Integrating Quantitative CT Metrics with Biochemical Profiles in the Assessment of Steatotic Liver Disease
Abdullah Enes Ataş1, Hasan Emin Kaya21Department of Radiology, Necmettin Erbakan University, School of Medicine, Konya, Türkiye, 2Department of Radiology, Bursa Uludağ University School of Medicine, Bursa, Türkiye
Objectives: This study aimed to evaluate the utility of quantitative CT metrics in assessing metabolic liver injury by integrating radiological findings with biochemical profiles and non-invasive fibrosis scores. Methods: We retrospectively analyzed 225 patients who underwent unenhanced abdominal CT. Liver attenuation (HU), liver-to-spleen (L/S) ratio, liver-spleen difference (L-S diff), and subcutaneous fat thickness (SFT) were measured and correlated with serum transaminases, gamma-glutamyl transferase (GGT), and FIB-4 scores. Results: While absolute liver density (<40 HU) identified steatosis in 9.3% of the cohort, the L-S difference (<10 HU) expanded detection to 56.8%. GGT demonstrated the strongest inverse correlation with absolute liver density (p=0.005) and was the most robust biochemical predictor in ROC analysis (AUC=0.669); the liver-spleen difference showed similar performance (AUC=0.663, p=0.008). The L-S difference was the sole parameter associated with significant elevations in AST, ALT, and GGT simultaneously. Radiologically defined steatosis correlated with higher FIB-4 scores (p<0.05), whereas no correlation was observed with APRI scores. Conclusion: Quantitative unenhanced CT parameters, particularly the liver-spleen attenuation difference, correlate significantly with GGT levels and FIB-4 scores. These accessible metrics serve as valuable tools for the opportunistic screening of steatotic liver disease, reflecting both morphological changes and underlying metabolic dysfunction beyond simple fat accumulation. Keywords: Adiposity, Biomarkers, Gamma-glutamyltransferase, Non-alcoholic Fatty liver disease, Tomography, X-ray computed
Cite This Article
Ataş A, Kaya H. Integrating Quantitative CT Metrics with Biochemical Profiles in the Assessment of Steatotic Liver Disease. EJMI. 2026; 10(3): 449-457
Corresponding Author: Abdullah Enes Ataş