Background and Aims: Despite that hepatic fibrosis often affects the liver globally, spatial distribution can be heterogeneous. This study aimed to investigate the effect of liver stiffness (LS) heterogeneity on concordance between MR elastography (MRE)-based fibrosis staging and biopsy staging in patients with NAFLD. Approach and Results:We retrospectively evaluated data from 155 NAFLD patients who underwent liver biopsy and 3 Tesla MRE and undertook a retrospective validation study of 169 NAFLD patients at three hepatology centers.Heterogeneity of stiffness was assessed by measuring the range between minimum and maximum MRE-based LS measurement (LSM). Variability of LSM was defined as the stiffness range divided by the maximum stiffness value. The cohort was divided into two groups (homogenous or heterogeneous), according to whether variability was below or above the average for the training cohort. Based on histopathology and receiver operating characteristic

Background and Aims: The presence of at-risk NASH is associated with an increased risk of cirrhosis and complications. Therefore, noninvasive identification of at-risk NASH with an accurate biomarker is a critical need for pharmacologic therapy. We aim to explore the performance of several magnetic resonance (MR)-based imaging parameters in diagnosing at-risk NASH. Approach and Results: This prospective clinical trial (NCT02565446) includes 104 paired MR examinations and liver biopsies performed in patients with suspected or diagnosed NAFLD. Magnetic resonance elastography-assessed liver stiffness (LS), 6-point Dixon-derived proton density fat fraction (PDFF), and single-point saturation-recovery acquisition-calculated T1 relaxation time were explored. Among all predictors, LS showed the significantly highest accuracy in diagnosing at-risk NASH [AUCLS: 0.89 (0.82, 0.95), AUCPDFF: 0.70 (0.58, 0.81), AUCT1: 0.72 (0.61, 0.82), z-score test z >1.96 for LS vs any of others]. The optimal cutoff value of LS to identify at-risk NASH patients was 3.3 kPa (sensitivity: 79%, specificity: 82%, negative predictive value: 91%), whereas the optimal cutoff value of T1 was 850 ms (sensitivity: 75%, specificity: 63%, and negative predictive value: 87%). PDFF had the highest performance in diagnosing NASH with any fibrosis stage [AUCPDFF: 0.82 (0.72, 0.91), AUCLS: 0.73 (0.63, 0.84), AUCT1: 0.72 (0.61, 0.83), |z| <1.96 for all]. Conclusion: Magnetic resonance elastography-assessed LS alone outperformed PDFF, and T1 in identifying patients with at-risk NASH for therapeutic trials.

Liver biopsy and multi-parametric MRI/MRE were performed on 380 patients at risk of NASH. Histopathological diagnoses of NASH were collected along with proton density fat fraction (PDFF), and 3D-MRE measured liver stiffness (LS) and loss modulus (LM). While PDFF alone showed good ability to predict NASH (AUC: 0.80 [0.76, 0.85]), the addition of MRE measurements increased the diagnostic performance with strong statistical significance (AUC: 0.85 [0.81, 0.89], p&lt;0.0004). LS alone was also found to be the strongest predictor of NASH-required treatment (AUC: 0.89 [0.84, 0.93], p&lt;0.05), further establishing the critical role of MRE for clinical management of liver disease.

Multiparametric MRI/MRE and liver biopsy were performed in 104 patients at risk for NASH. We correlated measurements of proton density fat fraction (PDFF), 3D vector MRE-assessed liver stiffness (LS), and T1 relaxation time across histological features of NASH. We found that PDFF showed superior diagnostic performance in identifying patients with NASH (AUC: 0.84 [0.76, 0.92]), while LS showed superior diagnostic performance in stratifying NASH patients at high risk (0.85 [0.75, 0.94]). The results showed that T1 measurement, either alone or in combination with other biomarkers, did not perform as well as PDFF and LS in diagnosing NASH or stratifying high-risk NASH.