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Abstract #4746

PDFF diagnoses MASH in patients with severe obesity but without known liver disease

Nikolaos Panagiotopoulos1, Rashmi Agni2, Danielle Batakis3, Lael Ceriani3, Yesenia Covarrubias3, Luke M. Funk4,5, Eduardo Grunvald6, James A. Goodman7, David T. Harris1, Gavin Hamilton3, Santiago Horgan8, Garth R. Jacobsen8, Anne O. Lidor4, Michael S. Middleton3, Thekla H. Oechtering1, Ryan Sappenfield2, Daiki Tamada1, Tanya Wolfson9, Claude B. Sirlin3, and Scott B. Reeder1,10,11,12,13
1Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, 2Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, United States, 3Department of Radiology, UC San Diego, San Diego, CA, United States, 4Department of Surgery, University of Wisconsin-Madison, Madison, WI, United States, 5Department of Surgery, William S. Middleton VA, Madison, WI, United States, 6Department of Medicine, UC San Diego, San Diego, CA, United States, 7Translational Clinical Sciences, Pfizer Research & Development, Cambridge, MA, United States, 8Department of Surgery, UC San Diego, San Diego, CA, United States, 9San Diego Supercomputer Center, UC San Diego, San Diego, CA, United States, 10Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, 11Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, 12Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States, 13Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States

Synopsis

Keywords: Liver, Quantitative Imaging, PDFF, MASH, NASH, MASLD, NAFLD, cT1

Motivation: To address the pressing need for non-invasive diagnosis of metabolic dysfunction-associated steatohepatitis (MASH).

Goal(s): To evaluate the potential of proton-density fat-fraction (PDFF), corrected T1 (cT1), liver enzymes, and fibrosis scores to assist in the diagnosis of MASH.

Approach: The study included study participants with obesity and at risk for MASH, undergoing bariatric surgery with intraoperative liver biopsy. Potential predictors and predictor combinations were evaluated as classifiers for MASH and steatosis.

Results: PDFF distinguished MASH from non-MASH (AUC=0.85; 95%CI 0.79-0.91, p<0.0001). A cutoff of PDFF≥13.9% detected MASH with 90% specificity and 59% sensitivity. Neither cT1, liver enzymes, nor fibrosis scores significantly improved diagnostic performance.

Impact: Our results suggest that PDFF alone may be sufficient for non-invasive detection of metabolic dysfunction-associated steatohepatitis (MASH). This novel use case for an established method has the potential to transform the diagnostic approach to MASH which currently necessitates invasive biopsy.

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Abstract

Keywords