Search

BackgroundNon-alcoholic steatohepatitis (NASH) is a progressive form of non-alcoholic fatty liver disease (NAFLD) associated with steatosis, hepatocellular injury, inflammation and fibrosis. In a Phase 2 trial in adults with NASH (NCT02912260), resmetirom, an orally administered, liver-targeted thyroid hormone receptor-β selective agonist, significantly reduced hepatic fat (via imaging) and resolved NASH without worsening fibrosis (via liver biopsy) in a significant number of patients compared with placebo.AimsTo present the design of the Phase 3 MAESTRO clinical programme evaluating resmetirom for treatment of NASH (MAESTRO-NAFLD-1 [NCT04197479], MAESTRO-NAFLD-OLE [NCT04951219], MAESTRO-NASH [NCT03900429], MAESTRO-NASH-OUTCOMES [NCT05500222]).MethodsMAESTRO-NASH is a pivotal serial biopsy trial in up to 2000 adults with biopsy-confirmed at-risk NASH. Patients are randomised to a once-daily oral placebo, 80 mg resmetirom, or 100 mg resmetirom. Liver biopsies are conducted at screening, week 52 and month 54. MAESTRO-NAFLD-1 is a 52-week safety trial in ~1400 adults with NAFLD/presumed NASH (based on non-invasive testing); ~700 patients from MAESTRO-NAFLD-1 are enrolled in MAESTRO-NAFLD-OLE, a 52-week active treatment extension to further evaluate safety. MAESTRO-NASH-OUTCOMES is enrolling 700 adults with well-compensated NASH cirrhosis to evaluate the potential for resmetirom to slow progression to hepatic decompensation events. Non-invasive tests (biomarkers, imaging) are assessed longitudinally throughout, in addition to validated patient-reported outcomes.ConclusionThe MAESTRO clinical programme was designed in conjunction with regulatory authorities to support approval of resmetirom for treatment of NASH. The surrogate endpoints, based on week 52 liver biopsy, serum biomarkers and imaging, are confirmed by long-term clinical liver-related outcomes in MAESTRO-NASH (month 54) and MAESTRO-NASH-OUTCOMES (time to event).

Nonalcoholic fatty liver disease (NAFLD) is a common liver disease, with a worldwide prevalence of 25%. NAFLD is a spectrum that includes nonalcoholic fatty liver defined histologically by isolated hepatocytes steatosis without inflammation and nonalcoholic steatohepatitis (NASH) is the inflammatory subtype of NAFLD and is associated with disease progression, development of cirrhosis, and increased rates of liver-specific and overall mortality. The differentiation between NAFLD and NASH as well as staging NASH are important yet challenging clinical problems. Liver biopsy is currently the standard for disease diagnosis and fibrosis staging. However, this procedure is invasive, costly, and cannot be used for longitudinal monitoring. Therefore, several noninvasive quantitative imaging biomarkers have been proposed that can estimate the severity of hepatic steatosis and fibrosis. Despite this, noninvasive diagnosis of NASH and accurate risk stratification remain unmet needs. In this work, the most relevant available imaging biomarkers are reviewed and their application in patients with NAFLD are discussed.

Background & aimsNon-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.MethodsAdult 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.ResultsTreatment 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).ConclusionsIn 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 numberNCT01265498.Impact and implicationsAlthough 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.

PurposeChemical shift-encoded MRI (CSE-MRI) is well-established to quantify proton density fat fraction (PDFF) as a quantitative biomarker of hepatic steatosis. However, temperature is known to bias PDFF estimation in phantom studies. In this study, strategies were developed and evaluated to correct for the effects of temperature on PDFF estimation through simulations, temperature-controlled experiments, and a multi-center, multi-vendor phantom study.Theory and methodsA technical solution that assumes and automatically estimates a uniform, global temperature throughout the phantom is proposed. Computer simulations modeled the effect of temperature on PDFF estimation using magnitude-, complex-, and hybrid-based CSE-MRI methods. Phantom experiments were performed to assess the temperature correction on PDFF estimation at controlled phantom temperatures. To assess the temperature correction method on a larger scale, the proposed method was applied to data acquired as part of a nine-site multi-vendor phantom study and compared to temperature-corrected PDFF estimation using an a priori guess for ambient room temperature.ResultsSimulations and temperature-controlled experiments show that as temperature deviates further from the assumed temperature, PDFF bias increases. Using the proposed correction method and a reasonable a priori guess for ambient temperature, PDFF bias and variability were reduced using magnitude-based CSE-MRI, across MRI systems, field strengths, protocols, and varying phantom temperature. Complex and hybrid methods showed little PDFF bias and variability both before and after correction.ConclusionCorrection for temperature reduces temperature-related PDFF bias and variability in phantoms across MRI vendors, sites, field strengths, and protocols for magnitude-based CSE-MRI, even without a priori information about the temperature.

