Changes in abdominal adipose tissue depots assessed by MRI correlate with hepatic histologic improvement in non-alcoholic steatohepatitis.

BACKGROUND & AIMS: Non-alcoholic steatohepatitis (NASH) is prevalent in adults with obesity and can progress to cirrhosis. In a secondary analysis of prospectively acquired data from the multicenter, randomized, placebo-controlled FLINT trial, we investigated the relationship between reduction in adipose tissue compartment volumes and hepatic histologic improvement. METHODS: Adult participants in the FLINT trial with paired liver biopsies and abdominal MRI exams at baseline and end-of-treatment (72 weeks) were included (n = 76). Adipose tissue compartment volumes were obtained using MRI. RESULTS: Treatment and placebo groups did not differ in baseline adipose tissue volumes, or in change in adipose tissue volumes longitudinally (p = 0.107 to 0.745). Deep subcutaneous adipose tissue (dSAT) and visceral adipose tissue volume reductions were associated with histologic improvement in NASH (i.e., NAS [non-alcoholic fatty liver disease activity score] reductions of ≥2 points, at least 1 point from lobular inflammation and hepatocellular ballooning, and no worsening of fibrosis) (p = 0.031, and 0.030, respectively). In a stepwise logistic regression procedure, which included demographics, treatment group, baseline histology, baseline and changes in adipose tissue volumes, MRI hepatic proton density fat fraction (PDFF), and serum aminotransferases as potential predictors, reductions in dSAT and PDFF were associated with histologic improvement in NASH (regression coefficient = -2.001 and -0.083, p = 0.044 and 0.033, respectively). CONCLUSIONS: In adults with NASH in the FLINT trial, those with greater longitudinal reductions in dSAT and potentially visceral adipose tissue volumes showed greater hepatic histologic improvements, independent of reductions in hepatic PDFF. CLINICAL TRIAL NUMBER: NCT01265498. IMPACT AND IMPLICATIONS: Although central obesity has been identified as a risk factor for obesity-related disorders including insulin resistance and cardiovascular disease, the role of central obesity in non-alcoholic steatohepatitis (NASH) warrants further clarification. Our results highlight that a reduction in central obesity, specifically deep subcutaneous adipose tissue and visceral adipose tissue, may be related to histologic improvement in NASH. The findings from this analysis should increase awareness of the importance of lifestyle intervention in NASH for clinical researchers and clinicians. Future studies and clinical practice may design interventions that assess the reduction of deep subcutaneous adipose tissue and visceral adipose tissue as outcome measures, rather than simply weight reduction.

A Multicenter Assessment of Interreader Reliability of LI-RADS Version 2018 for MRI and CT.

Background Various limitations have impacted research evaluating reader agreement for Liver Imaging Reporting and Data System (LI-RADS). Purpose To assess reader agreement of LI-RADS in an international multicenter multireader setting using scrollable images. Materials and Methods This retrospective study used deidentified clinical multiphase CT and MRI and reports with at least one untreated observation from six institutions and three countries; only qualifying examinations were submitted. Examination dates were October 2017 to August 2018 at the coordinating center. One untreated observation per examination was randomly selected using observation identifiers, and its clinically assigned features were extracted from the report. The corresponding LI-RADS version 2018 category was computed as a rescored clinical read. Each examination was randomly assigned to two of 43 research readers who independently scored the observation. Agreement for an ordinal modified four-category LI-RADS scale (LR-1, definitely benign; LR-2, probably benign; LR-3, intermediate probability of malignancy; LR-4, probably hepatocellular carcinoma [HCC]; LR-5, definitely HCC; LR-M, probably malignant but not HCC specific; and LR-TIV, tumor in vein) was computed using intraclass correlation coefficients (ICCs). Agreement was also computed for dichotomized malignancy (LR-4, LR-5, LR-M, and LR-TIV), LR-5, and LR-M. Agreement was compared between research-versus-research reads and research-versus-clinical reads. Results The study population consisted of 484 patients (mean age, 62 years ± 10 [SD]; 156 women; 93 CT examinations, 391 MRI examinations). ICCs for ordinal LI-RADS, dichotomized malignancy, LR-5, and LR-M were 0.68 (95% CI: 0.61, 0.73), 0.63 (95% CI: 0.55, 0.70), 0.58 (95% CI: 0.50, 0.66), and 0.46 (95% CI: 0.31, 0.61) respectively. Research-versus-research reader agreement was higher than research-versus-clinical agreement for modified four-category LI-RADS (ICC, 0.68 vs 0.62, respectively; P = .03) and for dichotomized malignancy (ICC, 0.63 vs 0.53, respectively; P = .005), but not for LR-5 (P = .14) or LR-M (P = .94). Conclusion There was moderate agreement for LI-RADS version 2018 overall. For some comparisons, research-versus-research reader agreement was higher than research-versus-clinical reader agreement, indicating differences between the clinical and research environments that warrant further study. © RSNA, 2023 Supplemental material is available for this article. See also the editorials by Johnson and Galgano and Smith in this issue.

