Association between single-slice and whole heart measurements of epicardial and pericardial fat in cardiac MRI

Vach, Marius; Luetkens, Julian A.; Faron, Anton; Isaak, Alexander; Salam, Babak; Thomas, Daniel; Attenberger, Ulrike; Sprinkart, Alois M.

doi:10.1177/02841851211054192

Cited by 4 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The relative localization of EPAT surrounding the heart may carry added significance. However, single-slice measurements of EAT and PAT have been found to be highly correlated with their volumes . Third, the accuracy of EPAT segmentation was not perfect.…”

Section: Discussionmentioning

confidence: 99%

Cardiovascular Significance and Genetics of Epicardial and Pericardial Adiposity

Rämö,

Kany,

Hou

et al. 2024

JAMA Cardiol

View full text Add to dashboard Cite

ImportanceEpicardial and pericardial adipose tissue (EPAT) has been associated with cardiovascular diseases such as atrial fibrillation or flutter (AF) and coronary artery disease (CAD), but studies have been limited in sample size or drawn from selected populations. It has been suggested that the association between EPAT and cardiovascular disease could be mediated by local or paracrine effects.ObjectiveTo evaluate the association of EPAT with prevalent and incident cardiovascular disease and to elucidate the genetic basis of EPAT in a large population cohort.Design, Setting, and ParticipantsA deep learning model was trained to quantify EPAT area from 4-chamber magnetic resonance images using semantic segmentation. Cross-sectional and prospective cardiovascular disease associations were evaluated, controlling for sex and age. Prospective associations were additionally controlled for abdominal visceral adipose tissue (VAT) volumes. A genome-wide association study was performed, and a polygenic score (PGS) for EPAT was examined in independent FinnGen cohort study participants. Data analyses were conducted from March 2022 to December 2023.ExposuresThe primary exposures were magnetic resonance imaging–derived continuous measurements of epicardial and pericardial adipose tissue area and visceral adipose tissue volume.Main Outcomes and MeasuresPrevalent and incident CAD, AF, heart failure (HF), stroke, and type 2 diabetes (T2D).ResultsAfter exclusions, this study included 44 475 participants (mean [SD] age, 64.1 [7.7] years; 22 972 female [51.7%]) from the UK Biobank. Cross-sectional and prospective cardiovascular disease associations were evaluated for a mean (SD) of 3.2 (1.5) years of follow-up. Prospective associations were additionally controlled for abdominal VAT volumes for 38 527 participants. A PGS for EPAT was examined in 453 733 independent FinnGen cohort study participants. EPAT was positively associated with male sex (β = +0.78 SD in EPAT; P &lt; 3 × 10−324), age (Pearson r = 0.15; P = 9.3 × 10−229), body mass index (Pearson r = 0.47; P &lt; 3 × 10−324), and VAT (Pearson r = 0.72; P &lt; 3 × 10−324). EPAT was more elevated in prevalent HF (β = +0.46 SD units) and T2D (β = +0.56) than in CAD (β = +0.23) or AF (β = +0.18). EPAT was associated with incident HF (hazard ratio [HR], 1.29 per +1 SD in EPAT; 95% CI, 1.17-1.43), T2D (HR, 1.63; 95% CI, 1.51-1.76), and CAD (HR, 1.19; 95% CI, 1.11-1.28). However, the associations were no longer significant when controlling for VAT. Seven genetic loci were identified for EPAT, implicating transcriptional regulators of adipocyte morphology and brown adipogenesis (EBF1, EBF2, and CEBPA) and regulators of visceral adiposity (WARS2 and TRIB2). The EPAT PGS was associated with T2D (odds ratio [OR], 1.06; 95% CI, 1.05-1.07; P =3.6 × 10−44), HF (OR, 1.05; 95% CI, 1.04-1.06; P =4.8 × 10−15), CAD (OR, 1.04; 95% CI, 1.03-1.05; P =1.4 × 10−17), AF (OR, 1.04; 95% CI, 1.03-1.06; P =7.6 × 10−12), and stroke in FinnGen (OR, 1.02; 95% CI, 1.01-1.03; P =3.5 × 10−3) per 1 SD in PGS.Conclusions and RelevanceResults of this cohort study suggest that epicardial and pericardial adiposity was associated with incident cardiovascular diseases, but this may largely reflect a metabolically unhealthy adiposity phenotype similar to abdominal visceral adiposity.

show abstract

Section: Discussionmentioning

confidence: 99%

Cardiovascular Significance and Genetics of Epicardial and Pericardial Adiposity

Rämö,

Kany,

Hou

et al. 2024

JAMA Cardiol

View full text Add to dashboard Cite

show abstract

“…A practical limitation of previous studies performing CT-derived body composition analysis was the considerable time required to segment multiple slices, making it difficult to implement in daily clinical practice. In a recent study by Vach et al, 27 based on imaging data from 113 individuals (79 ill, 34 healthy) at 5 different anatomic landmarks, strong correlations were found between the characteristics of cardiac adipose tissue (CAT) measured from single-slice images and the composition of the entire corresponding fat compartment. Therefore, we decided in our study to perform the quantitative and qualitative CAT analysis using CT single-slice measurements.…”

Section: Discussionmentioning

confidence: 99%

“…Only arterial phase scans with 120 kVp were considered for body composition analysis to prevent any potential bias due to the contrast phase or tube voltage. Adipose tissue areas collected from single axial images at the levels of the aortic valve (AV) have been shown to correlate strongly with the total volumes of the corresponding adipose tissue depots (CAT, EAT, and PAT) and were therefore used for image analysis 27,28 . An experienced cardiovascular radiologist, who was blinded to the patients’ clinical features, identified the AV anatomical landmark, and performed subsequent CAT, EAT, and PAT analysis.…”

