A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans

ABSTRACTBackgroundThis study investigated accuracy and consistency of epicardial adipose tissue (EAT) quantification in chest computed tomography (CT) scans.Methods and resultsEAT volume was quantified semi-automatically using a standard Hounsfield unit threshold (-190U, -30) in three independent cohorts: (1) Cohort 1 (N = 30) consisted of paired 120 KV cardiac non-contrast CT (NCCT) and 120 KV chest NCCT; (2) Cohort 2 (N = 20) consisted of paired 120 KV cardiac NCCT and 100 KV chest NCCT; (3) Cohort 3 (N = 20) consisted of paired chest NCCT and chest contrast-enhanced CT (CECT) datasets. Images were reconstructed with the slice thicknesses of 1.25 mm and 5 mm in the chest CT datasets, and 3 mm in the cardiac NCCT datasets. In Cohort 1, the chest NCCT-1.25 mm EAT volume was similar to the cardiac NCCT EAT volume, whilst chest NCCT-5 mm underestimated the EAT volume by 7.0%. In Cohort 2, 100 KV chest NCCT-1.25mm and -5 mm EAT volumes were 9.7% and 6.4% larger than corresponding 120 KV cardiac NCCT EAT volumes. In Cohort 3, the chest CECT dataset underestimated EAT volumes by ∼25%, relative to chest NCCT datasets. All chest CT-derived EAT volumes were strongly correlated with their cardiac CT counterparts.ConclusionsThe chest NCCT-1.25 mm EAT volume with the 120 KV tube energy produced EAT volumes that are comparable to cardiac NCCT. All chest CT EAT volumes were strongly correlated with EAT volumes obtained from cardiac CT, if imaging protocol is consistently applied to all participants.

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Quantification of epicardial adipose tissue by cardiac CT: Influence of acquisition parameters and contrast enhancement

European Journal of Radiology ◽

10.1016/j.ejrad.2019.108732 ◽

2019 ◽

Vol 121 ◽

pp. 108732 ◽

Cited By ~ 2

Author(s):

Mohamed Marwan ◽

Susanna Koenig ◽

Kirsten Schreiber ◽

Fabian Ammon ◽

Markus Goeller ◽

...

Keyword(s):

Adipose Tissue ◽

Contrast Enhancement ◽

Cardiac Ct ◽

Epicardial Adipose Tissue

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Epicardial adipose tissue and severe Coronavirus Disease 19

Cardiovascular Diabetology ◽

10.1186/s12933-021-01329-z ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Hélène Bihan ◽

Richard Heidar ◽

Aude Beloeuvre ◽

Lucie Allard ◽

Elise Ouedraogo ◽

...

Keyword(s):

Adipose Tissue ◽

Epicardial Adipose Tissue ◽

Ct Scans ◽

Lung Involvement ◽

Icu Admission ◽

Anthropometric Characteristics ◽

Automatic Software ◽

Risk Of Disease ◽

Key Indicators ◽

Visceral Adipose

Abstract Background Both visceral adipose tissue and epicardial adipose tissue (EAT) have pro-inflammatory properties. The former is associated with Coronavirus Disease 19 (COVID-19) severity. We aimed to investigate whether an association also exists for EAT. Material and methods We retrospectively measured EAT volume using computed tomography (CT) scans (semi-automatic software) of inpatients with COVID-19 and analyzed the correlation between EAT volume and anthropometric characteristics and comorbidities. We then analyzed the clinicobiological and radiological parameters associated with severe COVID-19 (O2 $$\ge$$ ≥ 6 l/min), intensive care unit (ICU) admission or death, and 25% or more CT lung involvement, which are three key indicators of COVID-19 severity. Results We included 100 consecutive patients; 63% were men, mean age was 61.8 ± 16.2 years, 47% were obese, 54% had hypertension, 42% diabetes, and 17.2% a cardiovascular event history. Severe COVID-19 (n = 35, 35%) was associated with EAT volume (132 ± 62 vs 104 ± 40 cm3, p = 0.02), age, ferritinemia, and 25% or more CT lung involvement. ICU admission or death (n = 14, 14%) was associated with EAT volume (153 ± 67 vs 108 ± 45 cm3, p = 0.015), hypertension and 25% or more CT lung involvement. The association between EAT volume and severe COVID-19 remained after adjustment for sex, BMI, ferritinemia and lung involvement, but not after adjustment for age. Instead, the association between EAT volume and ICU admission or death remained after adjustment for all five of these parameters. Conclusions Our results suggest that measuring EAT volume on chest CT scans at hospital admission in patients diagnosed with COVID-19 might help to assess the risk of disease aggravation.

