Ultrasound-guided attenuation parameter: a liver fat quantification technique for forecasting the progression of...

To investigate the application value of ultrasonic attenuation parameter imaging (UGAP) in the assessment of metabolic dysfunction-associated steatotic liver disease (MASLD) in overweight and obese patients.

A total of 328 overweight/obese patients-225 in the MASLD group and 103 in the simple overweight/obese group-were chosen from XX between August 2023 and August 2024. As the typical control group, 236 healthy individuals who were matched for age and gender were chosen during the same period. The attenuation coefficient (AC) differences between the groups were compared, and Pearson correlation analysis was used to look into the relationship between AC and clinical indicators. A prediction model was created, the diagnostic efficacy was examined, and MASLD risk factors in overweight and obese patients were screened using the independent sample T-test and multiple logistic regression analysis.

AC of the MASLD group, overweight and obese group, and normal control group were (0.73 ± 0.08), (0.57 ± 0.04), and (0.54 ± 0.07) dB^-1·cm^-1·MHz^-1. There was a statistically significant difference between the groups (P<0.05). In patients who were overweight or obese, AC, BMI, and visceral fat were the risk factors for predicting MASLD. The optimal cut-off values were AC ≥0.635dB^-1·cm^-1·MHz^-1, BMI ≥27.58kg/m², and visceral fat thickness (VFT) ≥66.115 mm. The areas under the receiver operating characteristic (ROC) curve were 0.993, 0.792, and 0.708. The area under ROC curve of AC combined with BMI and visceral fat was 0.997, and the prediction efficiency was greater than that of the single AC index and that of the AC + BMI bivariate prediction model. The diagnostic sensitivity and specificity were 96.4% and 98.1%.

UGAP can be utilised for clinical screening to assess the prevalence of MASLD in patients who are overweight or obese and to dynamically track the progression of the disease. In patients who are overweight or obese, the accuracy of the UGAP assessment of MASLD can be increased by combining AC with BMI and a visceral fat prediction model.