Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning: A Multicenter Study
Authors
Frederic Commandeur ; Markus Goeller ; Aryabod Razipour ; Sebastien Cadet ; Michaela M Hell ; Jacek Kwiecinski ; Xi Chen ; Hyuk-Jae Chang ; Mohamed Marwan ; Stephan Achenbach ; Daniel S Berman ; Piotr J Slomka ; Balaji K Tamarappoo ; Damini Dey
Purpose: To evaluate the performance of deep learning for robust and fully automated quantification of epicardial adipose tissue (EAT) from multicenter cardiac CT data.
Materials and methods: In this multicenter study, a convolutional neural network approach was trained to quantify EAT on non-contrast material-enhanced calcium-scoring CT scans from multiple cohorts, scanners, and protocols (n = 850). Deep learning performance was compared with the performance of three expert readers and with interobserver variability in a subset of 141 scans. The deep learning algorithm was incorporated into research software. Automated EAT progression was compared with expert measurements for 70 patients with baseline and follow-up scans.
Results: Automated quantification was performed in a mean (± standard deviation) time of 1.57 seconds ± 0.49, compared with 15 minutes for experts. Deep learning provided high agreement with expert manual quantification for all scans (R = 0.974; P < .001), with no significant bias (0.53 cm3; P = .13). Manual EAT volumes measured by two experienced readers were highly correlated (R = 0.984; P < .001) but with a bias of 4.35 cm3 (P < .001). Deep learning quantifications were highly correlated with the measurements of both experts (R = 0.973 and R = 0.979; P < .001), with significant bias for reader 1 (5.11 cm3; P < .001) but not for reader 2 (0.88 cm3; P = .26). EAT progression by deep learning correlated strongly with manual EAT progression (R = 0.905; P < .001) in 70 patients, with no significant bias (0.64 cm3; P = .43), and was related to an increased noncalcified plaque burden quantified from coronary CT angiography (5.7% vs 1.8%; P = .026).