1 distinguished normal coronary arteries from those with non-calcified plaques, and model 2 distinguished vulnerable plaques in non-calcified plaques. Performance was assessed by the area under the receiver operating characteristic curve (AUC-ROC). Main results. 4 and 9 features were selected for the establishment of the radiomics model respectively through Model 1 and 2. In the test group, the AUC values, sensitivity, specificity and accuracy were 0.833%, 78.3%, 80.8%, 76.6% and 0.7467%, 75.0%, 77.8%, 73.5%, respectively. The combined model including radiomics features and independent clinical factors yielded an AUC, sensitivity, specificity and accuracy of 0.896%, 81.4%, 86.5%, 77.9% for model 1 and 0.752%, 75.0%, 77.8%, 73.5% for model 2. Significance. The combined clinical-radiomics models based on non-contrast CT images of PCAT had good diagnostic efficacy for non-calcified and vulnerable plaques.

Influence of Different Segmentations on the Diagnostic Performance of Pericoronary Adipose Tissue

Objective

To investigate the influence of different segmentations on the diagnostic performance of pericoronary adipose tissue (PCAT) CT attenuation and radiomics features for the prediction of ischemic coronary artery stenosis.

Methods

From June 2016 to December 2018, 108 patients with 135 vessels were retrospectively analyzed in the present study. Vessel-based PCAT was segmented along the 40 mm-long proximal segments of three major epicardial coronary arteries, while lesion-based PCAT was defined around coronary lesions. CT attenuation and radiomics features derived from two segmentations were calculated and extracted. The diagnostic performance of PCAT CT attenuation or radiomics models in predicting ischemic coronary stenosis were also compared between vessel-based and lesion-based segmentations.

Results

The mean PCAT CT attenuation was −75.7 ± 9.1 HU and −76.1 ± 8.1 HU (p = 0.395) for lesion-based and vessel-based segmentations, respectively. A strong correlation was found between vessel-based and lesion-based PCAT CT attenuation for all cohort and subgroup analyses (all p < 0.01). A good agreement for all cohort and subgroup analyses was also detected between two segmentations. The diagnostic performance was comparable between vessel-based and lesion based PCAT CT attenuation in predicting ischemic stenosis. The radiomics features of PCAT based on vessel or lesion segmentation can both adequately identify the ischemic stenosis. However, no significant difference was detected between the two segmentations.

Conclusions

The quantitative evaluation of PCAT can be reliably measured both from vessel-based and lesion-based segmentation. Furthermore, the radiomics analysis of PCAT may potentially help predict hemodynamically significant coronary artery stenosis.

Additive value of epicardial adipose tissue quantification to coronary CT angiography-derived plaque characterization and CT fractional flow reserve for the prediction of lesion-specific ischemia

OBJECTIVES

Epicardial adipose tissue (EAT) from coronary CT angiography (CCTA) is strongly associated with coronary artery disease (CAD). We investigated the additive value of EAT volume to coronary plaque quantification and CT-derived fractional flow reserve (CT-FFR) to predict lesion-specific ischemia.

METHODS

Patients (n = 128, 60.6 ± 10.5 years, 61% male) with suspected CAD who had undergone invasive coronary angiography (ICA) and CCTA were retrospectively analyzed. EAT volume and plaque measures were derived from CCTA using a semi-automatic software approach, while CT-FFR was calculated using a machine learning algorithm. The predictive value and discriminatory power of EAT volume, plaque measures, and CT-FFR to identify ischemic CAD were assessed using invasive FFR as the reference standard.

RESULTS

Fifty-five of 152 lesions showed ischemic CAD by invasive FFR. EAT volume, CCTA ≥ 50% stenosis, and CT-FFR were significantly different in lesions with and without hemodynamic significance (all p < 0.05). Multivariate analysis revealed predictive value for lesion-specific ischemia of these parameters: EAT volume (OR 2.93, p = 0.021), CCTA ≥ 50% (OR 4.56, p = 0.002), and CT-FFR (OR 6.74, p < 0.001). ROC analysis demonstrated incremental discriminatory value with the addition of EAT volume to plaque measures alone (AUC 0.84 vs. 0.62, p < 0.05). CT-FFR (AUC 0.89) showed slightly superior performance over EAT volume with plaque measures (AUC 0.84), however without significant difference (p > 0.05).

