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

The epicardial adipose tissue volume (EATV) was quantitatively measured by deep learning-based computed tomography (CT) images, and its correlation with coronary heart disease (CHD) was investigated in this study. 150 patients who underwent coronary artery CT examination in hospital were taken as research objects. Besides, patients from the observation group (group A) suffered from vascular stenosis, while patients from the control group (group B) had no vascular stenosis. The deep convolutional neural network model was applied to construct deep learning algorithm, and deep learning-based CT images were adopted to quantitatively measure EATV. The results showed that the sensitivity, specificity, accuracy, and area under the curve (AUC) of the deep learning algorithm were 0.8512, 0.9899, 0.9623, and 0.9813, respectively. By comparison, the correlation results of the traditional George algorithm, Aslani algorithm, and Lahiri algorithm were all lower than those of the deep learning algorithm, and the difference was statistically substantial ( P < 0.05 ). The epicardial adipose tissue volume of the observation group (114.23 ± 55.46) was higher markedly than the volume of the control group (92.65 ± 43.28), with a statistically huge difference ( P < 0.05 ). The r values of EATV with plaque properties and the number of stenosed coronary vessels were 0.232 and 0.268 in turn, both showing significant positive correlation. In conclusion, the sensitivity and other index values of deep learning algorithm were improved greatly compared with traditional algorithm. CT images based on deep learning algorithm achieved good blood vessel segmentation effects. In addition, EATV was closely related to the development of CHD.

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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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