Kun WangQun LiuJing WangMinghui WangJinqiao ZhangYanjie YuLing LiuHairong Liu
Objectives To assess the diagnostic performance of ultrasound attenuation analysis (USAT) in evaluating hepatic steatosis in patients with metabolic dysfunction‐associated steatotic liver disease (MASLD), with magnetic resonance imaging proton density fat fraction (MRI‐PDFF) as the reference. Methods Participants were recruited. Each participant underwent both USAT and MRI‐PDFF examinations on the same day. We employed MRI‐PDFF thresholds to classify the stages of hepatic steatosis. Univariable and multivariable linear regression analyses were conducted to identify significant factors influencing USAT. Receiver operating characteristic (ROC) curve analysis was utilized to evaluate the diagnostic performance of USAT in predicting the grade of liver steatosis and compared with visual hepatic steatosis grade (VHSG) and clinical prediction models. Results A total of 145 patients took part in this study. The correlation coefficient between USAT and MRI‐PDFF was 0.805 ( p < .050). USAT varied significantly across different hepatic steatosis grade. Triglyceride (TG) and steatosis grade were significant determinant factors for USAT. The areas under the ROC curve of USAT for predicting steatosis ≥ S1, ≥ S2, and =S3 were 0.90 (cut‐off value of 0.61 dB/cm/MHz), 0.92 (cut‐off value of 0.71 dB/cm/MHz), and 0.92 (cut‐off value of 0.82 dB/cm/MHz), respectively. The diagnostic performance of USAT was statistically better than that of VHSG, Hepatic steatosis index (HSI), and Framingham steatosis index (FSI). Conclusion USAT is a promising quantitative tool for the quantitative assessment of hepatic steatosis in patients with MASLD, demonstrating a stronger correlation with MRI‐PDFF.
Meng SunMingwei ZhongFangqiong LuoMeng LanXinru ZhangWei NieZhe Ma
Mohammed Ajmal SVaishnavi NairJesse Jacob SkariahKevin JoyKrishnadas DevadasS. Srijaya
Yunling FanKailing ChenQiannan ZhaoHaohao YinYuli ZhuHuixiong Xu
Zhi PengZijin WangJuan WangXuefeng Li
Xueqi LiCheng Guang-wenIwaki AkiyamaXianjue HuangJing LiangLiyun XueYi ChengMasatoshi KudoHong Ding