Background Proton density fat fraction (PDFF) estimated by using chemical shift-encoded (CSE) MRI is an accepted imaging biomarker of hepatic steatosis. This work aims to promote standardized use of CSE MRI to estimate PDFF. Purpose To assess the accuracy of CSE MRI methods for estimating PDFF by determining the linearity and range of bias observed in a phantom. Materials and Methods In this prospective study, a commercial phantom with 12 vials of known PDFF values were shipped across nine U.S. centers. The phantom underwent 160 independent MRI examinations on 27 1.5-T and 3.0-T systems from three vendors. Two three-dimensional CSE MRI protocols with minimal T1 bias were included: vendor and standardized. Each vendor's confounder-corrected complex or hybrid magnitude-complex based reconstruction algorithm was used to generate PDFF maps in both protocols. The Siemens reconstruction required a configuration change to correct for water-fat swaps in the phantom. The MRI PDFF values were compared with the known PDFF values by using linear regression with mixed-effects modeling. The 95% CIs were calculated for the regression slope (ie, proportional bias) and intercept (ie, constant bias) and compared with the null hypothesis (slope = 1, intercept = 0). Results Pooled regression slope for estimated PDFF values versus phantom-derived reference PDFF values was 0.97 (95% CI: 0.96, 0.98) in the biologically relevant 0%-47.5% PDFF range. The corresponding pooled intercept was -0.27% (95% CI: -0.50%, -0.05%). Across vendors, slope ranges were 0.86-1.02 (vendor protocols) and 0.97-1.0 (standardized protocol) at 1.5 T and 0.91-1.01 (vendor protocols) and 0.87-1.01 (standardized protocol) at 3.0 T. The intercept ranges (absolute PDFF percentage) were -0.65% to 0.18% (vendor protocols) and -0.69% to -0.17% (standardized protocol) at 1.5 T and -0.48% to 0.10% (vendor protocols) and -0.78% to -0.21% (standardized protocol) at 3.0 T. Conclusion Proton density fat fraction estimation derived from three-dimensional chemical shift-encoded MRI in a commercial phantom was accurate across vendors, imaging centers, and field strengths, with use of the vendors' product acquisition and reconstruction software. © RSNA, 2021 See also the editorial by Dyke in this issue.

PurposeThe SARS-CoV-2 pandemic has drastically disrupted radiology in-person education. The purpose of this study was to assess the implementation of a virtual teaching method using available technology and its role in the continuity of education of practicing radiologists and trainees during the pandemic.MethodsThe authors created the Online Liver Imaging Course (OLIC) that comprised 28 online comprehensive lectures delivered in real-time and on-demand over six weeks. Radiologists and radiology trainees were asked to register to attend the live sessions. At the end of the course, we conducted a 46-question survey among registrants addressing their training level, perception of virtual conferencing, and evaluation of the course content.ResultsOne thousand four hundred and thirty four radiologists and trainees completed interest sign up forms before the start of the course with the first webinar having the highest number of live attendees (343 people). On average, there were 89 live participants per session and 750 YouTube views per recording (as of July 9, 2020). After the end of the course, 487 attendees from 37 countries responded to the postcourse survey for an overall response rate of (33%). Approximately (63%) of participants were practicing radiologists while (37%) were either fellows or residents and rarely medical students. The overwhelming majority (97%) found the OLIC webinar series to be beneficial. Essentially all attendees felt that the webinar sessions met (43%) or exceeded (57%) their expectations. When asked about their perception of virtual conferences after attending OLIC lectures, almost all attendees (99%) enjoyed the virtual conference with a majority (61%) of the respondents who enjoyed the virtual format more than in-person conferences, while (38%) enjoyed the webinar format but preferred in-person conferences. When asked about the willingness to attend virtual webinars in the future, (84%) said that they would attend future virtual conferences even if in-person conferences resume while (15%) were unsure.ConclusionThe success of the OLIC, attributed to many factors, indicates that videoconferencing technology provides an inexpensive alternative to in-person radiology conferences. The positive responses to our postcourse survey suggest that virtual education will remain to stay. Educational institutions and scientific societies should foster such models.

