The Role of Three-Dimensional Magnetic Resonance Elastography in the Diagnosis of Nonalcoholic Steatohepatitis in Obese Patients Undergoing Bariatric Surgery

Abstract

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.

Trial registration: ClinicalTrials.gov NCT02565446.

FIGURE 1.. Imaging parameters of interest in patients with NASH versus non-NASH.

A. Damping ratio derived from 3D-MRE at 60Hz, 40Hz and 30Hz. Significant differences between NASH and non-NASH were noted at 40Hz and 60Hz. B. Shear stiffness (kPa) derived from 3D-MRE at 60Hz, 40Hz and 30Hz. Significant differences between NASH and non-NASH were noted at 40Hz and 60Hz. C. Fat fraction (%) derived from MRI-PDFF was significantly higher in NASH versus non-NASH patients.

FIGURE 2.

The correlation of damping ratio values with histologic parameters of nonalcoholic steatohepatitis.

FIGURE 3.. Predictive performance of the imaging biomarker for NASH diagnosis.

AUC: area under the curve; PPV: positive predictive value; NPV: negative predictive value.

FIGURE 4.. Examples of imaging analyses and predicted probabilities of NASH and NAFLD activity score (NAS) from three study patients.

The 3 imaging parameters included in the predictive model are shown: mf3D-MRE depiction of shear stiffness and damping ratio; MRI-PDFF depiction of fat fraction. The horizontal boxes illustrate the NAS values ranging from 0-8. The shaded part of the boxes represents the predicted range of NAS which were derived from the regression model as the highest probabilities within the 68% confidence interval. In patient A, who by histology does not have NASH and whose NAS=0, the logistic regression model using imaging parameters predicted a 15% probability of NASH and a predicted NAS=0-1. Patient B, with histologic NASH and NAS=3, has a predicted probability of 60% for NASH and a predicted NAS=3-4. Lastly, patient C, with histologic NASH and NAS=6, has a 98% predicted probability of NASH and a predicted NAS=5-6.