MR elastography in nonalcoholic fatty liver disease: inter-center and inter-analysis-method measurement reproducibility and accuracy at 3T.

OBJECTIVES: To assess reproducibility and fibrosis classification accuracy of magnetic resonance elastography (MRE)-determined liver stiffness measured manually at two different centers, and by automated analysis software in adults with nonalcoholic fatty liver disease (NAFLD), using histopathology as a reference standard. METHODS: This retrospective, cross-sectional study included 91 adults with NAFLD who underwent liver MRE and biopsy. MRE-determined liver stiffness was measured independently for this analysis by an image analyst at each of two centers using standardized manual analysis methodology, and separately by an automated analysis. Reproducibility was assessed pairwise by intraclass correlation coefficient (ICC) and Bland-Altman analysis. Diagnostic accuracy was assessed by receiver operating characteristic (ROC) analyses. RESULTS: ICC of liver stiffness measurements was 0.95 (95% CI: 0.93, 0.97) between center 1 and center 2 analysts, 0.96 (95% CI: 0.94, 0.97) between the center 1 analyst and automated analysis, and 0.94 (95% CI: 0.91, 0.96) between the center 2 analyst and automated analysis. Mean bias and 95% limits of agreement were 0.06 ± 0.38 kPa between center 1 and center 2 analysts, 0.05 ± 0.32 kPa between the center 1 analyst and automated analysis, and 0.11 ± 0.41 kPa between the center 2 analyst and automated analysis. The area under the ROC curves for the center 1 analyst, center 2 analyst, and automated analysis were 0.834, 0.833, and 0.847 for distinguishing fibrosis stage 0 vs. ≥ 1, and 0.939, 0.947, and 0.940 for distinguishing fibrosis stage ≤ 2 vs. ≥ 3. CONCLUSION: MRE-determined liver stiffness can be measured with high reproducibility and fibrosis classification accuracy at different centers and by an automated analysis. KEY POINTS: • Reproducibility of MRE liver stiffness measurements in adults with nonalcoholic fatty liver disease is high between two experienced centers and between manual and automated analysis methods. • Analysts at two centers had similar high diagnostic accuracy for distinguishing dichotomized fibrosis stages. • Automated analysis provides similar diagnostic accuracy as manual analysis for advanced fibrosis.

Automated CNN-Based Analysis Versus Manual Analysis for MR Elastography in Nonalcoholic Fatty Liver Disease: Intermethod Agreement and Fibrosis Stage Discriminative Performance.

