Texture-based classification of liver fibrosis using MRI

Abstract

Purpose: To investigate the ability of texture analysis of MRI images to stage liver fibrosis. Current noninvasive approaches for detecting liver fibrosis have limitations and cannot yet routinely replace biopsy for diagnosing significant fibrosis. Materials and Methods: Forty-nine patients with a range of liver diseases and biopsy-confirmed fibrosis were enrolled in the study. For texture analysis all patients were scanned with a T2-weighted, high-resolution, spin echo sequence and Haralick texture features applied. The area under the receiver operating characteristics curve (AUROC) was used to assess the diagnostic performance of the texture analysis. Results: The best mean AUROC achieved for separating mild from severe fibrosis was 0.81. The inclusion of age, liver fat and liver R2 variables into the generalized linear model improved AUROC values for all comparisons, with the F0 versus F1-4 comparison the highest (0.91). Conclusion: Our results suggest that a combination of MRI measures, that include selected texture features from T2-weighted images, may be a useful tool for excluding fibrosis in patients with liver disease. However, texture analysis of MRI performs only modestly when applied to the classification of patients in the mild and intermediate fibrosis stages.