Multivariable Quantitative US Parameters for Assessing Hepatic Steatosis

Background Because of the global increase in the incidence of nonalcoholic fatty liver disease, the development of noninvasive, widely available, and highly accurate methods for assessing hepatic steatosis is necessary. Purpose To evaluate the performance of models with different combinations of quantitative US parameters for their ability to predict at least 5% steatosis in patients with chronic liver disease (CLD) as defined using MRI proton density fat fraction (PDFF). Materials and Methods Patients with CLD were enrolled in this prospective multicenter study between February 2020 and April 2021. Integrated backscatter coefficient (IBSC), signal-to-noise ratio (SNR), and US-guided attenuation parameter (UGAP) were measured in all participants. Participant MRI PDFF value was used to define at least 5% steatosis. Four models based on different combinations of US parameters were created: model 1 (UGAP alone), model 2 (UGAP with IBSC), model 3 (UGAP with SNR), and model 4 (UGAP with IBSC and SNR). Diagnostic performance of all models was assessed using area under the receiver operating characteristic curve (AUC). The model was internally validated using 1000 bootstrap samples. Results A total of 582 participants were included in this study (median age, 64 years; IQR, 52-72 years; 274 female participants). There were 364 participants in the steatosis group and 218 in the nonsteatosis group. The AUC values for steatosis diagnosis in models 1-4 were 0.92, 0.93, 0.95, and 0.96, respectively. The C-indexes of models adjusted by the bootstrap method were 0.92, 0.93, 0.95, and 0.96, respectively. Compared with other models, models 3 and 4 demonstrated improved discrimination of at least 5% steatosis (P < .01). Conclusion A model built using the quantitative US parameters UGAP, IBSC, and SNR could accurately discriminate at least 5% steatosis in patients with CLD. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Han in this issue.