CONCLUSIONS

EAT volume is significantly associated with ischemic CAD. The combination of EAT volume with plaque quantification demonstrates a predictive value for lesion-specific ischemia similar to that of CT-FFR. Thus, EAT may aid in the identification of hemodynamically significant coronary stenosis.

KEY POINTS

• CT-derived EAT volume quantification demonstrates high discriminatory power to identify lesion-specific ischemia. • EAT volume shows incremental diagnostic performance over CCTA-derived plaque measures in detecting lesion-specific ischemia. • A combination of plaque measures with EAT volume provides a similar discriminatory value for detecting lesion-specific ischemia compared to CT-FFR.

Cardiovascular computed tomography imaging for coronary artery disease risk: plaque, flow and fat

Cardiac imaging is central to the diagnosis and risk stratification of coronary artery disease, beyond symptoms and clinical risk factors, by providing objective evidence of myocardial ischaemia and characterisation of coronary artery plaque. CT coronary angiography can detect coronary plaque with high resolution, estimate the degree of functional stenosis and characterise plaque features. However, coronary artery disease risk is also driven by biological processes, such as inflammation, that are not fully reflected by severity of stenosis, myocardial ischaemia or by coronary plaque features. New cardiac CT techniques can assess coronary artery inflammation by imaging perivascular fat, and this may represent an important step forward in identifying the ‘residual risk’ that is not detected by plaque or ischaemia imaging. Coronary artery disease risk assessment that incorporates clinical factors, plaque characteristics and perivascular inflammation offers a more comprehensive individualised approach to quantify and stratify coronary artery disease risk, with potential healthcare benefits for prevention, diagnosis and treatment recommendations. Furthermore, identifying new biomarkers of cardiovascular risk has the potential to refine early-life prevention strategies, before atherosclerosis becomes established.

Predicting haemodynamic significance of coronary stenosis with radiomics-based pericoronary adipose tissue characteristics

AIM

To investigate the diagnostic performance of the radiomics features of pericoronary adipose tissue (PCAT) in determining haemodynamically significant coronary artery stenosis as evaluated by fractional flow reserve (FFR).

MATERIALS AND METHODS

A total of 92 patients with clinically suspected coronary artery disease who underwent coronary computed tomography (CT) angiography (CCTA), invasive coronary angiography (ICA), and FFR examination within 1 month were included retrospectively, and 121 lesions were randomly assigned to the training and testing set. Based on manual segmentation of PCAT, 1,116 radiomics features were computed. After radiomics robustness assessment and feature selection, radiomics models were established using the different machine-learning algorithms. The area under the receiver operating characteristic (ROC) curve (AUC) and net reclassification index (NRI) were analysed to compare the discrimination and reclassification abilities of radiomics models.

RESULTS

Two radiomics features were selected after exclusions, and both were significantly higher in coronary arteries with FFR ≤0.8 than those with FFR >0.8. ROC analysis showed that the combination of CCTA and decision tree radiomics model achieved significantly higher diagnostic performance (AUC: 0.812) than CCTA alone (AUC: 0.599, p=0.015). Furthermore, the NRI of the combined model was 0.820 and 0.775 in the training and testing sets, respectively, suggesting the radiomics features of PCAT had were effective in classifying the haemodynamic significance of coronary stenosis.

CONCLUSIONS

Adding PCAT radiomics features to CCTA enabled identification of haemodynamically significant coronary artery stenosis.

Standardized measurement of coronary inflammation using cardiovascular computed tomography: integration in clinical care as a prognostic medical device

AIMS

Coronary computed tomography angiography (CCTA) is a first-line modality in the investigation of suspected coronary artery disease (CAD). Mapping of perivascular fat attenuation index (FAI) on routine CCTA enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which integrates standardized FAI mapping together with clinical risk factors and plaque metrics to provide individualized cardiovascular risk prediction.

