Recently, sarcopenia has garnered renewed interest. Sarcopenia is a disease characterized by decreased skeletal muscle mass and strength/function, which can impair the quality of life and increase physical disability, adverse metabolic effects, and mortality. Imaging tools for evaluating and diagnosing sarcopenia have developed rapidly. Radiologists should be aware of sarcopenia and its clinical implications. We review current knowledge about sarcopenia, its pathophysiological impact, and advantages and disadvantages of methods for evaluation of sarcopenia focusing on body composition imaging modalities such as whole-body dual-energy X-ray absorptiometry, CT, and MRI. Controversial issues are discussed, including the lack of consensus and standardization of the disease definition, imaging modality, measurement methods, and diagnostic cutoff points.

Purpose To develop and validate a deep learning system (DLS) for staging liver fibrosis by using CT images in the liver. Materials and Methods DLS for CT-based staging of liver fibrosis was created by using a development data set that included portal venous phase CT images in 7461 patients with pathologically confirmed liver fibrosis. The diagnostic performance of the DLS was evaluated in separate test data sets for 891 patients. The influence of patient characteristics and CT techniques on the staging accuracy of the DLS was evaluated by logistic regression analysis. In a subset of 421 patients, the diagnostic performance of the DLS was compared with that of the radiologist's assessment, aminotransferase-to-platelet ratio index (APRI), and fibrosis-4 index by using the area under the receiver operating characteristic curve (AUROC) and Obuchowski index. Results In the test data sets, the DLS had a staging accuracy of 79.4% (707 of 891) and an AUROC of 0.96, 0.97, and 0.95 for diagnosing significant fibrosis (F2-4), advanced fibrosis (F3-4), and cirrhosis (F4), respectively. At multivariable analysis, only pathologic fibrosis stage significantly affected the staging accuracy of the DLS (P = .016 and .013 for F1 and F2, respectively, compared with F4), whereas etiology of liver disease and CT technique did not. The DLS (Obuchowski index, 0.94) outperformed the radiologist's interpretation, APRI, and fibrosis-4 index (Obuchowski index range, 0.71-0.81; P ˂ .001) for staging liver fibrosis. Conclusion The deep learning system allows for accurate staging of liver fibrosis by using CT images. © RSNA, 2018 Online supplemental material is available for this article.

3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;45:1637-1647.

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Background The impact of sarcopenia on clinical outcomes of coronavirus disease 2019 (COVID-19) is not clearly determined yet. We aimed to investigate the association between baseline sarcopenia and clinical outcomes in patients with COVID-19. Methods All hospitalized adult patients with COVID-19 who had baseline chest computed tomography (CT) scans at a Korean university hospital from February 2020 to May 2020 were included. The main outcome was time from hospital admission to discharge. Death was considered as a competing risk for discharge. Baseline skeletal muscle cross-sectional area at the level of the 12th thoracic vertebra was measured from chest CT scans. The lowest quartile of skeletal muscle index (skeletal muscle cross-sectional area divided by height-squared) was defined as sarcopenia. Results Of 121 patients (median age, 62 years; 44 men; 29 sarcopenic), 7 patients died and 86 patients were discharged during the 60-day follow-up. Patients with sarcopenia showed a longer time to discharge (median, 55 vs. 28 days; p<0.001) and a higher incidence of death (17.2% vs. 2.2%; p=0.004) than those without sarcopenia. Baseline sarcopenia was an independent predictor of delayed hospital discharge (adjusted hazard ratio [aHR], 0.47; 95% CI, 0.23-0.96), but was not independently associated with mortality in patients with COVID-19 (aHR, 3.80; 95% CI, 0.48-30.26). The association between baseline sarcopenia and delayed hospital discharge was consistent in subgroups stratified by age, sex, comorbidities, and severity of COVID-19. Conclusion Baseline sarcopenia was independently associated with prolonged hospital stay in patients with COVID-19. Sarcopenia could be a prognostic marker in COVID-19.

CAD can improve the diagnostic performance of DCE-MRI in prostate cancer detection, which may vary according to zonal anatomy.

BackgroundQuantification of abdominal muscle mass by cross-sectional imaging has been increasingly used to diagnose sarcopenia; however, the technical method for quantification has not been standardized yet. We aimed to determine an optimal method to measure the abdominal muscle area.MethodsAmong 50 consecutive subjects who underwent abdominal CT and MRI for possible liver donation, total abdominal muscle area (TAMA) and total psoas muscle area (TPA) at the L3 inferior endplate level were measured by two blinded readers. Inter-scan agreement between CT and MRI and inter-reader agreement between the two readers were evaluated using intraclass correlation coefficient (ICC) and within-subject coefficient of variation (WSCV). To evaluate the effect of measurement level, one reader measured TAMA and TPA at six levels from the L2 to L4 vertebral bodies.ResultsTAMA was a more reliable biomarker than TPA in terms of inter-scan agreement (ICC: 0.928 vs. 0.788 for reader 1 and 0.853 vs. 0.821 for reader 2, respectively; WSCV: 8.3% vs. 23.4% for reader 1 and 10.4% vs. 22.3% for reader 2, respectively) and inter-reader agreement (ICC: 0.986 vs. 0.886 for CT and 0.865 vs. 0.669 for MRI, respectively; WSCV: 8.2% vs. 16.0% for CT and 11.6% vs. 29.7% for MRI, respectively). In terms of the measurement level, TAMA did not differ from the L2inf to L4inf levels, whereas TPA increased with a decrease in measurement level.ConclusionsTAMA is a better biomarker than TPA in terms of inter-scan and inter-reader agreement and robustness to the measurement level. CT was a more reliable imaging modality than MRI. Our results support the use of TAMA measured by CT as a standard biomarker for abdominal muscle area measurement.