New Inuyama classification; new criteria for histological assessment of chronic hepatitis

Abstract

To investigate whether the iodine density of liver parenchyma in the equilibrium phase and extracellular volume fraction (ECV) measured by deep learning-based spectral computed tomography (CT) can enable noninvasive liver fibrosis staging.We retrospectively analyzed 63 patients who underwent dynamic CT using deep learning-based spectral CT before a hepatectomy or liver transplantation. The iodine densities of the liver parenchyma (I-liver) and abdominal aorta (I-aorta) were independently measured by two radiologists using iodine density images at the equilibrium phase. The iodine-density ratio (I-ratio: I-liver/I-aorta) and CT-ECV were calculated. Spearman's rank correlation analysis was used to evaluate the relationship between the I-ratio or CT-ECV and liver fibrosis stage, and receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performances of the I-ratio and CT-ECV.The I-ratio and CT-ECV showed significant positive correlations with liver fibrosis stage (ρ = 0.648, p < 0.0001 and ρ = 0.723, p < 0.0001, respectively). The areas under the ROC curve for the CT-ECV were 0.882 (F0 vs ≥ F1), 0.873 (≤F1 vs ≥ F2), 0.848 (≤F2 vs ≥ F3), and 0.891 (≤F3 vs F4).Deep learning-based spectral CT may be useful for noninvasive assessments of liver fibrosis.