A new study was released on February 16, 2021 that looked at automated MRE and PDFF analysis, while also looking at advanced models to prospectively predict biopsy-diagnosed NASH.
Nonalcoholic fatty liver disease (NAFLD) affects 25% of the global population. The standard of diagnosis, biopsy, is invasive and affected by sampling error and inter‐reader variability. We hypothesized that widely available rapid MRI techniques could be used to predict nonalcoholic steatohepatitis (NASH) noninvasively by measuring liver stiffness, with magnetic resonance elastography (MRE), and liver fat, with chemical shift‐encoded (CSE) MRI. Besides, we validate an automated image analysis technique to maximize the utility of these methods.
The MRE and CSE‐MRI data were analyzed by two readers (5+ and 1 years of experience) and an automated algorithm. A logistic regression model to predict pathology‐diagnosed NASH was trained based on stiffness and proton density fat fraction, and the area under the receiver operating characteristic curve (AUROC) was calculated using 10‐fold cross validation for models based on both automated and manual measurements. A separate model was trained to predict the NASH severity score (NAS).
The agreement between automated measurements and the more experienced reader (R2 = 0.87 for stiffness and R2 = 0.99 for proton density fat fraction [PDFF]) was slightly better than the agreement between readers (R2 = 0.85 and 0.98). The model for predicting biopsy‐diagnosed NASH had an AUROC of 0.87. The NAS‐prediction model had a C‐statistic of 0.85.
We demonstrated a workflow that used a limited MRI acquisition protocol and fully automated analysis to predict NASH with high accuracy. These methods show promise to provide a reliable noninvasive alternative to biopsy for NASH‐screening in populations with NAFLD.