Aims Increased attenuation of pericoronary adipose tissue (PCAT) around the proximal right coronary artery (RCA) from coronary computed tomography angiography (CTA) has been shown to be associated with coronary inflammation and improved prediction of cardiac death over plaque features. Our aim was to investigate whether PCAT CT attenuation is related to progression of coronary plaque burden. Methods and results We analysed CTA studies of 111 stable patients (age 59.2 ± 9.8 years, 77% male) who underwent sequential CTA (3.4 ± 1.6 years between scans) with identical acquisition protocols. Total plaque (TP), calcified plaque (CP), non-calcified plaque (NCP), and low-density non-calcified plaque (LD-NCP) volumes and corresponding burden (plaque volume × 100%/vessel volume) were quantified using semi-automated software. PCAT CT attenuation (HU) was measured around the proximal RCA, the most standardized method for PCAT analysis. Patients with an increase in NCP burden (n = 51) showed an increase in PCAT attenuation, whereas patients with a decrease in NCP burden (n = 60) showed a decrease {4.4 [95% confidence interval (CI) 2.6–6.2] vs. −2.78 (95% CI −4.6 to −1.0) HU, P < 0.0001}. Changes in PCAT attenuation correlated with changes in the burden of NCP (r = 0.55, P < 0.001) and LD-NCP (r = 0.24, P = 0.01); but not CP burden (P = 0.3). Increased baseline PCAT attenuation ≥−75 HU was independently associated with increase in NCP (odds ratio 3.07, 95% CI 1.4–7.0; P < 0.008) and TP burden on follow-up CTA. Conclusion PCAT attenuation measured from routine CTA is related to the progression of NCP and TP burden. This imaging biomarker may help to identify patients at increased risk of high-risk plaque progression and allow monitoring of beneficial changes from medical therapy.

Background: Epicardial adipose tissue (EAT) volume (cm 3 ) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of fully automated deep learning-based EAT volume and attenuation measurements quantified from noncontrast cardiac computed tomography. Methods: Our study included 2068 asymptomatic subjects (56±9 years, 59% male) from the EISNER trial (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) with long-term follow-up after coronary artery calcium measurement. EAT volume and mean attenuation were quantified using automated deep learning software from noncontrast cardiac computed tomography. MACE was defined as myocardial infarction, late (>180 days) revascularization, and cardiac death. EAT measures were compared to coronary artery calcium score and atherosclerotic cardiovascular disease risk score for MACE prediction. Results: At 14±3 years, 223 subjects suffered MACE. Increased EAT volume and decreased EAT attenuation were both independently associated with MACE. Atherosclerotic cardiovascular disease risk score, coronary artery calcium, and EAT volume were associated with increased risk of MACE (hazard ratio [95%CI]: 1.03 [1.01–1.04]; 1.25 [1.19–1.30]; and 1.35 [1.07–1.68], P <0.01 for all) and EAT attenuation was inversely associated with MACE (hazard ratio, 0.83 [95% CI, 0.72–0.96]; P =0.01), with corresponding Harrell C statistic of 0.76. MACE risk progressively increased with EAT volume ≥113 cm 3 and coronary artery calcium ≥100 AU and was highest in subjects with both ( P <0.02 for all). In 1317 subjects, EAT volume was correlated with inflammatory biomarkers C-reactive protein, myeloperoxidase, and adiponectin reduction; EAT attenuation was inversely related to these biomarkers. Conclusions: Fully automated EAT volume and attenuation quantification by deep learning from noncontrast cardiac computed tomography can provide prognostic value for the asymptomatic patient, without additional imaging or physician interaction.

To evaluate the performance of deep learning for robust and fully automated quantification of epicardial adipose tissue (EAT) from multicenter cardiac CT data. Materials and Methods:In this multicenter study, a convolutional neural network approach was trained to quantify EAT on non-contrast material-enhanced calcium-scoring CT scans from multiple cohorts, scanners, and protocols (n = 850). Deep learning performance was compared with the performance of three expert readers and with interobserver variability in a subset of 141 scans. The deep learning algorithm was incorporated into research software. Automated EAT progression was compared with expert measurements for 70 patients with baseline and follow-up scans.Results: Automated quantification was performed in a mean (6 standard deviation) time of 1.57 seconds 6 0.49, compared with 15 minutes for experts. Deep learning provided high agreement with expert manual quantification for all scans (R = 0.974; P , .001), with no significant bias (0.53 cm 3 ; P = .13). Manual EAT volumes measured by two experienced readers were highly correlated (R = 0.984; P , .001) but with a bias of 4.35 cm 3 (P , .001). Deep learning quantifications were highly correlated with the measurements of both experts (R = 0.973 and R = 0.979; P , .001), with significant bias for reader 1 (5.11 cm 3 ; P , .001) but not for reader 2 (0.88 cm 3 ; P = .26). EAT progression by deep learning correlated strongly with manual EAT progression (R = 0.905; P , .001) in 70 patients, with no significant bias (0.64 cm 3 ; P = .43), and was related to an increased noncalcified plaque burden quantified from coronary CT angiography (5.7% vs 1.8%; P = .026). Conclusion:Deep learning allows rapid, robust, and fully automated quantification of EAT from calcium scoring CT. It performs as well as an expert reader and can be implemented for routine cardiovascular risk assessment.

