Interpretation, Reporting, and Clinical Applications of Liver MR Elastography.

Chronic liver disease is highly prevalent and often leads to fibrosis or cirrhosis and complications such as liver failure and hepatocellular carcinoma. The diagnosis and staging of liver fibrosis is crucial to determine management and mitigate complications. Liver biopsy for histologic assessment has limitations such as sampling bias and high interreader variability that reduce precision, which is particularly challenging in longitudinal monitoring. MR elastography (MRE) is considered the most accurate noninvasive technique for diagnosing and staging liver fibrosis. In MRE, low-frequency vibrations are applied to the abdomen, and the propagation of shear waves through the liver is analyzed to measure liver stiffness, a biomarker for the detection and staging of liver fibrosis. As MRE has become more widely used in clinical care and research, different contexts of use have emerged. This review focuses on the latest developments in the use of MRE for the assessment of liver fibrosis; provides guidance for image acquisition and interpretation; summarizes diagnostic performance, along with thresholds for diagnosis and staging of liver fibrosis; discusses current and emerging clinical applications; and describes the latest technical developments.

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.

Intra-examination agreement between multi-echo gradient echo and confounder-corrected chemical shift-encoded MR sequences for R2* estimation as a biomarker of liver iron content in patients with a wide range of T2*/R2* and proton density fat fraction values.

PURPOSE: To investigate the intra-examination agreement between multi-echo gradient echo (MEGE) and confounder-corrected chemical shift-encoded (CSE) sequences for liver T2*/R2* estimations in a wide range of T2*/R2* and proton density fat fraction (PDFF) values. Exploratorily, to search for the T2*/R2* value where the agreement line breaks and examine differences between regions of low and high agreement. METHODS: Consecutive patients at risk for liver iron overload who underwent MEGE and CSE sequences within the same exam at 1.5 T were retrospectively selected. Regions of interest were drawn in the right and one in the left liver lobes on post-processed images for R2*(sec-1) and PDFF (%) estimation. Agreement between MEGE-R2* and CSE-R2* was evaluated using intra-class correlation coefficient (ICC) and Bland-Altman analysis. 95% confidence intervals (CI) were computed. Segment-and-regression analysis was performed to find the point where the agreement between sequences is interrupted. Regions of low and high agreement were examined using tree-based partitioning analyses. RESULTS: 49 patients were included. Mean MEGE-R2* was 94.2 s-1 (range: 31.0-737.1) and mean CSE-R2* 87.7 (29.7-748.1). Mean CSE-PDFF was 9.12% (0.1-43.3). Agreement was strong for R2* estimations (ICC: 0.992,95%CI 0.987,0.996), but the relation was nonlinear and possibly heteroskedastic. Lower agreement occurred when MEGE-R2* > 235 s-1, with MEGE-R2* values consistently lower than CSE-R2*. Higher agreement was observed when PDFF < 14%. CONCLUSION: MEGE-R2* and CSE-R2* strongly agree, though at higher iron content, MEGE-R2* is consistently lower than CSE-R2*. In this preliminary dataset, a breaking point for agreement was found at R2* > 235. Lower agreement was observed in patients with moderate to severe liver steatosis.

Up-to-Date Role of CT/MRI LI-RADS in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of mortality worldwide and a major healthcare burden in most societies. Computed tomography (CT) and magnetic resonance imaging (MRI) play a pivotal role in the medical care of patients with or at risk for hepatocellular carcinoma (HCC). When stringent imaging criteria are fulfilled, CT and MRI allow for diagnosis, staging, and assessment of response to treatment, without the need for invasive workup, and can inform clinical decision making. Owing to the central role of these imaging modalities in HCC management, standardization is essential to facilitate proper imaging technique, accurate interpretation, and clear communication among all stakeholders in both the clinical practice and research settings. The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system that provides standardization across the continuum of HCC imaging, including ordinal probabilistic approach for reporting that directs individualized management. This review discusses the up-to-date role of CT and MRI in HCC imaging from the LI-RADS perspective. It also provides a glimpse into the future by discussing how advances in knowledge and technology are likely to enrich the LI-RADS approach.

Quantitative magnetic resonance imaging for chronic liver disease.

