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Whole-liver apparent diffusion coefficient histogram analysis for the diagnosis and staging of liver fibrosis
Zheng You1,2, Lei Jun-qiang2, and Xu Yong-sheng2
1First Clinical Medical College of LanZhou University, Lanzhou, Gansu, China, 2Radiology, First Hospital of LanZhou University, Lanzhou, Gansu, China

Synopsis

This study aimed to determine whether whole-liver apparent diffusion coefficient (ADC) histogram parameters can contribute to hepatic fibrosis staging. We evaluated quantitative histogram parameters between different pathological fibrosis stages. And the diagnostic performance of ADC histogram parameters in discriminating stage 1 or greater (≥F1), stage 2 or greater (≥F2), and stage 3 or greater (≥F3) liver fibrosis were compared. The results showed that many histogram parameters (kurtosis, skewness, entropy, mode, 75th and 90th percentiles) had statistical significance among the pathologic liver fibrosis stages (P<0.05), and kurtosis yielded the highest area under the curve (0.801).

Purpose

Conventional diffusion-weighted magnetic resonance imaging (DWI) is limited in quantitative evaluation of liver fibrosis, whole-liver apparent diffusion coefficient (ADC) histogram analysis might contributes to the diagnosis and staging of liver fibrosis. The study explored the value of whole-liver ADC histogram parameters in the diagnosis and staging of liver fibrosis.

Materials and Methods

Totally 86 patients with liver fibrosis (30 with chronic viral hepatitis, 29 with autoimmune hepatitis and 27 with unexplained liver fibrosis) and 20 individuals with no liver disease were enrolled in this retrospective study. All the subjects underwent diffusion-weighted MR imaging on a 3.0T MR scanner (MAGNETOM Skyra, Siemens Healthineers, Erlangen, Germany). DWI was performed adopting diffusion‐weighted‐echo‐planar imaging. For each patient, region of interest (ROI) covering the entire liver were drawn in each slice of axial diffusion-weighted images with FireVoxel (FireVoxel, 301; https://wp.nyu.edu/firevoxel/downloads/) by two radiologists. The whole-liver ADC histogram and its parameters were obtained by accumulating all ROIs in each slice. The differences of ADC histogram parameters between different histological liver fibrosis stages were compared using the Mann–Whitney U test. And ROC curve was constructed to evaluate the effectiveness of histogram parameters in differentiating stage 1 or greater (≥F1), stage 2 or greater (≥F2), and stage 3 or greater (≥F3) liver fibrosis.

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% 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 cut-off value of 1.817, in differentiating fibrosis stage ≥F1.

Conclusion

Histogram parameters of ADC ( kurtosis, entropy, skewness, mode, and 90th and 75th percentiles) may contribute to the diagnosis and staging of liver fibrosis, especially kurtosis.

Acknowledgements

You Zheng, Yong-Sheng Xu, Zhao Liu, Hai-Feng Liu, Ya-Nan Zhai, Xiao-Rong Mao, Jun-Qiang Lei.

References

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Figures

Images from a 37-year-old male. (a) The ROIs were delineated at the whole liver through all slices on the diffusion-weighted images (b = 0 s/mm2). (b) Calculation of the ADC map within the ROIs. (c) To convert the ADC map into whole numbers, we multiplied it by 105 and discarded anything after the decimal point. (d) Pathological staining (hematoxylin–eosin staining; ×100) confirmed liver fibrosis with a METAVIR score of 3. (e) Corresponding histograms were generated through the voxel-by-voxel ADC values of whole liver, and ADC histogram parameters were subsequently calculated.

(a,b): ROC curves for the discrimination of fibrosis stage ≥F2 in the viral hepatitis group by significant ADC histogram parameters. (c,d): ROC curves for the discrimination of fibrosis stage ≥F1 in the autoimmune hepatitis group by significant ADC histogram parameters. (e): ROC curves for the discrimination of fibrosis stage ≥F1 in the unexplained liver fibrosis group by significant ADC histogram parameters. (f,g): ROC curves for the discrimination of fibrosis stage ≥F1 in the combined liver fibrosis group by significant ADC histogram parameters.

Box plots show the measured values of the best parameters associated with histological fibrosis stages. (a-c): the viral hepatitis group; (d-f): the autoimmune hepatitis group; (g,h): the unexplained liver fibrosis group. (i,j): the combined liver fibrosis group. Circles indicate outliers.

ROC results for ADC histogram parameters in identifying different fibrosis stages of different groups.

Diagnostic performance of the best ADC histogram parameters in the differentiation of different fibrosis stages in different groups.

Proc. Intl. Soc. Mag. Reson. Med. 28 (2020)
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