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• Original Article •
Study of whole-liver histogram analysis based on Gd-EOB-DTPA hepatobiliary phase for evaluating liver fibrosis in rabbits
LI Yufeng  LIU Haifeng  WANG Qing  DU Ya'nan  ZHU Zuhui  XING Wei 

Cite this article as: Li YF, Liu HF, Wang Q, et al. Study of whole-liver histogram analysis based on Gd-EOB-DTPA hepatobiliary phase for evaluating liver fibrosis in rabbits[J]. Chin J Magn Reson Imaging, 2021, 12(7): 45-50. DOI:10.12015/issn.1674-8034.2021.07.009.


[Abstract] Objective To explore parameters derived from whole-liver histogram analysis (HA) with Gd-EOB-DTPA enhanced MRI hepatobiliary phase in quantitatively evaluating liver fibrosis (LF). Materials andMethods One hundred rabbits were randomly divided into CCl4-induced LF group (n=80) and control group (n=20), and then were scheduled for hepatobiliary phase imaging examination. Whole-liver region of interest was drawn, and histogram analysis metrics including minimum, maximum, mean, median, skewness, kurtosis, inhomogeneity, entropy, and nth percentiles were extracted from hepatobiliary phase. Parameters were compared among different LF stages using Mann-Whitney U test and receivers operating characteristic curve, using histopathological results as reference standard.Results Eighty-four patients were enrolled, 18, 17, 17, 18, and 14 rabbits were pathologically diagnosed as F0—F4, respectively. HA parameters including the inhomogeneity (r=0.809), 90th (r=0.718), 75th (r=0.645), entropy (r=0.546), median (r=0.425) and kurtosis (r=0.305) of hepatobiliary phase demonstrated significant positive correlation with increasing fibrosis stage (P＜0.05). Inhomogeneity, 90th, 75th and entropy were of value in different normal (F0), early stage (F1—F2) and advanced (F3—F4) stage. The inhomogeneity parameter demonstrated higher diagnostic efficacy than other histogram analysis parameters in fibrosis staging, with an AUC value of 0.87 for F0 vs. F1—F4, 0.77 for F0 vs. F1—F2, 0.90 for F1—F2 vs. F3—F4, and 0.97 for F0 vs. F3—F4.Conclusions Histogram analysis of Gd-EOB-DTPA enhanced MRI hepatobiliary phase provide higher diagnostic value in distinguishing LF stages, which can be served as an effective imaging alternative in staging LF.
[Keywords] gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid；hepatobiliary phase；histogram analysis；liver fibrosis；magnetic resonance imaging；experiment study

LI Yufeng   LIU Haifeng   WANG Qing   DU Ya'nan   ZHU Zuhui   XING Wei*  

Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, China

Xing W, E-mail: suzhxingwei@suda.edu.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81771798). Science and Technology Project of Jiangsu Province (No. BE2018646). Youth Project of Changzhou Health Commission (No. QN202022).
Received  2021-03-04
Accepted  2021-04-19
DOI: 10.12015/issn.1674-8034.2021.07.009
Cite this article as: Li YF, Liu HF, Wang Q, et al. Study of whole-liver histogram analysis based on Gd-EOB-DTPA hepatobiliary phase for evaluating liver fibrosis in rabbits[J]. Chin J Magn Reson Imaging, 2021, 12(7): 45-50. DOI:10.12015/issn.1674-8034.2021.07.009.