PurposeTo develop a deep convolutional neural network (CNN) model to categorize multiphase CT and MRI liver observations using the liver imaging reporting and data system (LI-RADS) (version 2014).MethodsA pre-existing dataset comprising 314 hepatic observations (163 CT, 151 MRI) with corresponding diameters and LI-RADS categories (LR-1-5) assigned in consensus by two LI-RADS steering committee members was used to develop two CNNs: pre-trained network with an input of triple-phase images (training with transfer learning) and custom-made network with an input of quadruple-phase images (training from scratch). The dataset was randomly split into training, validation, and internal test sets (70:15:15 split). The overall accuracy and area under receiver operating characteristic curve (AUROC) were assessed for categorizing LR-1/2, LR-3, LR-4, and LR-5. External validation was performed for the model with the better performance on the internal test set using two external datasets (EXT-CT and EXT-MR: 68 and 44 observations, respectively).ResultsThe transfer learning model outperformed the custom-made model: overall accuracy of 60.4% and AUROCs of 0.85, 0.90, 0.63, 0.82 for LR-1/2, LR-3, LR-4, LR-5, respectively. On EXT-CT, the model had an overall accuracy of 41.2% and AUROCs of 0.70, 0.66, 0.60, 0.76 for LR-1/2, LR-3, LR-4, LR-5, respectively. On EXT-MR, the model had an overall accuracy of 47.7% and AUROCs of 0.88, 0.74, 0.69, 0.79 for LR-1/2, LR-3, LR-4, LR-5, respectively.ConclusionOur study shows the feasibility of CNN for assigning LI-RADS categories from a relatively small dataset but highlights the challenges of model development and validation.

The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver imaging, created specifically for patients at risk for hepatocellular carcinoma. Over the past years, LI-RADS has been progressively implemented into clinical practice, but pitfalls remain related to user error and inherent limitations of the system. User pitfalls include the inappropriate application of LI-RADS to a low-risk patient population, incorrect measurement techniques, inaccurate assumptions about LI-RADS requirements, and improper usage of LI-RADS terminology and categories. System pitfalls include areas of discordance with the Organ Procurement and Transplantation Network (OPTN) as well as pitfalls related to rare ancillary features. This article reviews common user pitfalls in applying LI-RADS v2018 and how to avoid preventable errors and also highlights deficiencies of the current version of LI-RADS and how it might be improved in the future. Level of Evidence:3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2019;50:1673-1686.

Background Ferumoxytol is approved for use in the treatment of iron deficiency anemia, but it can serve as an alternative to gadolinium-based contrast agents. On the basis of postmarketing surveillance data, the Food and Drug Administration issued a black box warning regarding the risks of rare but serious acute hypersensitivity reactions during fast high-dose injection (510 mg iron in 17 seconds) for therapeutic use. Whereas single-center safety data for diagnostic use have been positive, multicenter data are lacking. Purpose To report multicenter safety data for off-label diagnostic ferumoxytol use. Materials and Methods The multicenter ferumoxytol MRI registry was established as an open-label nonrandomized surveillance databank without industry involvement. Each center monitored all ferumoxytol administrations, classified adverse events (AEs) using the National Cancer Institute Common Terminology Criteria for Adverse Events (grade 1-5), and assessed the relationship of AEs to ferumoxytol administration. AEs related to or possibly related to ferumoxytol injection were considered adverse reactions. The core laboratory adjudicated the AEs and classified them with the American College of Radiology (ACR) classification. Analysis of variance was used to compare vital signs. Results Between January 2003 and October 2018, 3215 patients (median age, 58 years; range, 1 day to 96 years; 1897 male patients) received 4240 ferumoxytol injections for MRI. Ferumoxytol dose ranged from 1 to 11 mg per kilogram of body weight (≤510 mg iron; rate ≤45 mg iron/sec). There were no systematic changes in vital signs after ferumoxytol administration (P > .05). No severe, life-threatening, or fatal AEs occurred. Eighty-three (1.9%) of 4240 AEs were related or possibly related to ferumoxytol infusions (75 mild [1.8%], eight moderate [0.2%]). Thirty-one AEs were classified as allergiclike reactions using ACR criteria but were consistent with minor infusion reactions observed with parenteral iron. Conclusion Diagnostic ferumoxytol use was well tolerated, associated with no serious adverse events, and implicated in few adverse reactions. Registry results indicate a positive safety profile for ferumoxytol use in MRI. © RSNA, 2019 Online supplemental material is available for this article.

