Noninvasive Assessment of Advanced Fibrosis Based on Hepatic Volume in Patients with Nonalcoholic Fatty Liver Disease

BACKGROUND/AIMS: Noninvasive liver fibrosis evaluation was performed in patients with nonalcoholic fatty liver disease (NAFLD). We used a quantitative method based on the hepatic volume acquired from gadoxetate disodium-enhanced (Gd-EOB-DTPA-enhanced) magnetic resonance imaging (MRI) for diagnosing advanced fibrosis in patients with NAFLD. METHODS: A total of 130 patients who were diagnosed with NAFLD and underwent Gd-EOB-DTPA-enhanced MRI were retrospectively included. Histological data were available for 118 patients. Hepatic volumetric parameters, including the left hepatic lobe to right hepatic lobe volume ratio (L/R ratio), were measured. The usefulness of the L/R ratio for diagnosing fibrosis ≥F3–4 and F4 was assessed using the area under the receiver operating characteristic (AUROC) curve. Multiple regression analysis was performed to identify variables (age, body mass index, serum fibrosis markers, and histological features) that were associated with the L/R ratio. RESULTS: The L/R ratio demonstrated good performance in differentiating advanced fibrosis (AUROC, 0.80; 95% confidence interval, 0.72 to 0.88) from cirrhosis (AUROC, 0.87; 95% confidence interval, 0.75 to 0.99). Multiple regression analysis showed that only fibrosis was significantly associated with the L/R ratio (coefficient, 0.121; p<0.0001). CONCLUSIONS: The L/R ratio, which is not influenced by pathological parameters other than fibrosis, is useful for diagnosing cirrhosis in patients with NAFLD.

Influence of Signal Intensity Non-Uniformity on Brain Volumetry Using an Atlas-Based Method

OBJECTIVE: Many studies have reported pre-processing effects for brain volumetry; however, no study has investigated whether non-parametric non-uniform intensity normalization (N3) correction processing results in reduced system dependency when using an atlas-based method. To address this shortcoming, the present study assessed whether N3 correction processing provides reduced system dependency in atlas-based volumetry. MATERIALS AND METHODS: Contiguous sagittal T1-weighted images of the brain were obtained from 21 healthy participants, by using five magnetic resonance protocols. After image preprocessing using the Statistical Parametric Mapping 5 software, we measured the structural volume of the segmented images with the WFU-PickAtlas software. We applied six different bias-correction levels (Regularization 10, Regularization 0.0001, Regularization 0, Regularization 10 with N3, Regularization 0.0001 with N3, and Regularization 0 with N3) to each set of images. The structural volume change ratio (%) was defined as the change ratio (%) = (100 x [measured volume - mean volume of five magnetic resonance protocols] / mean volume of five magnetic resonance protocols) for each bias-correction level. RESULTS: A low change ratio was synonymous with lower system dependency. The results showed that the images with the N3 correction had a lower change ratio compared with those without the N3 correction. CONCLUSION: The present study is the first atlas-based volumetry study to show that the precision of atlas-based volumetry improves when using N3-corrected images. Therefore, correction for signal intensity non-uniformity is strongly advised for multi-scanner or multi-site imaging trials.