Chronic liver disease (CLD) has rapidly increased in prevalence over the past two decades, resulting in significant morbidity and mortality worldwide. Historically, the clinical gold standard for diagnosis, assessment of severity, and longitudinal monitoring of CLD has been liver biopsy with histological analysis, but this approach has limitations that may make it suboptimal for clinical and research settings. Magnetic resonance (MR)-based biomarkers can overcome the limitations by allowing accurate, precise, and quantitative assessment of key components of CLD without the risk of invasive procedures. This review briefly describes the limitations associated with liver biopsy and the need for non-invasive biomarkers. It then discusses the current state-of-the-art for MRI-based biomarkers of liver iron, fat, and fibrosis, and inflammation.

Assessment of SE-MRE-derived shear stiffness at 3.0 Tesla for solid liver tumors characterization.

OBJECTIVES: To evaluate the feasibility and diagnostic value of using a 2D spin-echo MR elastography (SE-MRE) sequence at 3.0 Tesla for solid focal liver lesions (FLL) characterization. METHODS: This prospective study included 55 patients with solid FLL (size > 20 mm), who underwent liver SE-MRE at 3 Tesla between 2016 and 2019. Stiffness measurements were performed by two independent readers blinded to the complete MRI exam or patient information. Histological confirmation or typical behavior on the complete MRI exam evaluated in consensus by expert abdominal radiologists was used as reference standard. FLLs were grouped and compared (malignant vs. benign) using the Mann-Whitney and Kruskal-Wallis tests. MRE diagnostic performance was assessed, and stiffness cutoffs were obtained by analysis of ROC curves from accuracy maximization. A linear regression plot was used to evaluate inter-rater agreement for FLLs stiffness measurements. p values < 0.05 were considered statistically significant. RESULTS: The final study group comprised 57 FLLs (34 malignant, 23 benign). Stiffness measurements were technically successful in 91.23% of lesions. To both readers, the median stiffness of the lesions categorized as benign was 4.5 ± 1.5 kPa and in the malignant group 6.8 ± 1.7 and 7.5 ± 1.5 kPa depending on the reader. A cutoff of 5.8 kPa distinguished malignant and benign lesions with 88% specificity and 75-85% accuracy depending on the reader. The inter-rater agreement was 0.90 ± 0.04 with a correlation coefficient of 0.94. CONCLUSION: 2D-SE-MRE at 3.0 T provides high specificity and PPV to differentiate benign from malignant liver lesions. Trial registration 18FFUA-A02.

Imaging of hepatocellular carcinoma: a pilot international survey.

PURPOSE: To perform an international survey on current practices in imaging-based surveillance, diagnosis, staging, and assessment of treatment response for HCC. MATERIALS AND METHODS: Three themes were covered in this international survey: demographics of respondents and liver imaging expertise; imaging practices for screening, surveillance, diagnosis, staging, and assessment of treatment response for HCC; and diagnostic imaging systems used. Descriptive summaries were created. RESULTS: Of 151 respondents, 22.5% were from Asia, 6.0% from Europe, 19.9% from North America, 26.5% from South America, and 25.2% from Australasia; 57.0% respondents worked in academic and 34.4% in private or mixed settings. Non-contrast ultrasound was most commonly used for screening and surveillance of HCC (90.7%), and multiphase computed tomography was used for diagnosis (96.0%). Extracellular contrast agents (69.5%) were the most commonly used MRI contrast agents and Lumason/SonoVue (31.1%) is the most commonly used contrast-enhanced ultrasound contrast agent. A majority (94.0%) of respondents use ancillary imaging features for assessment of liver lesions in at-risk patients. Usage of diagnostic imaging systems for HCC varied by region. RECIST or mRECIST criteria were most commonly used for assessing HCC treatment response (48.3%). Most respondents agreed that a standardized classification for the diagnosis of HCC is needed (68.9%) and that an atlas and lexicon would help improve inter-reader agreement (71.5%). CONCLUSION: Practices and recommendations for imaging of HCC vary between geographical regions. Future efforts to develop a unified system should address regional differences and potential barriers for adoption of a standardized diagnostic system for HCC.

Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment.

We propose a random forest classifier for identifying adequacy of liver MR images using handcrafted (HC) features and deep convolutional neural networks (CNNs), and analyze the relative role of these two components in relation to the training sample size. The HC features, specifically developed for this application, include Gaussian mixture models, Euler characteristic curves and texture analysis. Using HC features outperforms the CNN for smaller sample sizes and with increased interpretability. On the other hand, with enough training data, the combined classifier outperforms the models trained with HC features or CNN features alone. These results illustrate the added value of HC features with respect to CNNs, especially when insufficient data is available, as is often found in clinical studies.