Loss of muscle mass and function, or sarcopenia, is a common feature of cirrhosis and contributes significantly to morbidity and mortality in this population. Sarcopenia is a main indicator of adverse outcomes in this population, including poor quality of life, hepatic decompensation, mortality in patients with cirrhosis evaluated for liver transplantation (LT), longer hospital and intensive care unit stay, higher incidence of infection following LT, and higher overall health care cost. Although it is clear that muscle mass is an important predictor of LT outcomes, many questions remain, including the best modality for assessing muscle mass, the optimal cut-off values for sarcopenia, the ideal timing and frequency of muscle mass assessment, and how to best incorporate the concept of sarcopenia into clinical decision making. For these reasons, we assembled a group of experts to form the North American Working Group on Sarcopenia in Liver Transplantation to use evidence from the medical literature to address these outstanding questions regarding sarcopenia in LT. We believe sarcopenia assessment should be considered in all patients with cirrhosis evaluated for liver transplantation. Skeletal muscle index (SMI) assessed by computed tomography constitutes the best-studied technique for assessing sarcopenia in patients with cirrhosis. Cut-off values for sarcopenia, defined as SMI < 50 cm2 /m2 in male and < 39 cm2 /m2 in female patients, constitute the validated definition for sarcopenia in patients with cirrhosis. Conclusion: The management of sarcopenia requires a multipronged approach including nutrition, exercise, and additional pharmacological therapy as deemed necessary. Future studies should evaluate whether recovery of sarcopenia with nutritional management in combination with an exercise program is sustainable as well as how improvement in muscle mass might be associated with improvement in clinical outcomes.

BackgroundThe liver R2* value is widely used as a measure of liver iron but may be confounded by the presence of hepatic steatosis and other covariates.PurposeTo identify the most influential covariates for liver R2* values in patients with nonalcoholic fatty liver disease (NAFLD).Study typeRetrospective analysis of prospectively acquired data.PopulationBaseline data from 204 subjects enrolled in NAFLD/NASH (nonalcoholic steatohepatitis) treatment trials.Field strength1.5T and 3T; chemical-shift encoded multiecho gradient echo.AssessmentCorrelation between liver proton density fat fraction and R2*; assessment for demographic, metabolic, laboratory, MRI-derived, and histological covariates of liver R2*.Statistical testsPearson's and Spearman's correlations; univariate analysis; gradient boosting machines (GBM) multivariable machine-learning method.ResultsHepatic proton density fat fraction (PDFF) was the most strongly correlated covariate for R2* at both 1.5T (r = 0.652, P

