Incremental diagnostic value of radiomics signature of pericoronary adipose tissue for detecting functional myocardial ischemia: a multicenter study

OBJECTIVES

To determine the incremental diagnostic value of radiomics signature of pericoronary adipose tissue (PCAT) in addition to the coronary artery stenosis and plaque characters for detecting hemodynamic significant coronary artery disease (CAD) based on coronary computed tomography angiography (CCTA).

METHODS

In a multicenter trial of 262 patients, CCTA and invasive coronary angiography were performed, with fractional flow reserve (FFR) in 306 vessels. A total of 13 conventional quantitative characteristics including plaque characteristics (N = 10) and epicardial adipose tissue characteristics (N = 3) were obtained. A total of 106 radiomics features depicting the phenotype of the PCAT surrounding the lesion were calculated. All data were randomly split into a training dataset (75%) and a testing dataset (25%). Then three models (including the conventional model, the PCAT radiomics model, and the combined model) were established in the training dataset using multivariate logistic regression algorithm based on the conventional quantitative features and the PCAT radiomics features after dimension reduction.

RESULTS

A total of 124/306 vessels showed functional ischemia (FFR ≤ 0.80). The radiomics model performed better in discriminating ischemia from non-ischemia than the conventional model in both training (area under the receiver operating characteristic (ROC) curve (AUC): 0.770 vs 0.732, p < 0.05) and testing datasets (AUC: 0.740 vs 0.696, p < 0.05). The combined model showed significantly better discrimination than the conventional model in both training (AUC: 0.810 vs 0.732, p < 0.05) and testing datasets (AUC: 0.809 vs 0.696, p < 0.05).

CONCLUSIONS

The PCAT radiomics model showed good performance in predicting myocardial ischemia. Addition of PCAT radiomics to lesion quantitative characteristics improves the predictive power of functionally relevant CAD.

KEY POINTS

• Based on the plaque characteristics and EAT characteristics, the conventional model showed poor performance in predicting myocardial ischemia. • The PCAT radiomics model showed good prospect in predicting myocardial ischemia. • When combining the radiomics signature with the conventional quantitative features (including plaque features and EAT features), it showed significantly better performance in predicting myocardial ischemia.

Epicardial Adipose Tissue Role as a Marker of Higher Vulnerability in Patients with Coronary Artery Disease

Background: Epicardial adipose tissue (EAT) has been recently identified as a major player in the development of the atherosclerotic process. This study aimed to investigate the role of EAT as a marker associated with a higher vulnerability of atheromatous coronary plaques in patients with acute myocardial infarction (AMI) as compared to patients with stable angina.

Material and methods: This analysis enrolled a total of 89 patients, 47 with stable angina (SA) and 42 with AMI, who underwent echocardiographic investigations and epicardial fat measurement in 2D-parasternal long axis view. The study lot was divided as follows: Group 1 included patients with prior AMI, and Group 2 included patients with SA.

Results: There were no significant differences between the two groups regarding cardiovascular risk factors, excepting smoking status, which was recorded more frequently in Group 1 as compared to Group 2 (36.17% vs. 11.63%, p = 0.02). The mean epicardial fat diameter was 9.12 ± 2.28 mm (95% CI: 8.45–9.79 mm) in Group 1 and 6.30 ± 2.03 mm (95% CI: 5.675–6.93 mm) in Group 2, the difference being highly significant statistically (p <0.0001). The mean value of left ventricular ejection fraction was significantly lower in patients with AMI (Group 1 – 47.60% ± 7.96 vs. Group 2 – 51.23% ± 9.05, p = 0.04). EAT thickness values showed a weak but significant positive correlation with the level of total cholesterol (r = −0.22, p = 0.03) and with the value of end-systolic left ventricle diameter (r = 0.33, = 0.001).

Conclusions: The increased thickness of EAT was associated with other serum- or image-based biomarkers of disease severity, such as the left ventricular ejection fraction, end-systolic diameter of the left ventricle, and total cholesterol. Our results indicate that EAT is significantly higher in patients with acute coronary syndrome, proving that EAT could serve as a marker of vulnerability in cardiovascular diseases.

The Effect of Periplaque Fat on Coronary Plaque Vulnerability in Patients with Stable Coronary Artery Disease – a 128-multislice CT-based Study

Background: The role of periplaque fat (PPF), as a fragment of the total epicardial adipose tissue, measured in the vicinity of a target coronary lesion, more specifically within the close proximity of a vulnerable plaque, has yet to be evaluated.

The study aimed to evaluate the interrelation between PPF and coronary plaque vulnerability in patients with stable coronary artery disease (CAD). Secondary objective: evaluation of the relationship between the total pericardial fat and markers for plaque vulnerability.

