Technical acoustic measurements combined with clinical parameters for differential diagnosis of nonalcoholic steatohepatitis

Abstract

Background and aim Diagnosing NASH is challenging. This study intended to explore the diagnostic value of multiple technical acoustic measurements in the diagnosis of NASH, and to establish a diagnostic model combining technical acoustic measurements with clinical parameters to improve the diagnostic efficacy of NASH. Methods We consecutively enrolled 75 patients with clinically suspected NAFLD who underwent percutaneous liver biopsy in our hospital from June 2020 to December 2021. All cases underwent multiple advanced acoustic measurements for liver such as SWD, SWS, ATI, NLV, and Ratio examination before liver biopsies. A nomogram prediction model combining the technical acoustic measurements and clinical parameters was established and the model is proposed to improve the diagnostic performance of NASH. Results A total of 75 cases were included in this study. Classification of Pathological grade for NASH was as follows: normal liver, (n=15, 20%), NAFL, (n=44, 58.7%), and NASH, (n=16, 21.3%). There were statistically significant differences in SWS (p=0.002), AC (p=0.018), NLV (p=0.033), age (p=0.013) and Glucose (Glu) (p=0.049) between NASH and non-NASH. A nomogram model which includes SWS, AC, NLV, age and Glu was built to predict NASH, and the calibration curves showed good calibrations in both training and validation sets. The AUCs of the combined nomogram model for the training set and validation set were 0.8597 and 0.7794, respectively. Conclusion There were statistically significant differences in SWS, AC, NLV, age and Glu between NASH and non-NASH. A nomogram model which includes SWS, AC, NLV, age and Glu was built to predict NASH. The predictive model has a higher diagnostic performance than a single factor model in the diagnosis of NASH, and has a good clinical application prospect.

Assessment of Hepatic Steatosis Using Ultrasound-Based Techniques: Focus on Fat Quantification

“…Several studies have attempted to enhance the accuracy of measuring hepatic steatosis by combining multiple parameters, such as the integrated BSC, the signal-to-noise ratio with the AC value, or by improving existing algorithms [89,114]. Moreover, a predictive model utilizing multiparametric US combining [79,[115][116][117]. However, there is a need to mitigate the variability in cutoff values, develop standardized protocols, and further investigate the effects of confounding factors.…”