We sought to examine the association of epicardial adipose tissue (EAT) quantified on chest computed tomography (CT) with the extent of pneumonia and adverse outcomes in patients with coronavirus disease 2019 (COVID-19). Methods: We performed a post-hoc analysis of a prospective international registry comprising 109 consecutive patients (age 64 ± 16 years; 62% male) with laboratory-confirmed COVID-19 and noncontrast chest CT imaging. Using semi-automated software, we quantified the burden (%) of lung abnormalities associated with COVID-19 pneumonia. EAT volume (mL) and attenuation (Hounsfield units) were measured using deep learning software. The primary outcome was clinical deterioration (intensive care unit admission, invasive mechanical ventilation, or vasopressor therapy) or in-hospital death. Results: In multivariable linear regression analysis adjusted for patient comorbidities, the total burden of COVID-19 pneumonia was associated with EAT volume (β = 10.6, p = 0.005) and EAT attenuation (β = 5.2, p = 0.004). EAT volume correlated with serum levels of lactate dehydrogenase (r = 0.361, p = 0.001) and C-reactive protein (r = 0.450, p < 0.001). Clinical deterioration or death occurred in 23 (21.1%) patients at a median of 3 days (IQR 1-13 days) following the chest CT. In multivariable logistic regression analysis, EAT volume (OR 5.1 [95% CI 1.8-14.1] per doubling p = 0.011) and EAT attenuation (OR 3.4 [95% CI 1.5-7.5] per 5 Hounsfield unit increase, p = 0.003) were independent predictors of clinical deterioration or death, as was total pneumonia burden (OR 2.5, 95% CI 1.4-4.6, p = 0.002), chronic lung disease (OR 1.3 [95% CI 1.1-1.7], p = 0.011), and history of heart failure (OR 3.5 [95% 1.1-8.2], p = 0.037). Conclusions: EAT measures quantified from chest CT are independently associated with extent of pneumonia and adverse outcomes in patients with COVID-19, lending support to their use in clinical risk stratification.

Aims Our aim was to evaluate the performance of machine learning (ML), integrating clinical parameters with coronary artery calcium (CAC), and automated epicardial adipose tissue (EAT) quantification, for the prediction of long-term risk of myocardial infarction (MI) and cardiac death in asymptomatic subjects. Methods and results Our study included 1912 asymptomatic subjects [1117 (58.4%) male, age: 55.8 ± 9.1 years] from the prospective EISNER trial with long-term follow-up after CAC scoring. EAT volume and density were quantified using a fully automated deep learning method. ML extreme gradient boosting was trained using clinical co-variates, plasma lipid panel measurements, risk factors, CAC, aortic calcium, and automated EAT measures, and validated using repeated 10-fold cross validation. During mean follow-up of 14.5 ± 2 years, 76 events of MI and/or cardiac death occurred. ML obtained a significantly higher AUC than atherosclerotic cardiovascular disease (ASCVD) risk and CAC score for predicting events (ML: 0.82; ASCVD: 0.77; CAC: 0.77, P &lt; 0.05 for all). Subjects with a higher ML score (by Youden’s index) had high hazard of suffering events (HR: 10.38, P &lt; 0.001); the relationships persisted in multivariable analysis including ASCVD-risk and CAC measures (HR: 2.94, P = 0.005). Age, ASCVD-risk, and CAC were prognostically important for both genders. Systolic blood pressure was more important than cholesterol in women, and the opposite in men. Conclusions In this prospective study, machine learning used to integrate clinical and quantitative imaging-based variables significantly improves prediction of MI and cardiac death compared with standard clinical risk assessment. Following further validation, such a personalized paradigm could potentially be used to improve cardiovascular risk assessment.