Related Experiment Video For apparent diffusion coefficient
Updated: Jan 3, 2026
Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
Published on: July 21, 2023
Whole-Liver Apparent Diffusion Coefficient Histogram Analysis for the Diagnosis and Staging of Liver Fibrosis
You Zheng1,2, Yong-Sheng Xu1, Zhao Liu1
1First Clinical Medical College of Lanzhou University, Lanzhou, Gansu, China.
Background:
Conventional diffusion-weighted imaging is limited in the quantitative evaluation of liver fibrosis, and whole-liver apparent diffusion coefficient (ADC) histogram analysis might contribute to the diagnosis and staging of liver fibrosis.
Purpose:
To explore the value of whole-liver ADC histogram parameters in the diagnosis and staging of liver fibrosis.
Study Type:
Retrospective.
Population:
Twenty individuals with no liver disease and 86 patients with liver fibrosis, including 30 with chronic viral hepatitis, 29 with autoimmune hepatitis, and 27 with unexplained liver fibrosis patients.
Field Strength/Sequence:
3.0T/T1 -weighted, T2 -weighted, and diffusion-weighted images.
Assessment:
A region of interest (ROI) was drawn in each slice of the diffusion-weighted images. Whole-liver histogram parameters were obtained with dedicated software by accumulating all ROIs. The effectiveness of the parameters in differentiating stage 1 or greater (≥F1), stage 2 or greater (≥F2), and stage 3 or greater (≥F3) liver fibrosis was assessed.
Statistical Tests:
Mann-Whitney U-test and receiver operating characteristic curve analysis.
Results:
Kurtosis, entropy, skewness, mode, and 90th and 75th percentiles exhibited significant differences among the pathological fibrosis stages (P < 0.05). Kurtosis was found to be the most meaningful parameter in differentiating fibrosis stages of the viral hepatitis, autoimmune hepatitis, and unexplained liver fibrosis group (area under the curve) (AUC = 0.793, 0.771, 0.798, respectively). In the combined liver fibrosis group, kurtosis achieved the highest AUC (0.801; 95% confidence interval [CI]: 0.702-0.900; sensitivity: 0.750; specificity: 0.850; positive likelihood ratio: 4.953; negative likelihood ratio: 0.302; positive predictive value: 0.946; negative predictive value: 0.486), with a cutoff value of 1.817, in differentiating fibrosis stage ≥F1.
Data Conclusion:
Kurtosis, entropy, skewness, mode, and 90th and 75th percentiles may contribute to the diagnosis and staging of liver fibrosis, especially kurtosis.
Level Of Evidence:
4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1745-1754.
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