RAD23-0341_dataset.xlsx

<b>Background: </b>Owing to 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 ≥5% steatosis in patients with chronic liver disease (CLD) as defined using MRI-proton density fat fraction (MRI-PDFF).<b> </b><b>Materials and Methods: </b>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 ultrasound-guided attenuation parameter (UGAP) were measured in all participants. Participant MRI-PDFF value was used to define ≥5% steatosis. Four models based on different combinations of US parameters were created: Model 1 (UGAP alone), Model 2 (UGAP + IBSC), Model 3 (UGAP + SNR), and Model 4 (UGAP + IBSC + SNR). The diagnostic performance of all models was assessed using the area under the receiver operating characteristic curve (AUC). The model was internally validated using 1000 bootstrap samples.<b> </b><b>Results: </b>A total of 582 participants were included in this study (median age, 64 years [IQR, 52–72]; 274 females), 364 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 to other models, Models 3 and 4 demonstrated improved discrimination of ≥5% steatosis (p&lt;.01). <b>Conclusion: </b>A model built using the quantitative US parameters, UGAP, IBSC, and SNR, could accurately discriminate ≥5% steatosis in patients with CLD.<br>