Background & aimsThe Liver Imaging Reporting and Data System (LI-RADS) categorizes observations from imaging analyses of high-risk patients based on the level of suspicion for hepatocellular carcinoma (HCC) and overall malignancy. The categories range from definitely benign (LR-1) to definitely HCC (LR-5), malignancy (LR-M), or tumor in vein (LR-TIV) based on findings from computed tomography or magnetic resonance imaging. However, the actual percentage of HCC and overall malignancy within each LI-RADS category is not known. We performed a systematic review to determine the percentage of observations in each LI-RADS category for computed tomography and magnetic resonance imaging that are HCCs or malignancies.MethodsWe searched the MEDLINE, Embase, Cochrane CENTRAL, and Scopus databases from 2014 through 2018 for studies that reported the percentage of observations in each LI-RADS v2014 and v2017 category that were confirmed as HCCs or other malignancies based on pathology, follow-up imaging analyses, or response to treatment (reference standard). Data were assessed on a per-observation basis. Random-effects models were used to determine the pooled percentages of HCC and overall malignancy for each LI-RADS category. Differences between categories were compared by analysis of variance of logit-transformed percentage of HCC and overall malignancy. Risk of bias and concerns about applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool.ResultsOf 454 studies identified, 17 (all retrospective studies) were included in the final analysis, consisting of 2760 patients, 3556 observations, and 2482 HCCs. The pooled percentages of observations confirmed as HCC and overall malignancy, respectively, were 94% (95% confidence interval [CI] 92%-96%) and 97% (95% CI 95%-99%) for LR-5, 74% (95% CI 67%-80%) and 80% (95% CI 75%-85%) for LR-4, 38% (95% CI 31%-45%) and 40% (95% CI 31%-50%) for LR-3, 13% (95% CI 8%-22%) and 14% (95% CI 9%-21%) for LR-2, 79% (95% CI 63%-89%) and 92% (95% CI 77%-98%) for LR-TIV, and 36% (95% CI 26%-48%) and 93% (95% CI 87%-97%) for LR-M. No malignancies were found in the LR-1 group. The percentage of HCCs and overall malignancies confirmed differed significantly among LR groups 2-5 (P < .00001). Patient selection was the most frequent factor that affected bias risk, because of verification bias and case-control study design.ConclusionsIn a systematic review, we found that increasing LI-RADS categories contained increasing percentages of HCCs and overall malignancy based on reference standard confirmation. Of observations categorized as LR-M, 93% were malignancies and 36% were confirmed as HCCs. The percentage of HCCs found in the LR-2 and LR-3 categories indicate the need for a more active management strategy than currently recommended. Prospective studies are needed to validate these findings. PROSPERO number CRD42018087441.

The Liver Imaging Reporting and Data System (LI-RADS®) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver observations in individuals at high risk for hepatocellular carcinoma (HCC). LI-RADS is supported and endorsed by the American College of Radiology (ACR). Upon its initial release in 2011, LI-RADS applied only to liver observations identified at CT or MRI. It has since been refined and expanded over multiple updates to now also address ultrasound-based surveillance, contrast-enhanced ultrasound for HCC diagnosis, and CT/MRI for assessing treatment response after locoregional therapy. The LI-RADS 2018 version was integrated into the HCC diagnosis, staging, and management practice guidance of the American Association for the Study of Liver Diseases (AASLD). This article reviews the major LI-RADS updates since its 2011 inception and provides an overview of the currently published LI-RADS algorithms.

The Liver Imaging Reporting and Data System (LI-RADS) is composed of four individual algorithms intended to standardize the lexicon, as well as reporting and care, in patients with or at risk for hepatocellular carcinoma in the context of surveillance with US; diagnosis with CT, MRI, or contrast material-enhanced US; and assessment of treatment response with CT or MRI. This report provides a broad overview of LI-RADS, including its historic development, relationship to other imaging guidelines, composition, aims, and future directions. In addition, readers will understand the motivation for and key components of the 2018 update.

