Attenuation imaging: Diagnostic differences in hepatic steatosis for chronic hepatitis B vs metabolic dysfunction-associated steatotic liver disease patients

BACKGROUND Hepatic steatosis, characterized by fat accumulation in hepatocytes, can result from metabolic dysfunction-associated steatotic liver disease (MASLD), infections, alcoholism, chemotherapy, and toxins. MASLD is diagnosed via imaging or biopsy with metabolic criteria and may progress to metabolic dysfunction–associated steatohepatitis, potentially leading to fibrosis, cirrhosis, or cancer. The coexistence of hepatic steatosis with chronic hepatitis B (CHB) is mainly related to metabolic factors and increases mortality and cancer risks. As a noninvasive method, attenuation imaging (ATI) shows promise in quantifying liver fat, demonstrating strong correlation with liver biopsy. AIM To investigate the disparity of ATI for assessing biopsy-based hepatic steatosis in CHB patients and MASLD patients. METHODS The study enrolled 249 patients who underwent both ATI and liver biopsy, including 78 with CHB and 171 with MASLD. Hepatic steatosis was classified into grades S0 to S3 according to the proportion of fat cells present. Liver fibrosis was staged from 0 to 4 according to the meta-analysis of histological data in viral hepatitis scoring system. The diagnostic performance of attenuation coefficient (AC) values across different groups was compared for each grade of steatosis. Factors associated with the AC values were determined through linear regression analysis. A multivariate logistic regression model was established to predict ≥ S2 within the MASLD group. RESULTS In both the CHB and the MASLD groups, AC values increased significantly with higher steatosis grade (P < 0.001). In the CHB group, the areas under the curve (AUCs) of AC for predicting steatosis grades ≥ S1, ≥ S2 and S3 were 0.918, 0.960 and 0.987, respectively. In contrast, the MASLD group showed AUCs of 0.836, 0.774, and 0.688 for the same steatosis grades. The diagnostic performance of AC for detecting ≥ S2 and S3 indicated significant differences between the two groups (both P < 0.001). Multivariate linear regression analysis identified body mass index, triglycerides, and steatosis grade as significant factors for AC. When the steatosis grade is ≥ S2, it can progress to more serious liver conditions. A clinical model integrating blood biochemical parameters and AC was developed in the MASLD group to enhance the prediction of ≥ S2, achieving an AUC of 0.848. CONCLUSION The AC could effectively discriminate the degree of steatosis in both the CHB and MASLD groups. In the MASLD group, when combined with blood biochemical parameters, AC exhibited better predictive ability for moderate to severe steatosis.