Higher cut-off values of non-invasive methods might be needed to detect moderate-to-severe steatosis in morbid obese patients: a pilot study

MBR-Net: A multi-branch residual network based on ultrasound backscattered signals for characterizing pediatric hepatic steatosis

The evaluation of pediatric hepatic steatosis and early detection of fatty liver in children are of critical importance. In this paper, a deep learning model based on the convolutional neural network (CNN) of ultrasound backscattered signals, multi-branch residual network (MBR-Net), was proposed for characterizing pediatric hepatic steatosis. The MBR-Net was composed of three convolutional branches. Each branch used different sizes of convolution blocks to enhance the capability of local feature acquisition, and leveraged the residual mechanism with skip connections to guide the network to effectively capture features. A total of 393 frames of ultrasound backscattered signals collected from 131 children were included in the experiments. The hepatic steatosis index was used as the reference standard for diagnosing the steatosis grade, G0-G3. The ultrasound backscattered signals within the liver region of interests (ROIs) were normalized and augmented using a sliding gate method. The gated ROI signals were randomly divided into training, validation, and test sets with the ratio of 8:1:1. The area under the operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were used as the evaluation metrics. Experimental results showed that the MBR-Net yields AUCs for diagnosing pediatric hepatic steatosis grade ≥G1, ≥G2, and ≥G3 of 0.94 (ACC: 93.65%; SEN: 89.79%; SPE: 84.48%), 0.93 (ACC: 90.48%; SEN: 87.75%; SPE: 82.65%), and 0.93 (ACC: 87.76%; SEN: 84.84%; SPE: 86.55%), respectively, which were superior to the conventional one-branch CNNs without residual mechanisms. The proposed MBR-Net can be used as a new deep learning method for ultrasound backscattered signal analysis to characterize pediatric hepatic steatosis.

Severe Hepatic Steatosis Is Associated With Low-Level Viremia and Advanced Fibrosis in Patients With Chronic Hepatitis B in North America

Background and Aims

The obesity epidemic has increased the risk of nonalcoholic fatty liver disease (NAFLD) in both the general and chronic hepatitis B (CHB) populations. Our study aims to determine the prevalence of NAFLD in patients with CHB based on controlled attenuation parameter (CAP) and the epidemiological, clinical, and virological factors associated with severe hepatic steatosis.

Methods

The Canadian Hepatitis B Network cohort was utilized to provide a cross-sectional description of demographics, comorbidities, antiviral treatment, and hepatits B virus (HBV) tests. Liver fibrosis and steatosis were measured by transient elastography and CAP, respectively. Any grade and severe steatosis were defined as CAP >248 and >280 dB/m, respectively. Advanced liver fibrosis was defined as transient elastography measurement >10.7 kPa.

Results

In 1178 patients with CHB (median age: 47.4%, 57.7% males, 75.7% Asian, 13% African, 6.5% White, 86% HBV e antigen negative, median HBV DNA of 2.44 log10IU/mL, 42.7% receiving treatment), the prevalence of any grade and severe steatosis was 53% and 36%, respectively. In the multivariate analysis, obesity was a significant predictor for severe steatosis (adjusted odds ratio: 5.046, 95% confidence interval: 1.22-20.93). Severe steatosis was a determinant associated with viral load (adjusted odds ratio: 0.385, 95% confidence interval: 0.20-0.75, P < .01; r = -0.096, P = .007) regardless of antiviral therapy, age, and alanine aminotransferase levels.

Conclusion

In this large multiethnic CHB population, hepatic steatosis is common. Severe steatosis is independently associated with higher fibrosis, but negatively with HBV DNA, regardless of antiviral therapy history.