Background: LI‐RADS version 2018 (v2018) is used for non‐invasive diagnosis of hepatocellular carcinoma (HCC). A recently proposed modification (known as mLI‐RADS) demonstrated improved sensitivity while maintaining specificity and positive predictive value (PPV) of LI‐RADS category 5 (definite HCC) for HCC. However, mLI‐RADS requires multicenter validation. Purpose: To evaluate the performance of v2018 and mLI‐RADS for liver lesions in a large, heterogeneous, multi‐national cohort of patients at risk for HCC. Study Type: Systematic review and meta‐analysis using individual participant data (IPD) [Study Protocol: https://osf.io/duys4]. Population: 2223 observations from 1817 patients (includes all LI‐RADS categories; females = 448, males = 1361, not reported = 8) at elevated risk for developing HCC (based on LI‐RADS population criteria) from 12 retrospective studies. Field Strength/Sequence: 1.5T and 3T; complete liver MRI with gadoxetate disodium, including axial T2w images and dynamic axial fat‐suppressed T1w images precontrast and in the arterial, portal venous, transitional, and hepatobiliary phases. Diffusion‐weighted imaging was used when available. Assessment: Liver observations were categorized using v2018 and mLI‐RADS. The diagnostic performance of each system's category 5 (LR‐5 and mLR‐5) for HCC were compared. Statistical Tests: The Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS‐2 was applied to determine risk of bias and applicability. Diagnostic performances were assessed using the likelihood ratio test for sensitivity and specificity and the Wald test for PPV. The significance level was P < 0.05. Results: 17% (2/12) of the studies were considered low risk of bias (244 liver observations; 164 patients). When compared to v2018, mLR‐5 demonstrated higher sensitivity (61.3% vs. 46.5%, P < 0.001), similar PPV (85.3% vs. 86.3%, P = 0.89), and similar specificity (85.8% vs. 90.8%, P = 0.16) for HCC. Data Conclusion: This study confirms mLR‐5 has higher sensitivity than LR‐5 for HCC identification, while maintaining similar PPV and specificity, validating the mLI‐RADS proposal in a heterogeneous, international cohort. Level of Evidence: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Background & aimsPatients with nonalcoholic fatty liver disease (NAFLD) or nonalcoholic steatohepatitis (NASH) often require histologic assessment via liver biopsy. Magnetic resonance imaging (MRI)-based methods for measuring liver triglycerides based on proton density fat fraction (PDFF) are increasingly used as a noninvasive tool to identify patients with hepatic steatosis and to assess for change in liver fat over time. We aimed to determine whether MRI-PDFF accurately reflects a variety of liver histology features in patients with NAFLD or NASH.MethodsWe performed a retrospective analysis of pooled data from 3 phase 2a trials of pharmacotherapies for NAFLD or NASH. We collected baseline clinical, laboratory, and histopathology data on all subjects who had undergone MRI analysis in 1 of the trials. We assessed the relationship between liver PDFF values and liver histologic findings using correlation and area under the receiver operating characteristic (AUROC) analyses. As an ancillary analysis, we also simulated a clinical trial selection process and calculated subject exclusion rates and differences in population characteristics caused by PDFF inclusion thresholds of 6% to 15%.ResultsIn 370 subjects, the mean baseline PDFF was 17.4% ± 8.6%. Baseline PDFF values correlated with several histopathology parameters, including steatosis grade (r = 0.78; P < .001), NAFLD activity score (NAS, r = 0.54; P < .001), and fibrosis stage (r = -0.59; P 4 or advanced fibrosis. Although MRI-PDFF is an important imaging biomarker for continued evaluation of this patient population, liver biopsy analysis is still necessary.

The Liver Imaging and Reporting Data System (LI-RADS) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver imaging with the overarching goal of improving communication, clinical care, education, and research relating to patients at risk for or diagnosed with hepatocellular carcinoma (HCC). In 2018, the American Association for the Study of Liver Diseases (AASLD) integrated LI-RADS into its clinical practice guidance for the imaging-based diagnosis of HCC. The harmonization between the AASLD and LI-RADS diagnostic imaging criteria required minor modifications to the recently released LI-RADS v2017 guidelines, necessitating a LI-RADS v2018 update. This article provides an overview of the key changes included in LI-RADS v2018 as well as a look at the LI-RADS v2018 diagnostic algorithm and criteria, technical recommendations, and management suggestions. Substantive changes in LI-RADS v2018 are the removal of the requirement for visibility on antecedent surveillance ultrasound for LI-RADS 5 (LR-5) categorization of 10-19 mm observations with nonrim arterial phase hyper-enhancement and nonperipheral "washout", and adoption of the Organ Procurement and Transplantation Network definition of threshold growth (≥ 50% size increase of a mass in ≤ 6 months). Nomenclatural changes in LI-RADS v2018 are the removal of -us and -g as LR-5 qualifiers.

ObjectiveThe purposes of this article are to review a variety of pitfalls in liver imaging that can lead to the inaccurate diagnosis of focal hepatic lesions in cirrhosis, to describe the pathophysiologic processes of these pitfalls, and to provide specific clues for achieving the correct diagnoses.ConclusionCirrhosis complicates liver imaging. The distortion and replacement of normal liver parenchyma by fibrous and regenerative tissue can change the typical appearance of many benign lesions, causing them to be misinterpreted as malignancy. In addition, the high incidence and prevalence of hepatocellular carcinoma among patients with cirrhosis put radiologists on high alert for any suspicious findings, especially because not all hepatocellular carcinomas have a typical imaging appearance.