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Epicardial adipose tissue and atrial fibrillation: guilty as charged or guilty by association?

European Heart Journal ◽

10.1093/ehjci/ehaa946.0468 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

J Sousa ◽

D Matos ◽

A Ferreira ◽

J Abecasis ◽

C Saraiva ◽

...

Keyword(s):

Risk Factors ◽

Atrial Fibrillation ◽

Adipose Tissue ◽

Cardiovascular Risk ◽

Cardiac Ct ◽

Epicardial Adipose Tissue ◽

Surrogate Marker ◽

Epicardial Fat ◽

The Relationship ◽

Af Recurrence

Abstract Background Epicardial adipose tissue (EAT) has been linked to the presence and burden of atrial fibrillation (AF). However, it is still unclear whether this relationship is causal or simply a surrogate marker of other risk factors commonly associated with AF. Purpose The purpose of this study was to assess the relationship between these factors and EAT, and to compare their performance in predicting AF recurrence after an ablation procedure. Methods We assessed 575 consecutive patients (mean age 61±11 years, 62% male) undergoing AF ablation preceded by cardiac CT in a high-volume ablation center. EAT was measured on cardiac CT using a modified simplified method. Patients were divided into 2 groups (above vs. below the median EAT volume). Cox regression was used to assess the relationship between epicardial fat, risk factors, and AF relapse. Results Patients with above-median EAT volume were older (p<0.001), more often male (OR 1.7, p=0.002), had higher body mass index, and higher prevalence of smoking, hypertension, diabetes and dyslipidemia (p<0.05). Non-paroxysmal AF was also more common in those with above-median EAT volume. During a median follow-up of 18 months, 232 patients (40.3%) suffered AF recurrence. After adjustment for BMI and other univariate predictors of relapse, three variables emerged independently associated with time to AF recurrence: non-paroxysmal AF (HR 2.1, 95% CI: 1.5–2.7, p<0.001), indexed left atrial (LA) volume (HR 1.006 per mL/m2, 95% CI: 1.002–1.011, p<0.001), and indexed epicardial fat volume (HR 1.87 per mL/m2, 95% CI: 1.66–2.1, p<0.001). None of the classic cardiovascular risk factors were an independent predictor of AF recurrence (all p>0.10). Conclusion Classic cardiovascular risk factors are more prevalent in patients with higher amounts of epicardial fat. However, unlike these risk factors, EAT is a powerful predictor of AF recurrence after ablation. These findings suggest that EAT is not merely a surrogate marker, but an important participant in the pathophysiology of AF. EAT, cvrf and AF burden Funding Acknowledgement Type of funding source: None

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Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network

10.1109/elmar52657.2021.9550936 ◽

2021 ◽

Author(s):

Marin Bencevic ◽

Marija Habijan ◽

Irena Galic

Keyword(s):

Neural Network ◽

Adipose Tissue ◽

Epicardial Adipose Tissue ◽

Ct Images ◽

Tissue Segmentation

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Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies

Scientific Reports ◽

10.1038/s41598-021-03150-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

David Molnar ◽

Olof Enqvist ◽

Johannes Ulén ◽

Måns Larsson ◽

John Brandberg ◽

...

Keyword(s):

Adipose Tissue ◽

Large Scale ◽

Cardiometabolic Risk ◽

Cardiac Ct ◽

Population Studies ◽

Epicardial Adipose Tissue ◽

Anthropometric Measures ◽

Automatic Quantification ◽

Scale Population ◽

Fully Automatic

AbstractTo develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardiac CT images from the SCAPIS study were used to train and test a convolutional neural network based model to quantify EAT by: segmenting the pericardium, suppressing noise-induced artifacts in the heart chambers, and, if image sets were incomplete, imputing missing EAT volumes. The model achieved a mean Dice coefficient of 0.90 when tested against expert manual segmentations on 25 image sets. Tested on 1400 image sets, the model successfully segmented 99.4% of the cases. Automatic imputation of missing EAT volumes had an error of less than 3.1% with up to 20% of the slices in image sets missing. The most important predictors of EAT volumes were weight and waist, while EAT attenuation was predicted mainly by EAT volume. A model with excellent performance, capable of fully automatic handling of the most common challenges in large scale EAT quantification has been developed. In studies of the importance of EAT in disease development, the strong co-variation with anthropometric measures needs to be carefully considered.

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Feasibility of epicardial adipose tissue quantification in non-ECG-gated low-radiation-dose CT: comparison with prospectively ECG-gated cardiac CT

Acta Radiologica ◽

10.1258/ar.2012.110537 ◽

2012 ◽

Vol 53 (5) ◽

pp. 536-540 ◽

Cited By ~ 4

Author(s):

Isabel Simon-Yarza ◽

Guillermo Viteri-Ramírez ◽

Ramón Saiz-Mendiguren ◽

Pedro J Slon-Roblero ◽

María Paramo ◽

...

