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

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.

Radon descriptor-based machine learning using CT images to predict the fat tissue on left atrium in the heart

“…The fat tissue was measured using Mass software, and the posterior left atrial fat mass was manually traced. Ilyushenkova et al 12 segmented the epicardial adipose tissue by 3D-Sliser software and the SliserRadiomics module. They quantified the epicardial tissue from CT images and 93 radiomic features which include the first order statistics GLCM, GLDM, GLRLM, GLSZM, and NGTDM parameters.…”