METHODS AND RESULTS

The study included 3912 consecutive patients undergoing CCTA as part of clinical care in the USA (n = 2040) and Europe (n = 1872). These cohorts were used to generate age-specific nomograms and percentile curves as reference maps for the standardized interpretation of FAI. The first output of CaRi-Heart® is the FAI-Score of each coronary artery, which provides a measure of coronary inflammation adjusted for technical, biological, and anatomical characteristics. FAI-Score is then incorporated into a risk prediction algorithm together with clinical risk factors and CCTA-derived coronary plaque metrics to generate the CaRi-Heart® Risk that predicts the likelihood of a fatal cardiac event at 8 years. CaRi-Heart® Risk was trained in the US population and its performance was validated externally in the European population. It improved risk discrimination over a clinical risk factor-based model [Δ(C-statistic) of 0.085, P = 0.01 in the US Cohort and 0.149, P < 0.001 in the European cohort] and had a consistent net clinical benefit on decision curve analysis above a baseline traditional risk factor-based model across the spectrum of cardiac risk.

CONCLUSION

Mapping of perivascular FAI on CCTA enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which allows standardized measurement of coronary inflammation by calculating the FAI-Score of each coronary artery. The CaRi-Heart® device provides a reliable prediction of the patient's absolute risk for a fatal cardiac event by incorporating traditional cardiovascular risk factors along with comprehensive CCTA coronary plaque and perivascular adipose tissue phenotyping. This integration advances the prognostic utility of CCTA for individual patients and paves the way for its use as a dual diagnostic and prognostic tool among patients referred for CCTA.

Coronary artery disease in East and South Asians: differences observed on cardiac CT

Epidemiological studies have observed East Asians (EAs) are significantly less likely to develop or die from coronary artery disease (CAD) compared with Caucasians. Conversely South Asians (SAs) develop CAD at higher rate and earlier age. Recently, a range of features derived from cardiac CT have been identified which may further characterise ethnic differences in CAD. Emerging data suggest EAs exhibit less coronary calcification and high-risk, non-calcified plaque compared with Caucasians on CT, with no difference in luminal stenosis. In contrast, SAs exhibit similar to higher coronary calcification and luminal stenosis, smaller luminal dimensions and more high-risk, non-calcified plaque than Caucasians. Beyond demonstrating ethnic differences in CAD, cardiac CT may enhance and individualise cardiovascular risk stratification in EAs and SAs. While data thus far in EAs have demonstrated calcium score and CT-derived luminal stenosis may incrementally predict cardiovascular risk beyond traditional risk scores, there remains a paucity of data assessing its use in SAs. Future studies may clarify the prognostic value of cardiac CT in SAs and investigate how this modality may guide preventative therapy and coronary intervention of CAD in EAs and SAs.

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.

Pericoronary Adipose Tissue Attenuation Is Associated with High-Risk Plaque and Subsequent Acute Coronary Syndrome in Patients with Stable Coronary Artery Disease

BACKGROUND

High-risk plaques (HRP) detected on coronary computed tomography angiography (CTA) confer an increased risk of acute coronary syndrome (ACS). Pericoronary adipose tissue attenuation (PCAT) is a novel biomarker of coronary inflammation. This study aimed to evaluate the association of PCAT with HRP and subsequent ACS development in patients with stable coronary artery disease (CAD).

METHODS

Patients with stable CAD who underwent coronary CTA from 2011 to 2016 and had available outcome data were included. We studied 41 patients with HRP propensity matched to 41 controls without HRP (60 ± 10 years, 67% males). PCAT was assessed using semi-automated software on a per-patient basis in the proximal right coronary artery (PCATRCA) and a per-lesion basis (PCATLesion) around HRP in cases and the highest-grade stenosis lesions in controls.

