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.
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep‐learning algorithms ...have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mainly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we discuss the general context of radiology and opportunities for application of deep‐learning algorithms. We also introduce basic concepts of deep learning, including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review a broad range of deep‐learning algorithms being utilized. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future.
Level of Evidence: 3
Technical Efficacy: Stage 1
J. Magn. Reson. Imaging 2019;49:939–954.
Gadolinium‐based contrast agents (GBCAs) have been used in magnetic resonance imaging (MRI) since the 1980s and are now administered in up to 35% of all MRI examinations. While GBCAs were initially ...felt to carry minimal risk, the subsequent identification of GBCAs as the key etiologic factor in the development of nephrogenic systemic fibrosis (NSF) has raised concerns about the broader health impacts of gadolinium exposure. Clinicians, radiologists, and patients should be aware of the most up‐to‐date data pertaining to the risks of GBCA administration. Specific issues covered in this review article include immediate adverse reactions; pregnancy and lactation; and gadolinium deposition and toxicity, with a special focus on NSF. Practice recommendations based on the presented data, as well as current professional society guidelines, are provided for each section.
Level of Evidence: 1
Technical Efficacy: Stage 5
J. MAGN. RESON. IMAGING 2017;46:338–353
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.
The 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.
We 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.
Of 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.
In 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) 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.
Non-alcoholic steatohepatitis (NASH) is characterised by hepatic steatosis, inflammation, hepatocellular injury, and progressive liver fibrosis. Resmetirom (MGL-3196) is a liver-directed, orally ...active, selective thyroid hormone receptor-β agonist designed to improve NASH by increasing hepatic fat metabolism and reducing lipotoxicity. We aimed to assess the safety and efficacy of resmetirom in patients with NASH.
MGL-3196-05 was a 36-week randomised, double-blind, placebo-controlled study at 25 centres in the USA. Adults with biopsy confirmed NASH (fibrosis stages 1–3) and hepatic fat fraction of at least 10% at baseline when assessed by MRI-proton density fat fraction (MRI-PDFF) were eligible. Patients were randomly assigned 2:1 by a computer-based system to receive resmetirom 80 mg or matching placebo, orally once a day. Serial hepatic fat measurements were obtained at weeks 12 and 36, and a second liver biopsy was obtained at week 36. The primary endpoint was relative change in MRI-PDFF assessed hepatic fat compared with placebo at week 12 in patients who had both a baseline and week 12 MRI-PDFF. This trial is registered with ClinicalTrials.gov, number NCT02912260.
348 patients were screened and 84 were randomly assigned to resmetirom and 41 to placebo at 18 sites in the USA. Resmetirom-treated patients (n=78) showed a relative reduction of hepatic fat compared with placebo (n=38) at week 12 (−32·9% resmetirom vs −10·4% placebo; least squares mean difference −22·5%, 95% CI −32·9 to −12·2; p<0·0001) and week 36 (−37·3% resmetirom n=74 vs −8·5 placebo n=34; −28·8%, −42·0 to −15·7; p<0·0001). Adverse events were mostly mild or moderate and were balanced between groups, except for a higher incidence of transient mild diarrhoea and nausea with resmetirom.
Resmetirom treatment resulted in significant reduction in hepatic fat after 12 weeks and 36 weeks of treatment in patients with NASH. Further studies of resmetirom will allow assessment of safety and effectiveness of resmetirom in a larger number of patients with NASH with the possibility of documenting associations between histological effects and changes in non-invasive markers and imaging.
Madrigal Pharmaceuticals.
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