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Abstract #0238

Ensemble Learning Stratification of Liver Histologic Fibrosis using Multi-Modal MRI and EHR Data in Pediatric and Adult Patients

Hailong Li1, Zhixiu Lu1, Scott B. Reeder2, David T. Harris 2, William Masch3, Anum Aslam3, Krishna Shanbhogue4, Anas Bernieh1, Sarangarajan Ranganathan1, Jonathan R. Dillman1, and Lili He1
1Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 2University of Wisconsin, Madison, WI, United States, 3Michigan Medicine, University of Michigan, Ann Arbor, MI, United States, 4New York University, New York, NY, United States

Synopsis

Keywords: Diagnosis/Prediction, AI/ML Software, liver fibrosis, MRI, EHR

Motivation: Chronic liver diseases can lead to variable liver fibrosis. Percutaneous liver biopsy is the clinical standard; however, it has limitations.

Goal(s): Develop an ensemble learning model to stratify liver fibrosis using non-invasive clinical MRI and EHR data.

Approach: In this retrospective, multi-site study, we retrieved liver tissue specimens, multi-modal MRI, and EHR data. Using biopsy-derived liver fibrosis stages as reference, a stacking ensemble learning model was trained to classify a patient into the no/mild or advanced liver fibrosis.

Results: Using multi-modal MRI and EHR data, our model achieved an AUROCs of 0.69 – 0.75 on liver fibrosis stratification in internal and external cohorts.

Impact: Our study demonstrated that an ensemble learning model had a moderate performance in stratifying liver fibrosis using clinical multi-modal MRI and EHR data. With further tuning, it provides a potential non-invasive means for monitoring and screening of liver fibrosis.

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Keywords