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Abstract #1415

Multiparametric Deep and Radiomic MRI Features for Liver Stiffness Classification in Children and Adults with Chronic Liver Disease

Redha Ali1, Hailong Li1, Wen Pan2, Scott B. Reeder3, David T. Harris3, William Masch4, Anum Alsam4, Krishna Shanbhogue5, Nehal A. Parikh6, Jonathan R. Dillman6, and Lili He1
1Department of Radiology, Cincinnati children's hospital medical center, Cincinnati, OH, United States, 2Department of Radiology, Cincinnati children's hospital medical center, 45429, OH, United States, 3Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, 4Michigan Medicine, University of Michigan, Ann Arbor, MI, United States, 5New York University Langone Health, New York, NY, United States, 6Cincinnati children's hospital medical center, Cincinnati, OH, United States

Synopsis

Keywords: Diagnosis/Prediction, Diagnosis/Prediction

Motivation: To improve access to liver stiffness assessment by offering an alternative to magnetic resonance elastography (MRE), which has limited availability in many geographic regions.

Goal(s): Develop a deep learning method to classify liver stiffness as no/mild (<3 kPa) vs moderate/severe (≥3 kPa) using MRI and electronic health record (EHR) data.

Approach: We used MRSegmentator for segmentation, the Swin-Transformer model and PyRadiomics for feature extraction, and combined these features with EHR data for classification. Our model was validated through internal and external experiments.

Results: Our model achieved an AUROC of 0.88 and 0.90 during internal and external validation, respectively.

Impact: Our model offers an alternative to conventional MR elastography, potentially expanding access and improving care for patients with chronic liver disease.

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Keywords