Materials and methods: We prospectively enrolled 77 patients with stable CAD, who underwent 128-multislice computed tomography coronary angiography (CTCA), and who presented minimum one lesion with >50% stenosis. CTCA analysis included measurements of: total pericardial fat and PPF volumes, coronary plaque characteristics, markers for plaque vulnerability – positive remodeling (PR), low attenuation plaque (LAP), spotty calcifications (SC,) napkin ring sign (NRS). Study subjects were divided into two categories: Group 1 – 1 marker of plaque vulnerability (n = 36, 46.75%) and Group 2 – ≥1 marker of vulnerability (n = 41, 53.25%).

Results: The mean age of the population was 61.77 ± 11.28 years, and 41 (53.24%) were males. The analysis of plaque characteristics showed that Group 2 presented significantly longer plaques (16.26 ± 4.605 mm vs. 19.09 ± 5.227 mm, p = 0.02), remodeling index (0.96 ± 0.20 vs. 1.18 ± 0.33, p = 0.0009), and vessel volume (p = 0.027), and more voluminous plaques (147.5 ± 71.74 mm3 vs. 207.7 ± 108.9 mm3, p = 0.006) compared to Group 1. Group 2 presented larger volumes of PPF (512.2 ± 289.9 mm3 vs. 710.9 ± 361.9 mm3, p = 0.01) and of thoracic fat volume (1,616 ± 614.8 mm3 vs. 2,000 ± 850.9 mm3, p = 0.02), compared to Group 1, but no differences were found regarding the total pericardial fat (p = 0.49). Patients with 3 or 4 vulnerability markers (VM) presented significantly larges PPF volumes compared to those with 1 or 2 VM, respectively (p = 0.008). There was a significant positive correlation between PPF volume and the non-calcified (r = 0.474, 95% CI 0.2797–0.6311, p <0.0001), lipid-rich (r = 0.316, 95% CI 0.099–0.504, p = 0.005), and fibro-fatty (r = 0.452, 95% CI 0.2541–0.6142, p <0.0001) volumes. The total pericardial fat was significantly correlated only with the volume of lipid-rich plaques (p = 0.02).

Conclusions: Periplaque fat volume was associated with a higher degree of coronary plaque vulnerability. PPF was correlated with lipid-rich, fibro-fatty, and non-calcified plaque-related volumes, as markers for enhanced plaque vulnerability. PPF volume, assessed with native cardiac computed tomography, could become a novel marker for coronary plaque vulnerability.

Original research. The Assessment of Epicardial Adipose Tissue in Acute Coronary Syndrome Patients. A Systematic Review

Background: This systematic review seeks to evaluate the role of epicardial adipose tissue (EAT), quantified either by thickness, assessed by transthoracic echocardiography, or by volume, assessed by cardiac computed tomography (CT), in the follow-up of patients with acute coronary syndromes (ACS). Method: One-hundred forty-four articles were screened, from which 56 were reviewed in full-text. From those, 47 studies were excluded for the following reasons: they did not meet the inclusion criteria; they were either reviews or meta-analyses; the study cohorts included only stable coronary artery disease patients; they did not state a clear and concise study design, endpoints, or follow-up. The final draft included nine studies for systematic evaluation. Results: Of the 2,306 patients included in the review, 170 underwent cardiac CT while the remaining 2,136 underwent transthoracic echocardiography for the measurement of EAT. The analysis found that the EAT thickness was significantly associated with major adverse cardiovascular events (MACE) rates during hospitalization (OR: -1.3, 95% CI: 1.05-1.62, p = 0.020) and at three years (HR: 1.524, 95% CI: 1.0-2.2, p = 0.038). The included studies found that EAT was correlated with the following clinical and angiographic risk scores for ACS: GRACE (r = 0.438, p <0.001), TIMI risk score (r = 0.363, p = 0.001), SYNTAX score (r = 0.690, p <0.0001; r = 0.610, p <0.01), and Gensini score (r = 0.438, p = 0.001). There was an inverse correlation between ST-segment resolution of <70% after revascularization and EAT (r = −0.414, p = 0.01), and the myocardial blush grade (r = −0.549, p <0.001). The EF aggregation ranged between 2.65 mm and 4.7 mm within the included studies. Conclusions: EAT, evaluated either by echocardiography or cardiac CT, correlates with the severity of coronary lesions, with the clinical and angiographic risk scores for acute coronary syndromes, with indicators for coronary reperfusion, and with short- and long-term MACE rates. Further studies are required to fully elucidate the role of this extensively studied but still novel cardiovascular biomarker as part of a risk prediction tool.