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Prevalence of Fatty Liver Disease and Hepatic Iron Overload in a Northeastern German Population by Using Quantitative MR Imaging.

ABSTRACT: Purpose To quantify liver fat and liver iron content by measurement of confounder-corrected proton density fat fraction (PDFF) and R2* and to identify clinical associations for fatty liver disease and liver iron overload and their prevalence in a large-scale population-based study. Materials and Methods From 2008 to 2013, 2561 white participants (1336 women; median age, 52 years; 25th and 75th quartiles, 42 and 62 years) were prospectively recruited to the Study of Health in Pomerania (SHIP). Complex chemical shift-encoded magnetic resonance (MR) examination of the liver was performed, from which PDFF and R2* were assessed. On the basis of previous histopathologic calibration, participants were stratified according to their liver fat and iron content as follows: none (PDFF, ≤5.1%; R2*, ≤41.0 sec^-1), mild (PDFF, >5.1%; R2*, >41 sec^-1), moderate (PDFF, >14.1%; R2*, >62.5 sec^-1), high (PDFF: >28.0%; R2*: >70.1 sec^-1). Prevalence of fatty liver diseases and iron overload was calculated (weighted by probability of participation). Clinical associations were identified by using boosting for generalized linear models. Results Median PDFF was 3.9% (range, 0.6%-41.5%). Prevalence of fatty liver diseases was 42.2% (1082 of 2561 participants); mild, 28.5% (730 participants); moderate, 12.0% (307 participants); high content, 1.8% (45 participants). Median R2* was 34.4 sec^-1 (range, 14.0-311.8 sec^-1). Iron overload was observed in 17.4% (447 of 2561 participants; mild, 14.7% [376 participants]; moderate, 0.8% [20 participants]; high content, 2.0% [50 participants]). Liver fat content correlated with waist-to-height ratio, alanine transaminase, uric acid, serum triglycerides, and blood pressure. Liver iron content correlated with mean serum corpuscular hemoglobin, male sex, and age. Conclusion In a white German population, the prevalence of fatty liver diseases and liver iron overload is 42.2% (1082 of 2561) and 17.4% (447 of 2561). Whereas liver fat is associated with predictors related to the metabolic syndrome, liver iron content is mainly associated with mean serum corpuscular hemoglobin. ^© RSNA, 2017 Online supplemental material is available for this article.

SUBMITTER: Kuhn JP

PROVIDER: S-EPMC5565690 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Extraction of liver parenchyma is an important step in the evaluation of R2*-based hepatic iron content (HIC). Traditionally, this is performed by radiologists via whole-liver contouring and T2*-thresholding to exclude hepatic vessels. However, the vessel exclusion process is iterative, time-consuming, and susceptible to interreviewer variability. PURPOSE:To implement and evaluate an automatic hepatic vessel exclusion and parenchyma extraction technique for accurate assessment of R2*-based HIC. STUDY TYPE:Retrospective analysis of clinical data. SUBJECTS:Data from 511 MRI exams performed on 257 patients were analyzed. FIELD STRENGTH/SEQUENCE:All patients were scanned on a 1.5T scanner using a multiecho gradient echo sequence for clinical monitoring of HIC. ASSESSMENT:An automated method based on a multiscale vessel enhancement filter was investigated for three input data types-contrast-optimized composite image, T2* map, and R2* map-to segment blood vessels and extract liver tissue for R2*-based HIC assessment. Segmentation and R2* results obtained using this automated technique were compared with those from a reference T2*-thresholding technique performed by a radiologist. STATISTICAL TESTS:The Dice similarity coefficient was used to compare the segmentation results between the extracted parenchymas, and linear regression and Bland-Altman analyses were performed to compare the R2* results, obtained with the automated and reference techniques. RESULTS:Mean liver R2* values estimated from all three filter-based methods showed excellent agreement with the reference method (slopes 1.04-1.05, R2 > 0.99, P < 0.001). Parenchyma areas extracted using the reference and automated methods had an average overlap area of 87-88%. The T2*-thresholding technique included small vessels and pixels at the vessel/tissue boundaries as parenchymal area, potentially causing a small bias (<5%) in R2* values compared to the automated method. DATA CONCLUSION:The excellent agreement between reference and automated hepatic vessel segmentation methods confirms the accuracy and robustness of the proposed method. This automated approach might improve the radiologist's workflow by reducing the interpretation time and operator dependence for assessing HIC, an important clinical parameter that guides iron overload management. LEVEL OF EVIDENCE:3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1542-1551.

