From the Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering (A.H., W.D.O.), and Department of Food Science and Human Nutrition (J.W.E.), University of Illinois at Urbana-Champaign, 306 N Wright St, Urbana, IL 61801; Liver Imaging Group, Department of Radiology (Y.N.Z., A.S.B., V.M., C.B.S.), Department of Radiology (M.P.A.); NAFLD Research Center, Division of Gastroenterology, Department of Medicine (R.L.), and Department of Pathology (M.A.V.), University of California, San Diego, La Jolla, Calif.
Radiology. 2020 Apr;295(1):106-113. doi: 10.1148/radiol.2020191152. Epub 2020 Feb 4.
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 [ < .001]; Pearson = 0.76 [ < .001]). The mean bias was 0.02% ( = .97), and 95% limits of agreement were ±12.0%. The predicted fat fraction was linear with MRI PDFF ( = 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
背景 高级混杂因素校正的化学位移编码 MRI 衍生质子密度脂肪分数（PDFF）是用于非酒精性脂肪性肝病（NAFLD）脂肪分数定量的主要参数。由于这种 MRI 技术的可用性有限，因此需要开发和验证替代参数来评估肝脏脂肪。目的 评估定量 US 参数与 MRI PDFF 的关系，并开发多变量定量 US 模型以检测肝脂肪变性和定量肝脂肪。材料与方法 2015 年 8 月至 2019 年 2 月期间，前瞻性招募了已知患有 NAFLD 或疑似患有 NAFLD 的成年人。参与者接受了定量 US 和化学位移编码 MRI 肝脏检查。如果临床需要，进行肝活检。评估了七种定量 US 参数与 MRI PDFF 的相关性。通过使用留一法交叉验证，评估了两种定量 US 多变量模型：用于区分有或无 NAFLD 的参与者的分类器和脂肪分数估计器。通过接收者操作特征曲线下面积和精度-召回曲线下面积总结分类器性能。通过相关性、线性和偏差评估脂肪分数估计器的性能。结果 共纳入 102 名参与者（平均年龄，52 岁±13[标准差]；53 名女性），其中 78 名患有 NAFLD（MRI PDFF≥5%）。两变量分类器的交叉验证接收者操作特征曲线下面积为 0.89（95%置信区间：0.82，0.96），精度-召回曲线下面积为 0.96（95%置信区间：0.93，0.99）。两变量脂肪分数估计器预测的交叉验证脂肪分数与 MRI PDFF 相关（Spearman ρ=0.82[<0.001]；Pearson =0.76[<0.001]）。平均偏差为 0.02%（=0.97），95%一致性界限为±12.0%。对于 MRI PDFF 为 34%或更低的情况，预测的脂肪分数与 MRI PDFF 呈线性关系（=0.63；斜率，0.69；截距，4.3%）。结论 多变量定量 US 方法与 MRI 质子密度脂肪分数在非酒精性脂肪性肝病的肝脂肪变性评估中具有极好的相关性。
