Abstract #4797

Comparison of 2D and 3D MRE Performance in Prediction of MASLD-Associated Risk Factors

Nana Kwame Owusu¹, Kyle J. Kalutkiewicz², Jiahui Li³, Jun Chen³, Kevin J. Glaser³, Armando Manduca¹, Claude Sirlin⁴, Rohit Loomba⁵, Alina Allen⁶, Richard L. Ehman³, and Meng Yin³

¹Mayo Clinic, Rochester, MN, United States, ²Resoundant, Inc., Rochester, MN, United States, ³Radiology, Mayo Clinic, Rochester, MN, United States, ⁴Radiology, UC San Diego, La Jolla, CA, United States, ⁵Gastroenterology and Hepatology, UC San Diego, La Jolla, CA, United States, ⁶Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States

Synopsis

Keywords: Elastography, Elastography, diagnosis/prediction, machine learning

Motivation: Pathology of steatotic liver disease is characterized by fat burden, inflammation, and ballooning. Mechanical properties of tissue acquire by 2D-MRE has improved the detection of this disease. However, 3D-MRE, an improvement over 2D-MRE is not widely adopted by clinicians.

Goal(s): We aimed to compare the predictive capabilities of these two in risk factors associated with this disease using machine learning to help motivate clinicians to use 3D-MRE.

Approach: Use machine learning to determine the predictive capabilities of 2D-MRE and 3D-MRE.

Results: Results showed 3D-MRE best predicts steatohepatitis, inflammation, and ballooning.

Impact: The demonstrated predictive value of 3D-MRE in identifying those with certain risk factors allows clinicians to better plan treatment for patients with steatotic liver disease.

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