ABSTRACT
Background: Epicardial adipose tissue (EAT) is unique type of visceral adipose tissue, sharing the same microcirculation with myocardium. This study aimed to assess the imaging features of EAT in patients with acute myocarditis (AM) and explore the relationships with clinical characteristics. Methods: For this retrospective case-control study, totally 38 AM patients and 52 controls were screened retrospectively from January 2019 to December 2022, and the EAT volume was measured from coronary computed tomography (CT) angiography imaging. Histogram analysis was performed to calculate parameters like the mean, standard deviation, interquartile range and percentiles of EAT attenuation. Whether EAT features change was assessed when clinical characteristics including symptoms, T wave abnormalities, pericardial effusion (PE), impairment of systolic function, and the need for intensive care presented. Results: The EAT volume (75.2±22.8 mL) and mean EAT attenuation [-75.8±4.4 Hounsfield units (HU)] of the AM group was significantly larger than the control group (64.7±26.0 mL, P=0.049; -77.9±5.0 HU, P=0.044). Among the clinical characteristics, only the presence of PE was associated with changes in EAT features. Patients with PE showed significantly changes in EAT attenuation including mean attenuation [analysis of variance (ANOVA) P=0.001] and quantitative histogram parameters. The mean attenuation of patients with PE (-71.9±4.0 HU) was significantly larger than controls (-77.9±5.0 HU, Bonferroni corrected P<0.001) and patients without PE (-77.4±3.5 HU, Bonferroni corrected P=0.003). Observed in histogram, the overall increase in EAT attenuation could lead to decrease in EAT volume, which resulted in no statistically significant difference in EAT volume between the AM patients with PE and controls (64.7±26.0 vs. 72.2±28.3 mL, Bonferroni corrected P>0.99). Conclusions: Compared to controls, EAT volume was significantly larger in AM, and EAT attenuation increased notably in the presence of PE. We recommend evaluating EAT volume and attenuation simultaneously when quantifying EAT using CT attenuation thresholds.
ABSTRACT
OBJECTIVES: To compare the diagnostic performance of machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and cardiac magnetic resonance (MR) perfusion mapping for functional assessment of coronary stenosis. METHODS: Between October 2020 and March 2022, consecutive participants with stable coronary artery disease (CAD) were prospectively enrolled and underwent coronary CTA, cardiac MR, and invasive fractional flow reserve (FFR) within 2 weeks. Cardiac MR perfusion analysis was quantified by stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Hemodynamically significant stenosis was defined as FFR ≤ 0.8 or > 90% stenosis on invasive coronary angiography (ICA). The diagnostic performance of CT-FFR, MBF, and MPR was compared, using invasive FFR as a reference. RESULTS: The study protocol was completed in 110 participants (mean age, 62 years ± 8; 73 men), and hemodynamically significant stenosis was detected in 36 (33%). Among the quantitative perfusion indices, MPR had the largest area under receiver operating characteristic curve (AUC) (0.90) for identifying hemodynamically significant stenosis, which is in comparison with ML-based CT-FFR on the vessel level (AUC 0.89, p = 0.71), with comparable sensitivity (89% vs 79%, p = 0.20), specificity (87% vs 84%, p = 0.48), and accuracy (88% vs 83%, p = 0.24). However, MPR outperformed ML-based CT-FFR on the patient level (AUC 0.96 vs 0.86, p = 0.03), with improved specificity (95% vs 82%, p = 0.01) and accuracy (95% vs 81%, p < 0.01). CONCLUSION: ML-based CT-FFR and quantitative cardiac MR showed comparable diagnostic performance in detecting vessel-specific hemodynamically significant stenosis, whereas quantitative perfusion mapping had a favorable performance in per-patient analysis. CLINICAL RELEVANCE STATEMENT: ML-based CT-FFR and MPR derived from cardiac MR performed well in diagnosing vessel-specific hemodynamically significant stenosis, both of which showed no statistical discrepancy with each other. KEY POINTS: ⢠Both machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and quantitative perfusion cardiac MR performed well in the detection of hemodynamically significant stenosis. ⢠Compared with stress myocardial blood flow (MBF) from quantitative perfusion cardiac MR, myocardial perfusion reserve (MPR) provided higher diagnostic performance for detecting hemodynamically significant coronary artery stenosis. ⢠ML-based CT-FFR and MPR from quantitative cardiac MR perfusion yielded similar diagnostic performance in assessing vessel-specific hemodynamically significant stenosis, whereas MPR had a favorable performance in per-patient analysis.