The computed tomography / magnetic resonance imaging (CT/MRI) Liver Imaging Reporting & Data System (LI-RADS) is a standardized system for diagnostic imaging terminology, technique, interpretation, and reporting in patients with or at risk for developing hepatocellular carcinoma (HCC). Using diagnostic algorithms and tables, the system assigns to liver observations category codes reflecting the relative probability of HCC or other malignancies. This review article provides an overview of the 2017 version of CT/MRI LI-RADS with a focus on MRI. The main LI-RADS categories and their application will be described. Changes and updates introduced in this version of LI-RADS will be highlighted, including modifications to the diagnostic algorithm and to the optional application of ancillary features. Comparisons to other major diagnostic systems for HCC will be made, emphasizing key similarities, differences, strengths, and limitations. In addition, this review presents the new Treatment Response algorithm, while introducing the concepts of MRI nonviability and viability. Finally, planned future directions for LI-RADS will be outlined.Level of evidence5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;47:1459-1474.

Purpose To determine the linearity, bias, and precision of hepatic proton density fat fraction (PDFF) measurements by using magnetic resonance (MR) imaging across different field strengths, imager manufacturers, and reconstruction methods. Materials and Methods This meta-analysis was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A systematic literature search identified studies that evaluated the linearity and/or bias of hepatic PDFF measurements by using MR imaging (hereafter, MR imaging-PDFF) against PDFF measurements by using colocalized MR spectroscopy (hereafter, MR spectroscopy-PDFF) or the precision of MR imaging-PDFF. The quality of each study was evaluated by using the Quality Assessment of Studies of Diagnostic Accuracy 2 tool. De-identified original data sets from the selected studies were pooled. Linearity was evaluated by using linear regression between MR imaging-PDFF and MR spectroscopy-PDFF measurements. Bias, defined as the mean difference between MR imaging-PDFF and MR spectroscopy-PDFF measurements, was evaluated by using Bland-Altman analysis. Precision, defined as the agreement between repeated MR imaging-PDFF measurements, was evaluated by using a linear mixed-effects model, with field strength, imager manufacturer, reconstruction method, and region of interest as random effects. Results Twenty-three studies (1679 participants) were selected for linearity and bias analyses and 11 studies (425 participants) were selected for precision analyses. MR imaging-PDFF was linear with MR spectroscopy-PDFF (R2 = 0.96). Regression slope (0.97; P < .001) and mean Bland-Altman bias (-0.13%; 95% limits of agreement: -3.95%, 3.40%) indicated minimal underestimation by using MR imaging-PDFF. MR imaging-PDFF was precise at the region-of-interest level, with repeatability and reproducibility coefficients of 2.99% and 4.12%, respectively. Field strength, imager manufacturer, and reconstruction method each had minimal effects on reproducibility. Conclusion MR imaging-PDFF has excellent linearity, bias, and precision across different field strengths, imager manufacturers, and reconstruction methods. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 2, 2017.

A radiologist issuing a LI-RADS category is, implicitly or explicitly, a member of a multidisciplinary team. If the definite diagnosis of a benign or malignant entity is not possible, categorizing the uncertainty as LR-2, -3, -4, or -M has important management implications. In this article, we discuss the range of options for management or further diagnostic testing and how a LR category may affect the choice between them. We then review recent published data regarding eventual diagnoses following assignment of a LR category.

This article provides a glimpse into the future of the Liver Imaging Reporting and Data System (LI-RADS), discussing the immediate and long-term plans for its continuing improvement and expansion. To complement the Core and Essentials components of the latest version of LI-RADS, a comprehensive manual will be released soon, and it will include technical recommendations, management guidance, as well as reporting instructions and templates. In this article, we briefly review the process by which LI-RADS has been developed until now, a process guided by a variable combination of data, expert opinion, and desire for congruency with other diagnostic systems in North America. We then look forward, envisioning that forthcoming updates to LI-RADS will occur regularly every 3 to 5 years, driven by emerging high-quality scientific evidence. We highlight some of the key knowledge and technology gaps that will need to be addressed to enable the needed refinements. We also anticipate future expansions in scope to meet currently unaddressed clinical needs. Finally, we articulate a vision for eventual unification of imaging system for HCC screening and surveillance, diagnosis and staging, and treatment response assessment.

