Meeting Banner
Abstract #0307

Fully Automated Prediction of Liver Fibrosis using Deep Learning Analysis of Gadoxetic acid-enhanced MRI

Stefanie Hectors1,2,3, Paul Kennedy1,2, Kuang-Han Huang1,4, Hayit Greenspan5, Scott Friedman6, and Bachir Taouli1,2
1BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 2Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 3Department of Radiology, Weill Cornell Medicine, New York, NY, United States, 4Prealize Health, Palo Alto, CA, United States, 5Medical Imaging Processing Lab, Tel Aviv University, Tel Aviv, Israel, 6Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, United States

In this study we developed a fully automated deep learning algorithm based on gadoxetic acid-enhanced MRI for noninvasive prediction of liver fibrosis. We found good-to-excellent performance of the algorithm in an independent test set (AUC 0.77 – 0.91), which was equivalent to the diagnostic performance of MR elastography (AUC 0.86 – 0.92, p-values between methods >0.134). The developed algorithm may potentially allow for noninvasive liver fibrosis assessment, without the need for invasive biopsies.

This abstract and the presentation materials are available to 2020 meeting attendees and eLibrary customers only; a login is required.

Join Here