RESULTS

PCATRCA and PCATLesion were higher in HRP patients than controls (PCATRCA: -80.7 ± 6.50 HU vs. -84.2 ± 8.09 HU, p = 0.03; PCATLesion: -79.6 ± 7.86 HU vs. -84.2 ± 10.3 HU, p = 0.04), and were also higher in men (PCATRCA: -80.5 ± 7.03 HU vs. -86.1 ± 7.08 HU, p < 0.001; PCATLesion: -79.6 ± 9.06 HU vs. -85.2 ± 7.96 HU, p = 0.02). Median time to ACS was 1.9 years, within a median follow-up of 5.3 years. PCATRCA alone was higher in HRP patients who subsequently presented with ACS (-76.8 ± 5.69 HU vs. -82.0 ± 6.32 HU, p = 0.03). In time-dependent analysis, ACS was associated with HRP and PCATRCA.

CONCLUSIONS

PCAT attenuation is increased in stable CAD patients with HRP and is associated with subsequent ACS development. Further investigation is required to determine the clinical implications of these findings.

Imaging of the Pericoronary Adipose Tissue (PCAT) Using Cardiac Computed Tomography: Modern Clinical Implications

Modern coronary computed tomography angiography (CTA) is the gold standard to visualize the epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT). The EAT is a metabolic active fat depot enclosed by the visceral pericardium and surrounds the coronary arteries. In disease states with increased EAT volume and dysfunctional adipocytes, EAT secretes an increased amount of adipocytokines and the resulting imbalance of proinflammatory and anti-inflammatory mediators potentially causes atherogenic effects on the coronary vessel wall in a paracrine way ("outside-to-inside" signaling). These EAT-induced atherogenic effects are reported to increase the risk for the development of coronary artery disease, myocardial ischemia, high-risk plaque features, and future major adverse cardiac events. Coronary inflammation plays a key role in the development and progression of coronary artery disease; however, its noninvasive detection remains challenging. In future, this clinical dilemma might be changed by the CTA-derived analysis of the PCAT. On the basis of the concept of an "inside-to-outside" signaling between the inflamed coronary vessel wall and the surrounding PCAT recent evidence demonstrates that PCAT computed tomography attenuation especially around the right coronary artery derived from routine CTA is a promising imaging biomarker and "sensor" to noninvasively detect coronary inflammation. This review summarizes the biological and technical principles of CTA-derived PCAT analysis and highlights its clinical implications to improve modern cardiovascular prevention strategies.

Pericoronary adipose tissue CT attenuation and volume: Diagnostic performance for hemodynamically significant stenosis in patients with suspected coronary artery disease

OBJECTIVES

The aim of this study was to investigate the diagnostic abilities of both pericoronary adipose tissue (PCAT) CT attenuation and volume for the predication hemodynamic significance of coronary artery stenosis as evaluated by fractional flow reserve (FFR).

METHODS

Patients with ≥ 30 % in at least 1 major epicardial coronary artery were retrospectively included. Furthermore, all eligible patients underwent coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) as well as FFR within 1 month. PCAT CT attenuation and volume around ischemic and non-ischemic coronary stenosis were measured and compared. The diagnostic accuracy of PCAT CT attenuation and volume for the identification of hemodynamically significant stenosis was determined against the reference standard of FFR ≤ 0.80.

RESULTS

A total of 61 patients (mean age, 57.8 years ± 11.8) with 77 vessels were included. Average PCAT CT attenuation of all vessels was -70.3 ± 7.4 HU. PCAT CT attenuation in coronary arteries with hemodynamically significant stenosis (FFR ≤ 0.80) (-65.6 ± 5.9 HU) was significantly higher than those with FFR > 0.80 (-75.3 ± 5.4 HU; p = 0.000). There was a strong correlation between FFR and PCAT CT attenuation (r = 0.64, p < 0.001). However, no significant difference in PCAT volume was observed between FFR ≤ 0.8 (5.0 ± 3.5 cm³) and FFR > 0.80 (5.5 ± 3.7 cm³, p = 0.511). The diagnostic accuracy was significantly higher in the combination of CCTA and PCAT CT attenuation compared with CCTA alone (area under the curve: 0.869 vs. 0.569, p < 0.001).