Section: Methodsmentioning

confidence: 99%

“…Adipose tissue areas collected from single axial images at the levels of the aortic valve (AV) have been shown to correlate strongly with the total volumes of the corresponding adipose tissue depots (CAT, EAT, and PAT) and were therefore used for image analysis. 27,28 An experienced cardiovascular radiologist, who was blinded to the patients' clinical features, identified the AV anatomical landmark, and performed subsequent CAT, EAT, and PAT analysis. For each patient, single-slice images at this level were retrieved from the local image archiving and communication system and transferred to a conventional workstation.…”

Section: Image Analysismentioning

confidence: 99%

See 1 more Smart Citation

CT-derived Epicardial Adipose Tissue Inflammation Predicts Outcome in Patients Undergoing Transcatheter Aortic Valve Replacement

Salam,

Al-Kassou,

Weinhold

et al. 2024

Journal of Thoracic Imaging

Self Cite

View full text Add to dashboard Cite

Purpose: Inflammatory changes in epicardial (EAT) and pericardial adipose tissue (PAT) are associated with increased overall cardiovascular risk. Using routine, preinterventional cardiac CT data, we examined the predictive value of quantity and quality of EAT and PAT for outcome after transcatheter aortic valve replacement (TAVR). Materials and Methods: Cardiac CT data of 1197 patients who underwent TAVR at the in-house heart center between 2011 and 2020 were retrospectively analyzed. The amount and density of EAT and PAT were quantified from single-slice CT images at the level of the aortic valve. Using established risk scores and known independent risk factors, a clinical benchmark model (BMI, Chronic kidney disease stage, EuroSCORE 2, STS Prom, year of intervention) for outcome prediction (2-year mortality) after TAVR was established. Subsequently, we tested whether the additional inclusion of area and density values of EAT and PAT in the clinical benchmark model improved prediction. For this purpose, the cohort was divided into a training (n=798) and a test cohort (n=399). Results: Within the 2-year follow-up, 264 patients died. In the training cohort, particularly the addition of EAT density to the clinical benchmark model showed a significant association with outcome (hazard ratio 1.04, 95% CI: 1.01-1.07; P=0.013). In the test cohort, the outcome prediction of the clinical benchmark model was also significantly improved with the inclusion of EAT density (c-statistic: 0.589 vs. 0.628; P=0.026). Conclusions: EAT density as a surrogate marker of EAT inflammation was associated with 2-year mortality after TAVR and may improve outcome prediction independent of established risk parameters.

show abstract

Deep learning for opportunistic, end-to-end automated assessment of epicardial adipose tissue in pre-interventional, ECG-gated spiral computed tomography

Theis,

Garajová,

Salam

et al. 2024

Insights Imaging

View full text Add to dashboard Cite

Objectives Recently, epicardial adipose tissue (EAT) assessed by CT was identified as an independent mortality predictor in patients with various cardiac diseases. Our goal was to develop a deep learning pipeline for robust automatic EAT assessment in CT. Methods Contrast-enhanced ECG-gated cardiac and thoraco-abdominal spiral CT imaging from 1502 patients undergoing transcatheter aortic valve replacement (TAVR) was included. Slice selection at aortic valve (AV)-level and EAT segmentation were performed manually as ground truth. For slice extraction, two approaches were compared: A regression model with a 2D convolutional neural network (CNN) and a 3D CNN utilizing reinforcement learning (RL). Performance evaluation was based on mean absolute z-deviation to the manually selected AV-level (Δz). For tissue segmentation, a 2D U-Net was trained on single-slice images at AV-level and compared to the open-source body and organ analysis (BOA) framework using Dice score. Superior methods were selected for end-to-end evaluation, where mean absolute difference (MAD) of EAT area and tissue density were compared. 95% confidence intervals (CI) were assessed for all metrics. Results Slice extraction using RL was slightly more precise (Δz: RL 1.8 mm (95% CI: [1.6, 2.0]), 2D CNN 2.0 mm (95% CI: [1.8, 2.3])). For EAT segmentation at AV-level, the 2D U-Net outperformed BOA significantly (Dice score: 2D U-Net 91.3% (95% CI: [90.7, 91.8]), BOA 85.6% (95% CI: [84.7, 86.5])). The end-to-end evaluation revealed high agreement between automatic and manual measurements of EAT (MAD area: 1.1 cm2 (95% CI: [1.0, 1.3]), MAD density: 2.2 Hounsfield units (95% CI: [2.0, 2.5])). Conclusions We propose a method for robust automatic EAT assessment in spiral CT scans enabling opportunistic evaluation in clinical routine. Critical relevance statement Since inflammatory changes in epicardial adipose tissue (EAT) are associated with an increased risk of cardiac diseases, automated evaluation can serve as a basis for developing automated cardiac risk assessment tools, which are essential for efficient, large-scale assessment in opportunistic settings. Key Points Deep learning methods for automatic assessment of epicardial adipose tissue (EAT) have great potential. A 2-step approach with slice extraction and tissue segmentation enables robust automated evaluation of EAT. End-to-end automation enables large-scale research on the value of EAT for outcome analysis. Graphical Abstract

show abstract

Association between single-slice and whole heart measurements of epicardial and pericardial fat in cardiac MRI

Cited by 4 publications

References 32 publications

Cardiovascular Significance and Genetics of Epicardial and Pericardial Adiposity

Cardiovascular Significance and Genetics of Epicardial and Pericardial Adiposity

CT-derived Epicardial Adipose Tissue Inflammation Predicts Outcome in Patients Undergoing Transcatheter Aortic Valve Replacement

Deep learning for opportunistic, end-to-end automated assessment of epicardial adipose tissue in pre-interventional, ECG-gated spiral computed tomography

Contact Info

Product

Resources

About