Keyword(s):

Adipose Tissue ◽

Radiation Dose ◽

Cardiac Ct ◽

Epicardial Adipose Tissue ◽

Low Radiation Dose ◽

Tissue Quantification

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Potential role of CT-radiomics of epicardial adipose tissue in the prognosis of atrial fibrillation catheter ablation outcome

European Heart Journal - Cardiovascular Imaging ◽

10.1093/ehjci/jeab111.010 ◽

2021 ◽

Vol 22 (Supplement_3) ◽

Author(s):

J Ilyushenkova ◽

AE Shelemekhov ◽

EV Popov ◽

SI Sazonova ◽

RE Batalov ◽

...

Keyword(s):

Atrial Fibrillation ◽

Adipose Tissue ◽

Catheter Ablation ◽

Epicardial Adipose Tissue ◽

Public Institution ◽

Slice Thickness ◽

Tissue Segmentation ◽

Group 2 ◽

Af Recurrence ◽

Group 1

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): None Previous studies have shown that an increase of epicardial adipose tissue (EAT) volume is an independent risk factor of atrial fibrillation (AF) occurrence. However, there is no reliable data about the relationship between EAT and AF recurrence after catheter ablation (CA). Also, there are no studies of the possibility of using of CT radiomics of EAT, in particular of the quantitative assessment of EAT textural changes, for prognosis of CA outcomes in patients with AF. Thus, the aim of the present study was to estimate the association of CT-radiomics features of EAT with probability of AF recurrence after catheter ablation. Materials and Methods The prospective research included 46 patients (42 males and 4 females, mean age 42.4 ± 9.36) with drug-refractory lone AF referred for catheter ablation (CA). Before CA all patients underwent multislice CT-angiography for preoperative evaluation of cardiac and vessels anatomy and volumes. Images were acquired using a 64-detector CT scanner (GE Discovery NM/CT 570c, GE Healthcare, Milwaukee, WI, USA). Imaging parameters included a gantry rotation time of 400 ms, tube voltage of 120 mA, slice thickness 1.25 mm. For evaluation of EAT only native images (contrast-free scans) without ECG synchronization were analyzed. Epicardial adipose tissue segmentation was performed by 3D-Sliser software and the SliserRadiomics module (version 4.10.2). From CT images we quantified EAT volume and 93 radiomic features, including subgroups of first-order statistics, GLCM, GLDM, GLRLM, GLSZM and NGTDM parameters. All patients were followed-up prospectively for 12 months after the CA. A blanking period of 3 months was applied. The criteria of AF recurrence were AF episodes of more than 30 sec duration. Results. Recurrence of AF was registered in 26 patients. After the end of the follow-up, we divided study population on those with (Group 1) and without (Group 2) AF recurrence. EAT volume and attenuation values for Group 1 were 176.6 ± 56.9 sm3 and -77.47 ± 2.2 HU respectively; for Group 2 were 174.05 ± 73.3 sm3 and -78.42 ± 3.3 HU respectively, with no significant differences (p < 0.05). In the same time, 16 of 93 CT radiomics EAT parameters were significantly different between Group 1 and Group 2 and were significantly associated with AF recurrence after CA according to univariable logistic analyses. Multivariate regression analysis demonstrated that only Gray Level Non-Uniformity Normalized (GLNUN of GLSZM) parameter was an independent predictor of AF recurrence (Odds ratio 1.0022, 95%Cl 1.0006 to 1.0038, p = 0.0013); ROC-curve analysis data showed that GLNUN > 1227.2 indicates high probability of AF recurrence during 12 months (sensitivity 84.2 %, specificity 70.8 %, AUC:0.765; p = 0.001). Conclusion radiomic biomarkers of EAT have a potential to serve as a predictors of AF recurrence after CA.

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

Journal Of Cardiovascular Emergencies ◽

10.1515/jce-2017-0003 ◽

2017 ◽

Vol 3 (1) ◽

pp. 18-29 ◽

Cited By ~ 1

Author(s):

Theodora Benedek ◽

Nora Rat ◽

Roxana Hodas ◽

Diana Opincariu ◽

András Mester ◽

...

Keyword(s):

Systematic Review ◽

Adipose Tissue ◽

Transthoracic Echocardiography ◽

Acute Coronary Syndromes ◽

Cardiac Ct ◽

Epicardial Adipose Tissue ◽

Risk Scores ◽

Coronary Syndromes

Abstract 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.

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