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Automated quantification of epicardial adipose tissue from non-contrast CT on multi-center and multi-vendor data using deep learning

Session Advanced PET & CT techniques for clinical practice

Speaker Frederic Commandeur

Congress : ESC Congress 2019

  • Topic : imaging
  • Sub-topic : Coronary Calcium Score
  • Session type : Abstract Session
  • FP Number : 5963

Authors : F Commandeur (Los Angeles,US), M Goeller (Erlangen,DE), A Razipour (Los Angeles,US), S Cadet (Los Angeles,US), MM Hell (Erlangen,DE), J Kwiecinski (Los Angeles,US), X Chen (Los Angeles,US), HJ Chang (Seoul,KR), M Marwan (Erlangen,DE), S Achenbach (Erlangen,DE), DS Berman (Los Angeles,US), PJ Slomka (Los Angeles,US), BK Tamarappoo (Los Angeles,US), D Dey (Los Angeles,US)

F Commandeur1 , M Goeller2 , A Razipour1 , S Cadet3 , MM Hell2 , J Kwiecinski3 , X Chen3 , HJ Chang4 , M Marwan2 , S Achenbach2 , DS Berman3 , PJ Slomka3 , BK Tamarappoo3 , D Dey1 , 1Cedars-Sinai Medical Center, Biomedical Imaging Research Institute - Los Angeles - United States of America , 2Friedrich Alexander University, Department of Cardiology - Erlangen - Germany , 3Cedars-Sinai Medical Center, Department of Imaging and Medicine - Los Angeles - United States of America , 4Severance Hospital, Yonsei University College of Medicine - Seoul - Korea (Republic of) ,


Epicardial adipose tissue (EAT), a metabolically active visceral fat depot surrounding the coronary arteries, has been shown to promote the development of atherosclerosis in underlying coronary vasculature.

We evaluate the performance of deep learning (DL), a sub-group of machine learning algorithms, for robust and fully automated quantification of EAT on multi-center cardiac CT data.

In this study, 850 non-contrast calcium scoring CT scans, from multiple cohorts, scanners and protocols, with manual measurements of EAT from 3 different readers were considered. The DL method was based on a convolutional neural network trained to reproduce the expert measurement. DL global performance was first assessed using all the scans, and then compared to inter-observer variability on a subset of 141 scans. Finally, automated EAT progression was compared to manual measurement using baseline and follow-up serial scans available for 70 subjects. The proposed model was validated using 10-fold cross validation.

Automated quantification was performed in 1.57+-0.49 seconds compared to 15 minutes for manual measurement. DL provided high agreement with expert manual quantification for all scans (R=0.974, p<0.001) with no significant bias (0.53 cm3, p=0.13). EAT volume was higher in patients with hypertension (+18.02 cm3, p<0.001, N=442), with diabetes (+18.33 cm3, p<0.001, N=75) and with hypercholesterolemia (+7.33 cm3, p=0.039, N=508). Manual EAT volumes measured by two experienced readers on 141 scans were highly correlated (R=0.984, p<0.001) but presented a significant difference of 4.35 cm3 (p<0.001). On these 141 scans, DL quantifications were highly correlated to both experts’ measurements (R=0.973, p<0.001; R=0.979, p<0.001) with significant and non-significant bias for readers 1 and 2 (5.19 cm3, p<0.001; 0.84 cm3, p=0.26), respectively. In 70 subjects, EAT progression quantified by DL correlated strongly with EAT progression measured by the expert reader (R=0.905, p<0.001) with no significant bias (0.64 cm3, p=0.43), and was related to increased non-calcified plaque burden quantified from coronary CT angiography (5.7% vs 1.8%, p=0.026).

Deep learning allows rapid, robust and fully automated quantification of EAT from calcium scoring CT. It performs as an expert reader and can be implemented for routine cardiovascular risk assessment.

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