Purpose: The aim of our work was to systematically review and meta-analyze epicardial adipose tissue (EAT) density values reported in literature, assessing potential correlations of EAT density with segmentation thresholds and other technical and clinical variables. Method: A systematic search was performed, aiming for papers reporting global EAT density values in Hounsfield Units (HU) in patients undergoing chest CT for any clinical indication. After screening titles, abstract and full text of each retrieved work, studies reporting mean and standard deviation for EAT density were ultimately included. Technical, clinical and EAT data were extracted, and divided into subgroups according to clinical conditions of reported subjects. Pooled density analyses were performed both overall and for subgroups according to clinical conditions. Metaregression analyses were done to appraise the impact of clinical and technical variables on EAT volume. Results: Out of 152 initially retrieved works, 13 were ultimately included, totaling for 7683 subjects. EAT density showed an overall pooled value of -85.86 HU (95% confidence interval [95% CI] -91.84, -79.89 HU), being -86.40 HU (95% CI -112.69, -60.12 HU) in healthy subjects and -80.71 HU (95% CI -87.43, -73.99 HU) in patients with coronary artery disease. EAT volume and lower and higher segmentation thresholds were found to be significantly correlated with EAT density (p = 0.044, p < 0.001 and p< 0.001 respectively). Conclusions: Patients with coronary artery disease appear to present with higher EAT density values, while the correlations observed at metaregression highlight the need for well-established, shared thresholds for EAT segmentation.

CT-derived epicardial adipose tissue density: Systematic review and meta-analysis / C.B. Monti, D. Capra, M. Zanardo, G. Guarnieri, S. Schiaffino, F. Secchi, F. Sardanelli. - In: EUROPEAN JOURNAL OF RADIOLOGY. - ISSN 0720-048X. - 143:(2021). [Epub ahead of print] [10.1016/j.ejrad.2021.109902]

CT-derived epicardial adipose tissue density: Systematic review and meta-analysis

C.B. Monti
Co-primo
;
D. Capra
Co-primo
;
M. Zanardo
Secondo
;
G. Guarnieri;F. Secchi;F. Sardanelli
2021

Abstract

Purpose: The aim of our work was to systematically review and meta-analyze epicardial adipose tissue (EAT) density values reported in literature, assessing potential correlations of EAT density with segmentation thresholds and other technical and clinical variables. Method: A systematic search was performed, aiming for papers reporting global EAT density values in Hounsfield Units (HU) in patients undergoing chest CT for any clinical indication. After screening titles, abstract and full text of each retrieved work, studies reporting mean and standard deviation for EAT density were ultimately included. Technical, clinical and EAT data were extracted, and divided into subgroups according to clinical conditions of reported subjects. Pooled density analyses were performed both overall and for subgroups according to clinical conditions. Metaregression analyses were done to appraise the impact of clinical and technical variables on EAT volume. Results: Out of 152 initially retrieved works, 13 were ultimately included, totaling for 7683 subjects. EAT density showed an overall pooled value of -85.86 HU (95% confidence interval [95% CI] -91.84, -79.89 HU), being -86.40 HU (95% CI -112.69, -60.12 HU) in healthy subjects and -80.71 HU (95% CI -87.43, -73.99 HU) in patients with coronary artery disease. EAT volume and lower and higher segmentation thresholds were found to be significantly correlated with EAT density (p = 0.044, p < 0.001 and p< 0.001 respectively). Conclusions: Patients with coronary artery disease appear to present with higher EAT density values, while the correlations observed at metaregression highlight the need for well-established, shared thresholds for EAT segmentation.
Adipose Tissue; Coronary Artery Disease; Meta-Analysis; Tomography X-Ray Computed
Settore MED/36 - Diagnostica per Immagini e Radioterapia
Settore MED/50 - Scienze Tecniche Mediche Applicate
2021
13-ago-2021
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/866823
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