Purpose To determine in a large multicenter multireader setting the interreader reliability of Liver Imaging Reporting and Data System (LI-RADS) version 2014 categories, the major imaging features seen with computed tomography (CT) and magnetic resonance (MR) imaging, and the potential effect of reader demographics on agreement with a preselected nonconsecutive image set. Materials and Methods Institutional review board approval was obtained, and patient consent was waived for this retrospective study. Ten image sets, comprising 38-40 unique studies (equal number of CT and MR imaging studies, uniformly distributed LI-RADS categories), were randomly allocated to readers. Images were acquired in unenhanced and standard contrast material-enhanced phases, with observation diameter and growth data provided. Readers completed a demographic survey, assigned LI-RADS version 2014 categories, and assessed major features. Intraclass correlation coefficient (ICC) assessed with mixed-model regression analyses was the metric for interreader reliability of assigning categories and major features. Results A total of 113 readers evaluated 380 image sets. ICC of final LI-RADS category assignment was 0.67 (95% confidence interval [CI]: 0.61, 0.71) for CT and 0.73 (95% CI: 0.68, 0.77) for MR imaging. ICC was 0.87 (95% CI: 0.84, 0.90) for arterial phase hyperenhancement, 0.85 (95% CI: 0.81, 0.88) for washout appearance, and 0.84 (95% CI: 0.80, 0.87) for capsule appearance. ICC was not significantly affected by liver expertise, LI-RADS familiarity, or years of postresidency practice (ICC range, 0.69-0.70; ICC difference, 0.003-0.01 [95% CI: -0.003 to -0.01, 0.004-0.02]. ICC was borderline higher for private practice readers than for academic readers (ICC difference, 0.009; 95% CI: 0.000, 0.021). Conclusion ICC is good for final LI-RADS categorization and high for major feature characterization, with minimal reader demographic effect. Of note, our results using selected image sets from nonconsecutive examinations are not necessarily comparable with those of prior studies that used consecutive examination series. © RSNA, 2017.

The Liver Imaging Reporting and Data System (LI-RADS) standardizes the interpretation, reporting, and data collection for imaging examinations in patients at risk for hepatocellular carcinoma (HCC). It assigns category codes reflecting relative probability of HCC to imaging-detected liver observations based on major and ancillary imaging features. LI-RADS also includes imaging features suggesting malignancy other than HCC. Supported and endorsed by the American College of Radiology (ACR), the system has been developed by a committee of radiologists, hepatologists, pathologists, surgeons, lexicon experts, and ACR staff, with input from the American Association for the Study of Liver Diseases and the Organ Procurement Transplantation Network/United Network for Organ Sharing. Development of LI-RADS has been based on literature review, expert opinion, rounds of testing and iteration, and feedback from users. This article summarizes and assesses the quality of evidence supporting each LI-RADS major feature for diagnosis of HCC, as well as of the LI-RADS imaging features suggesting malignancy other than HCC. Based on the evidence, recommendations are provided for or against their continued inclusion in LI-RADS. © RSNA, 2017 Online supplemental material is available for this article.

The Liver Imaging Reporting and Data System (LI-RADS) is a reporting system created for the standardized interpretation of liver imaging findings in patients who are at risk for hepatocellular carcinoma (HCC). This system was developed with the cooperative and ongoing efforts of an American College of Radiology-supported committee of diagnostic radiologists with expertise in liver imaging and valuable input from hepatobiliary surgeons, hepatologists, hepatopathologists, and interventional radiologists. In this article, the 2017 version of LI-RADS for computed tomography and magnetic resonance imaging is reviewed. Specific topics include the appropriate population for application of LI-RADS; technical recommendations for image optimization, including definitions of dynamic enhancement phases; diagnostic and treatment response categories; definitions of major and ancillary imaging features; criteria for distinguishing definite HCC from a malignancy that might be non-HCC; management options following LI-RADS categorization; and reporting. ©RSNA, 2017.