CONCLUSIONS

PCAT CT attenuation but not volume was related to the hemodynamic significance of coronary artery stenosis. For the patients with suspected coronary artery disease, after adding of PCAT CT attenuation to CCTA, the diagnostic ability for the identification of ischemic coronary stenosis was significantly improved.

Pericoronary adipose tissue CT attenuation and its association with serum levels of atherosclerosis-relevant inflammatory mediators, coronary calcification and major adverse cardiac events

BACKGROUND

Increased attenuation of pericoronary adipose tissue (PCAT) around the right coronary artery (RCA) derived from coronary CTA might detect coronary inflammation. We investigated a potential association between RCA PCAT attenuation and serum levels of atherosclerosis-relevant cytokines and MACE (coronary revascularization, myocardial infarction and/or cardiac death).

METHODS

Blood samples of 293 clinically stable individuals (59.0 ​± ​9.8 years, 69% males) were analyzed for atherosclerosis-relevant cytokines including interleukin (IL)-2, IL- 4, IL-6, IL-7, IL-8, IL-10, IL-13, IL-15, IL-17, TNF-a, IP-10, CRP, MCP-1, MIP-1a, Eotaxin and GM-CSF. Subjects also underwent coronary calcium scoring (CCS) followed by CTA. PCAT CT attenuation was measured around the RCA using semi-automated software. Increased RCA PCAT attenuation was defined as PCAT attenuation above the 75th percentile (>-73.5 HU). To assess MACE, 232 individuals were followed for a mean duration of 9.6 ​± ​2.1 years.

RESULTS

In patients with increased RCA PCAT attenuation the serum levels of MCP-1 were increased (p ​< ​0.01), whereas levels of anti-inflammatory mediators IL-4 and -13 were significantly reduced (each p ​< ​0.05). Adipocytokine MCP-1 (r ​= ​0.23, p ​< ​0.01) and pro-inflammatory mediator IL-7 (r ​= ​0.12, p ​= ​0.04) showed a mild positive correlation with RCA PCAT attenuation, whereas anti-inflammatory mediators Il-4, -10 and -13 correlated inversely (each r < -0.12, each p ​< ​0.05). 40/232 patients experienced MACE during follow-up. In multivariable Cox regression analysis increased RCA PCAT attenuation was shown to be an independent predictor of MACE (HR 2.01, p ​= ​0.044).

CONCLUSIONS

Increased RCA PCAT CT attenuation shows a weak association with serum levels of selected atherosclerosis-relevant inflammatory biomarkers. Increased RCA PCAT attenuation is an independent predictor of MACE and may potentially guide future prevention strategies in stable patients.

Epicardial fat and coronary artery disease: Role of cardiac imaging

Epicardial adipose tissue (EAT) represents the fat depot located between the myocardium and the visceral pericardial layer. Far from being an inert tissue, EAT has been recognized as secreting a large amount of bioactive molecules called adipokines, which have numerous exocrine and paracrine effects. Recent evidence demonstrates that pericoronary adipose tissue (PCAT) - the EAT directly surrounding the coronary arteries - has a complex bidirectional interaction with the underlying vascular wall. While in normal conditions this mutual cross-talk helps maintain the homeostasis of the vascular wall, dysfunctional PCAT produces deleterious pro-inflammatory adipokines involved in atherogenesis. Importantly, PCAT inflammation has been associated with coronary artery disease (CAD) and major cardiovascular events. This review aims to provide an overview of the imaging techniques used to assess EAT, with a specific focus on cardiac computed tomography (CCT), which has become the key modality in this field. In contrast to echocardiography and cardiac magnetic resonance (CMR), CCT is not only able to visualize and precisely quantify EAT, but also to assess the coronary arteries and the PCAT simultaneously. In recent years, several papers have shown the utility of using CCT-derived PCAT attenuation as a surrogate measure of coronary inflammation. This noninvasive imaging biomarker may potentially be used to monitor patient responses to new antinflammatory drugs for the treatment of CAD.

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.