Project description:BackgroundR2*-MRI is clinically used to noninvasively assess hepatic iron content (HIC) to guide potential iron chelation therapy. However, coexisting pathologies, such as fibrosis and steatosis, affect R2* measurements and may thus confound HIC estimations.PurposeTo evaluate whether a multispectral auto regressive moving average (ARMA) model can be used in conjunction with quantitative susceptibility mapping (QSM) to measure magnetic susceptibility as a confounder-free predictor of HIC.Study typePhantom study and in vivo cohort.SubjectsNine iron phantoms covering clinically relevant R2* range (20-1200/second) and 48 patients (22 male, 26 female, median age 18 years).Field strength/sequenceThree-dimensional (3D) and two-dimensional (2D) multi-echo gradient echo (GRE) at 1.5 T.AssessmentARMA-QSM modeling was performed on the complex 3D GRE signal to estimate R2*, fat fraction (FF), and susceptibility measurements. R2*-based dry clinical HIC values were calculated from the 2D GRE acquisition using a published R2*-HIC calibration curve as reference standard.Statistical testsLinear regression analysis was performed to compare ARMA R2* and susceptibility-based estimates to iron concentrations and dry clinical HIC values in phantoms and patients, respectively.ResultsIn phantoms, the ARMA R2* and susceptibility values strongly correlated with iron concentrations (R2 ≥ 0.9). In patients, the ARMA R2* values highly correlated (R2 = 0.97) with clinical HIC values with slope = 0.026, and the susceptibility values showed good correlation (R2 = 0.82) with clinical dry HIC values with slope = 3.3 and produced a dry-to-wet HIC ratio of 4.8.Data conclusionThis study shows the feasibility that ARMA-QSM can simultaneously estimate susceptibility-based wet HIC, R2*-based dry HIC and FFs from a single multi-echo GRE acquisition. Our results demonstrate that both, R2* and susceptibility-based wet HIC values estimated with ARMA-QSM showed good association with clinical dry HIC values with slopes similar to published R2*-biopsy HIC calibration and dry-to-wet tissue weight ratio, respectively. Hence, our study shows that ARMA-QSM can provide potentially confounder-free assessment of hepatic iron overload.Level of evidence3 TECHNICAL EFFICACY: Stage 2.

Project description:Background Advanced confounder-corrected chemical shift-encoded MRI-derived proton density fat fraction (PDFF) is a leading parameter for fat fraction quantification in nonalcoholic fatty liver disease (NAFLD). Because of the limited availability of this MRI technique, there is a need to develop and validate alternative parameters to assess liver fat. Purpose To assess relationship of quantitative US parameters to MRI PDFF and to develop multivariable quantitative US models to detect hepatic steatosis and quantify hepatic fat. Materials and Methods Adults with known NAFLD or who were suspected of having NAFLD were prospectively recruited between August 2015 and February 2019. Participants underwent quantitative US and chemical shift-encoded MRI liver examinations. Liver biopsies were performed if clinically indicated. The correlation between seven quantitative US parameters and MRI PDFF was evaluated. By using leave-one-out cross validation, two quantitative US multivariable models were evaluated: a classifier to differentiate participants with NAFLD versus participants without NAFLD and a fat fraction estimator. Classifier performance was summarized by area under the receiver operating characteristic curve and area under the precision-recall curve. Fat fraction estimator performance was evaluated by correlation, linearity, and bias. Results Included were 102 participants (mean age, 52 years ± 13 [standard deviation]; 53 women), 78 with NAFLD (MRI PDFF ≥ 5%). A two-variable classifier yielded a cross-validated area under the receiver operating characteristic curve of 0.89 (95% confidence interval: 0.82, 0.96) and an area under the precision-recall curve of 0.96 (95% confidence interval: 0.93, 0.99). The cross-validated fat fraction predicted by a two-variable fat fraction estimator was correlated with MRI PDFF (Spearman ρ = 0.82 [P < .001]; Pearson r = 0.76 [P < .001]). The mean bias was 0.02% (P = .97), and 95% limits of agreement were ±12.0%. The predicted fat fraction was linear with MRI PDFF (R 2 = 0.63; slope, 0.69; intercept, 4.3%) for MRI PDFF of 34% or less. Conclusion A multivariable quantitative US approach yielded excellent correlation with MRI proton density fat fraction for hepatic steatosis assessment in nonalcoholic fatty liver disease. © RSNA, 2020 Online supplemental material is available for this article.

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Prevalence of Fatty Liver Disease and Hepatic Iron Overload in a Northeastern German Population by Using Quantitative MR Imaging.

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