1
Tacke F, Trautwein C. Mechanisms of liver fibrosis resolution[J]. J Hepatol, 2015, 63(4): 1038-1039. DOI: 10.1016/j.jhep.2015.03.039.
2
Seki E, Brenner DA. Recent advancement of molecular mechanisms of liver fibrosis[J]. J Hepatobiliary Pancreat Sci, 2015, 22(7): 512-518. DOI: 10.1002/jhbp.245.
3
Tacke F, Weiskirchen R. An update on the recent advances in antifibrotic therapy[J]. Expert Rev Gastroenterol Hepatol, 2018, 12(11):1143-1152. DOI: 10.1080/17474124.2018.1530110.
4
Zuo ZB, Cui HZ, Huang CX, et al. Unscrambling of guideline on the diagnosis and treatment of hepatic fibrosis with integrated chinese and western medicine (2019 Edition)[J]. Chin J Hepatol, 2019, 27(7): 494-504. DOI: 10.16305/j.1007-1334.2020.03.008.
5
Hu GC, Xu YS, Zhao XJ, et al. The value of whole-liver ADC histogram and T2WI liver signal intensityin liver fibrosis stage in rats[J]. J Clin Radiol, 2021, 40(1): 152-157. DOI: 10.13437/j.cnki.jcr.2021.01.036.
6
Hako R, Kristian P. Noninvasive assessment of liver fibrosis in patients with chronic hepatitis B or C by contrast-enhanced magnetic resonance imaging[J]. Can J Gastroenterol Hepatol, 2019, 2019: 3024630. DOI: 10.1155/2019/3024630.
7
Noren B, Forsgren MF, Dahlqvist Leinhard O, et al. Separation of advanced from mild hepatic fibrosis by quantification of the hepatobiliary uptake of Gd-EOB-DTPA[J]. Eur Radiol, 2013, 23(1): 174-181. DOI: 10.1007/s00330-012-2583-2.
8
Yu H, Touret AS, Li B, et al. Application of texture analysis on parametric T(1) and T(2) maps for detection of hepatic fibrosis[J]. J Magn Reson Imaging, 2017, 45(1): 250-259. DOI: 10.1002/jmri.25328.
9
Liu HF, Xu YS, Liu Z, et al. Value of Gd-EOB-DTPA-enhanced MRI and diffusion-weighted imaging in detecting residual hepatocellular carcinoma after drug-eluting bead transarterial chemoembolization[J]. Acad Radiol, 2021, 28(6): 790-798. DOI: 10.1016/j.acra.2020.04.003.
10
Liu HF, Wang Q, Du YN, et al. Dynamic contrast-enhanced MRI with Gd-EOB-DTPA for the quantitative assessment of early-stage liver fibrosis induced by carbon tetrachloride in rabbits[J]. Magn Reson Imaging, 2020, 70(5): 57-63. DOI: 10.1016/j.mri.2020.04.010.
11
Li CX, Liu HT, Li X, et al. Gd-EOB-DTPA-enhanced 3 Tesla MR imaging in screening the optimal parameters in the evaluation of liver function[J]. J Chin Pract Diagn Therapy, 2019, 33(12): 1212-1216. DOI: 10.13507/j.issn.1674-3474.2019.12.018.
12
Pan S, Wang L, Xin J. Combining (18) F-FDG PET and Gd-EOB-DTPA-enhanced MRI for staging liverfibrosis[J]. Life Sci, 2021, 269:119086. DOI: 10.1016/j.lfs.2021.119086.
13
Di Renzi P, Coniglio A, Abella A, et al. Volumetric histogram-based analysis of cardiac magnetic resonance T1 mapping: a tool to evaluate myocardial diffuse fibrosis[J]. Phys Med, 2021, 82(5):185-191. DOI: 10.1016/j.ejmp.2021.01.080.
14
Brown AL, Jeong J, Wahab RA, et al. Diagnostic accuracy of MRI textural analysis in the classification of breast tumors[J]. Clin Imaging. 2021, 24(2): 86-91. DOI: 10.1016/j.clinimag.2021.02.031.
15
Hu F, Yang R, Huang Z, et al. Liver fibrosis: in vivo evaluation using intravoxel incoherent motion-derived histogram metrics with histopathologic findings at 3.0 T[J]. Abdominal radiology, 2017, 42(12): 2855-2863. DOI: 10.1007/s00261-017-1208-2.
16
Sheng RF, Jin KP, Yang L, et al. Histogram analysis of diffusion kurtosis magnetic resonance imaging for diagnosis of hepatic fibrosis[J]. Korean J Radiol, 2018, 19(5): 916-922. DOI: 10.3348/kjr.2018.19.5.916.
17
Yang ZX, Liang HY, Hu XX, et al. Feasibility of histogram analysis of susceptibility-weighted MRI for staging of liver fibrosis[J]. Diagn Interv Radiol, 2016, 22(4): 301-307. DOI: 10.5152/dir.2016.15284.
18
Barry B, Buch K, Soto JA, et al. Quantifying liver fibrosis through the application of texture analysis to diffusion weighted imaging[J]. Magn Reson Imaging, 2014, 32(1): 84-90. DOI: 10.1016/j.mri.2013.04.006.
19
Zheng Y, Xu YS, Liu Z, et al. Whole-liver apparent diffusion coefficient histogram analysis for the diagnosis and staging of liver fibrosis[J]. J Magn Reson Imaging, 2020, 51(6): 1745-1754. DOI: 10.1002/jmri.26987.
20
Wang Q, Liu H, Zhu Z, et al. Feasibility of T1 mapping with histogram analysis for the diagnosis and staging of liver fibrosis: preclinical results[J]. Magn Reson Imaging, 2021, 76(3): 79-86. DOI: 10.1016/j.mri.2020.11.006.
21
Yoon JH, Lee JM, Kim E, et al. Quantitative liver function analysis: volumetric T1 mapping with fast multisection B(1) inhomogeneity correction in hepatocyte-specific contrast-enhanced liver MR imaging[J]. Radiology, 2017, 282(2): 408-417. DOI: 10.1148/radiol.2016152800.
22
Fujimoto K, Tonan T, Azuma S, et al. Evaluation of the mean and entropy of apparent diffusion coefficient values in chronic hepatitis C: correlation with pathologic fibrosis stage and inflammatory activity grade. Radiology, 2011, 258(3): 739-748. DOI: 10.1148/radiol.10100853.
23
Xu X, Zhu H, Li R, et al. Whole-liver histogram and texture analysis on T1 maps improves the risk stratification of advanced fibrosis in NAFLD. Eur Radiol, 2021, 31(3): 1748-1759. DOI: 10.1007/s00330-020-07235-4.

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