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Peer-Reviewed Publication
Eur Radiol2021;31(6):3805-3814.June 1, 2021Journal Article

Fully automated prediction of liver fibrosis using deep learning analysis of gadoxetic acid-enhanced MRI.

Stefanie J Hectors1,2,3, Paul Kennedy1,2, Kuang-Han Huang1,4, Daniel Stocker1,2,5, Guillermo Carbonell1,2,6, Hayit Greenspan7, Scott Friedman8, Bachir Taouli9,10
1BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
2Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.
3Department of Radiology, Weill Cornell Medicine, New York, NY, USA.
4Prealize Health, Palo Alto, CA, USA.
5Institute of Interventional and Diagnostic Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
6Department of Radiology, Virgen de la Arrixaca University Clinical Hospital, University of Murcia, Murcia, Spain.
7Medical Imaging Processing Lab, Faculty of Engineering, Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.
8Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
9BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. bachir.taouli@mountsinai.org.
10Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA. bachir.taouli@mountsinai.org.
PubMed DOI

Abstract

OBJECTIVES: To (1) develop a fully automated deep learning (DL) algorithm based on gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and (2) compare the diagnostic performance of DL vs. MR elastography (MRE) for noninvasive staging of liver fibrosis. METHODS: This single-center retrospective study included 355 patients (M/F 238/117, mean age 60 years; training, n = 178; validation, n = 123; t…

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