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BackgroundNonalcoholic 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.PurposeTo implement and test an automated system for analyzing CSE‐MRI and MRE data coupled with model‐based prediction of NASH.Study TypeProspective.SubjectsEighty‐three patients with suspected NAFLD.Field Strength/SequenceA 1.5 T using a flow‐compensated motion‐encoded gradient echo MRE sequence and a multiecho CSE‐MRI sequence.AssessmentsThe 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).Statistical TestsPearson's correlation, Bland–Altman, AUROC, C‐statistic.ResultsThe 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.Data ConclusionWe 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.Level of Evidence2Technical Efficacy Stage2

Disease monitoring in nonalcoholic steatohepatitis (NASH) is limited by absence of noninvasive biomarkers of disease regression or progression. We aimed to examine the role of multiparametric three‐dimensional magnetic resonance elastography (3D‐MRE) and magnetic resonance imaging proton density fat fraction (MRI‐PDFF) in the detection of NASH regression after interventions. This is a single‐center prospective clinical trial of 40 patients who underwent bariatric surgery. Imaging and liver biopsies were obtained at baseline and 1 year after surgery. The imaging protocol consisted of multifrequency 3D‐MRE to determine the shear stiffness at 60 Hz and damping ratio at 40 Hz, and MRI‐PDFF to measure the fat fraction. A logistic regression model including these three parameters was previously found to correlate with NASH. We assessed the model performance in the detection of NASH resolution after surgery by comparing the image‐predicted change in NAFLD activity score (delta NAS) to the histologic changes. A total of 38 patients (median age 43, 87% female, 30 of 38 with NAS ≥ 1, and 13 of 38 with NASH) had complete data at 1 year. The NAS decreased in all subjects with NAS ≥ 1 at index biopsy, and NASH resolved in all 13. There was a strong correlation between the predicted delta NAS by imaging and the delta NAS by histology (r = 0.73, P < 0.001). The strength of correlation between histology and the predicted delta NAS using single conventional parameters, such as the fat fraction by MRI‐PDFF or shear stiffness at 60 Hz by MRE, was r = 0.69 (P < 0.001) and r = 0.43 (P = 0.009), respectively. Conclusion: Multiparametric 3D‐MRE and MRI‐PDFF can detect histologic changes of NASH resolution after bariatric surgery. Studies in a nonbariatric setting are needed to confirm the performance as a composite noninvasive biomarker for longitudinal NASH monitoring.

Circulating extracellular vesicles (EVs) are a novel and emerging biomarker for nonalcoholic steatohepatitis (NASH). It has been demonstrated that total circulating EVs and hepatocyte‐derived EVs are elevated in male mice with diet‐induced NASH. How hepatocyte‐derived EVs change over time and other cellular sources of EVs in NASH have not been determined. Our objective was to define the quantitative evolution of hepatocyte‐derived, macrophage‐derived, neutrophil‐derived, and platelet‐derived EVs in male and female mice with dietary NASH. Fluorescently labeled antibodies and a nanoscale flow cytometer were used to detect plasma levels of EVs. Asialoglycoprotein receptor 1 (ASGR1) and cytochrome P450 family 2 subfamily E member 1 (CYP2E1) are markers of hepatocyte‐derived EVs; galectin 3 is a marker of macrophage‐derived EVs; common epitope on lymphocyte antigen 6 complex, locus G/C1 (Ly‐6G and Ly‐6C) is a marker of neutrophil‐derived EVs; and clusters of differentiation 61 (CD61) is a marker of platelet‐derived EVs. Nonalcoholic fatty liver disease activity score (NAS) was calculated using hematoxylin and eosin‐stained liver sections, and magnetic resonance imaging (MRI) was used for measurement of the fat fraction and elastography. Hepatocyte‐derived EVs increased in both male and female mice at 12 and 10 weeks of feeding, respectively, and remained elevated at 24 weeks in both male and female mice and at 48 weeks in male mice and 36 weeks in female mice. Macrophage‐ and neutrophil‐derived EVs were significantly elevated at 24 weeks of dietary feeding concomitant with the histologic presence of inflammatory foci in the liver. In fat‐, fructose‐, and cholesterol‐ (FFC) fed male mice, platelet‐derived EVs were elevated at 12, 24, and 48 weeks, whereas in female mice, platelet derived EVs were significantly elevated at 24 weeks. Hepatocyte‐, macrophage‐ and neutrophil‐derived EVs correlated well with the histologic NAS. Conclusion: Circulating cell‐type‐specific EVs may be a novel biomarker for NASH diagnosis and longitudinal follow up.

The lack of reliable, noninvasive methods to diagnose early nonalcoholic steatohepatitis (NASH) is a major unmet need. We aimed to determine the diagnostic accuracy of three‐dimensional magnetic resonance elastography (3D‐MRE), with shear stiffness measured at 60 Hz, damping ratio at 40 Hz, and magnetic resonance imaging proton density fat fraction (MRI‐PDFF) in the detection of NASH in individuals undergoing bariatric surgery. Obese adults at risk for NASH were enrolled between 2015 and 2017 (prospective cohort, n = 88) and 2010 and 2013 (retrospective cohort, n = 87). The imaging protocol consisted of multifrequency 3D‐MRE (mf3D‐MRE) with shear waves delivered at different frequencies to explore parameters that best correlated with histologic NASH, and MRI‐PDFF to estimate steatosis. The prospective cohort was used to establish the optimal mf3D‐MRE technical parameters for NASH detection. The two cohorts were then combined to derive predictive models of NASH and disease activity by nonalcoholic fatty liver disease activity score (NAS) using the three imaging parameters that correlated with NASH. A total of 175 patients (median age 45, 81% women, and 81 [46%] with histologic NASH) were used for model derivation. From the complex shear modulus output generated by mf3D‐MRE, the damping ratio at 40 Hz and shear stiffness at 60 Hz best correlated with NASH. The fat fraction obtained from MRI‐PDFF correlated with steatosis ( P < 0.05 for all). These three parameters were fit into a logistic regression model that predicted NASH with cross‐validated area under the receiver operating characteristic curve (AUROC) = 0.73, sensitivity = 0.67, specificity = 0.80, positive predictive value = 0.73 and negative predictive value = 0.74, and disease activity by NAS with cross‐validated AUROC = 0.82. Conclusion: The mf3D‐MRE allows identification of imaging parameters that predict early NASH and disease activity. This imaging biomarker represents a promising alternative to liver biopsy for NASH diagnosis and monitoring. The results provide motivation for further studies in nonbariatric cohorts.