Diffusion-Weighted MRI of the Liver in Patients With Chronic Liver Disease: A Comparative Study Between Different Fitting Approaches and Diffusion Models.

[en] BACKGROUND: Diffusion-weighted imaging (DWI) has been considered for chronic liver disease (CLD) characterization. Grading of liver fibrosis is important for disease management.
PURPOSE: To investigate the relationship between DWI's parameters and CLD-related features (particularly regarding fibrosis assessment).
STUDY TYPE: Retrospective.
SUBJECTS: Eighty-five patients with CLD (age: 47.9 ± 15.5, 42.4% females).
FIELD STRENGTH/SEQUENCE: 3-T, spin echo-echo planar imaging (SE-EPI) with 12 b-values (0-800 s/mm2 ).
ASSESSMENT: Several models statistical models, stretched exponential model, and intravoxel incoherent motion were simulated. The corresponding parameters (Ds , σ, DDC, α, f, D, D*) were estimated on simulation and in vivo data using the nonlinear least squares (NLS), segmented NLS, and Bayesian methods. The fitting accuracy was analyzed on simulated Rician noised DWI. In vivo, the parameters were averaged from five central slices entire liver to compare correlations with histological features (inflammation, fibrosis, and steatosis). Then, the differences between mild (F0-F2) or severe (F3-F6) groups were compared respecting to statistics and classification. A total of 75.3% of patients used to build various classifiers (stratified split strategy and 10-folders cross-validation) and the remaining for testing.
STATISTICAL TESTS: Mean squared error, mean average percentage error, spearman correlation, Mann-Whitney U-test, receiver operating characteristic (ROC) curve, area under ROC curve (AUC), sensitivity, specificity, accuracy, precision. A P-value <0.05 was considered statistically significant.
RESULTS: In simulation, the Bayesian method provided the most accurate parameters. In vivo, the highest negative significant correlation (Ds , steatosis: r = -0.46, D*, fibrosis: r = -0.24) and significant differences (Ds , σ, D*, f) were observed for Bayesian fitted parameters. Fibrosis classification was performed with an AUC of 0.92 (0.91 sensitivity and 0.70 specificity) with the aforementioned diffusion parameters based on the decision tree method.
DATA CONCLUSION: These results indicate that Bayesian fitted parameters may provide a noninvasive evaluation of fibrosis with decision tree.
EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.