Entropy-Based CT Radiomics as an Imaging Marker of Hepatic Injury in COVID-19

Background: Hepatic involvement in COVID-19 is frequently observed, yet conventional CT imaging may fail to detect subtle parenchymal alterations. This study aimed to evaluate whether CT-based radiomic texture analysis can identify liver injury associated with SARS-CoV-2 infection. Methods: We retrospectively analyzed 41 patients with RT-PCR-confirmed moderate or severe COVID-19 pneumonia who underwent non-contrast thoracoabdominal CT during the acute phase and at follow-up. Liver volume, mean hepatic attenuation, liver-to-spleen attenuation ratio, and radiomic features including first-order and GLCM entropy were extracted using 3D Slicer version 5.6.2 and SlicerRadiomic Revision: 8426cdf. Hepatic injury was defined by elevated serum transaminases. Three additional patients with available liver histopathology were included for correlation with imaging findings. Results: Patients with biochemical liver injury demonstrated significantly higher hepatic entropy values in the acute phase compared to those without injury (first-order entropy: 1.63 vs. 1.48, p = 0.019; GLCM entropy: 3.12 vs. 2.83, p = 0.013). Entropy metrics were inversely correlated with hepatic attenuation at follow-up (GLCM r = -0.385, p = 0.013; first-order r = -0.346, p = 0.027), indicating possible progression to lower-density states. Ferritin showed a moderate positive correlation with entropy (r = 0.47, p = 0.0017). Histopathological examination revealed steatosis, hepatocellular injury, inflammatory infiltration, and vascular congestion, aligning with radiomic abnormalities. Conclusions: Entropy-based CT radiomics reflect microstructural liver alterations in COVID-19, supported by both biochemical and histopathological data. This approach may enhance the detection of hepatic injury beyond conventional imaging and could be explored in systemic infections.