BACKGROUND. Histologic fibrosis stage is the most important prognostic factor in chronic liver disease. MR elastography (MRE) is the most accurate noninvasive method for detecting and staging liver fibrosis. Although accurate, manual ROI-based MRE analysis is complex, time-consuming, requires specialized readers, and is prone to methodologic variability and suboptimal interreader agreement. OBJECTIVE. The purpose of this study was to develop an automated convolutional neural network (CNN)-based method for liver MRE analysis, evaluate its agreement with manual ROI-based analysis, and assess its performance for classifying dichotomized fibrosis stages using histology as the reference standard. METHODS. In this retrospective cross-sectional study, 675 participants who underwent MRE using different MRI systems and field strengths at 28 imaging sites from five multicenter international clinical trials of nonalcoholic steatohepatitis were included for algorithm development and internal testing of agreement between automated CNN-based and manual ROI-based analyses. Eighty-one patients (52 women, 29 men; mean age, 54 years) who underwent MRE using a single 3-T system and liver biopsy for clinical purposes at a single institution were included for external testing of agreement between the two analysis methods and assessment of fibrosis stage discriminative performance. Agreement was evaluated using intraclass correlation coefficients (ICCs). Bootstrapping was used to compute 95% CIs. Discriminative performance of each method for dichotomized histologic fibrosis stage was evaluated by AUC and compared using bootstrapping. RESULTS. Mean CNN- and manual ROI-based stiffness measurements ranged from 3.21 to 3.34 kPa in trial participants and from 3.21 to 3.30 kPa in clinical patients. ICC for CNN- and manual ROI-based measurements was 0.98 (95% CI, 0.97-0.98) in trial participants and 0.99 (95% CI, 0.98-0.99) in clinical patients. AUCs for classification of dichotomized fibrosis stage ranged from 0.89 to 0.93 for CNN-based analysis and 0.87 to 0.93 for manual ROI-based analysis (p = .23-.75). CONCLUSION. Stiffness measurements using the automated CNN-based method agreed strongly with manual ROI-based analysis across MRI systems and field strengths, with excellent discriminative performance for histology-determined dichotomized fibrosis stages in external testing. CLINICAL IMPACT. Given the high incidence of chronic liver disease worldwide, it is important that noninvasive tools to assess fibrosis are applied reliably across different settings. CNN-based analysis is feasible and may reduce reliance on expert image analysts.

Multi-arterial phase MRI depicts inconsistent arterial phase hyperenhancement (APHE) subtypes in liver observations of patients at risk for hepatocellular carcinoma.

OBJECTIVES: According to LI-RADS, a major discriminating feature between hepatocellular carcinoma (HCC) and non-HCC malignancies is the subtype of arterial phase hyperenhancement (APHE). The aim of this study was to investigate whether APHE subtypes are consistent across multi-arterial phase (mHAP) MRI acquisitions while evaluating reader agreement. Secondarily, we investigated factors that may affect reader agreement for APHE subtype. METHODS: In this retrospective study, consecutive patients with liver cirrhosis and focal observations who underwent mHAP were included. Five radiologists reviewed MR images in 2 reading sessions. In reading session 1, individual AP series were reviewed and scored for presence of APHE and subtype. In reading session 2, readers scored observations' major and ancillary features and LI-RADS category in the complete MRI examination. Reader agreement was calculated using Fleiss' kappa for binary outcomes and Kendall's coefficient of concordance for LI-RADS categories. Univariate mixed effects logistic regressions were performed to investigate factors affecting agreement. RESULTS: In total, 61 patients with 77 focal observations were analyzed. Of observations unanimously scored as having APHE, 27.7% showed both rim and nonrim subtypes on mHAP. Inter-reader agreement for APHE subtype ranged from 0.49 (95% CI: 0.33, 0.64) to 0.57 (95% CI: 0.40, 0.74) between reading sessions. Observation size had a trend level effect on rim APHE agreement (p = 0.052). CONCLUSION: Approximately 1/3 of observations demonstrated inconsistent APHE subtype during mHAP acquisition. Small lesions were particularly challenging. Further guidance on APHE subtype classification, especially when applied to mHAP, could be a focus of LI-RADS refinement. KEY POINTS: • In a cohort of patients at risk for HCC, 28% of the observations showed inconsistent arterial phase hyperenhancement (APHE) subtypes (rim and nonrim) on multi-arterial phase imaging according to the majority score of 5 independent readers. • Inconsistent APHE subtypes may challenge reliable imaging diagnosis, i.e., LI-RADS categorization, of focal liver observations in patients at risk for HCC.

Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.

PURPOSE: To assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry. METHODS: We trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics. RESULTS: Dice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]). CONCLUSIONS: Utilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization.