Abstract #0307

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

Stefanie Hectors^1,2,3, Paul Kennedy^1,2, Kuang-Han Huang^1,4, Hayit Greenspan⁵, Scott Friedman⁶, and Bachir Taouli^1,2

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.

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