钆塞酸二钠增强磁共振图像纹理分析对于评价大鼠肝纤维化的价值

doi:10.24920/003562

Highlight

In this article, the authors found texture feature Entropy of gadoxetic acid-enhanced magnetic resonance imaging T1 mapping images to be a useful biomarker for the diagnosis of liver fibrosis in an experimental rat model. And the textural features from T1-weighted, T2-weighted and apparent diffusion coefficient maps were used to evaluate fibrosis as a comparison

Abstract

Objective To explore the ability of texture analysis of gadoxetic acid-enhanced magnetic resonance imaging (MRI) T1 mapping images, as well as T1-weighted (T1W), T2-weighted (T2W) and apparent diffusion coefficient (ADC) maps for distinguishing between varying degrees of hepatic fibrosis in an experimental rat model.

Methods Liver fibrosis in rats was induced by carbon tetrachloride intraperitoneal injection for 4-12 weeks (n=30). In the control group (n=10) normal saline was applied. The MRI protocol contained T2W, diffusion weighted imaging, pre-and post-contrast image series of T1W and T1 mapping images. METAVIR score was used to grade liver fibrosis as normal (F0), mild fibrosis (F1-2), and advanced fibrosis (F3-4). Texture parameters including mean gray-level intensity (Mean), standard deviation (SD), Entropy, mean of positive pixels (MPP), Skewness, and Kurtosis were obtained. Nonparametric Mann-Whitney U test was used to compare the average value of each texture parameter in each sequence for assessing the difference between F0 and F≥1 as well as F0-2 and F3-4. Receiver operating characteristic (ROC) curves were obtained to assess the diagnosing accuracy of the parameters for differentiating no liver fibrosis from liver fibrosis and rats with liver fibrosis grading F0-2 from those with grading F3-4. The area under ROC curve (AUC) was calculated to evaluate the diagnostic efficiency of texture parameters.

Results Finally, 20 rats completed MR T1 mapping image scan. The pathologic staging of these 20 rats was no fibrosis (F0, n=6), mild fibrosis (F1-2, n=5) and advanced fibrosis (F3-4, n=9). On pre-contrast T1 mapping image, Entropy was seen to be statistically significant higher in the F≥1 group than that in the F0 group at each spatial scaling factor (SSF) setting (P=0.015, 0.015, 0.015, 0.013, 0.015 and 0.018 respectively to SSF=0, 2, 3, 4, 5, 6), and Mean of the F≥1 rats was statistically significant higher than that of the F0 rats at SSF 4, 5, 6 (P=0.004, 0.006, and 0.013, respectively). Entropy and Mean showed a moderate diagnostic performance in most SSF settings of T1 mapping pre-contrast images for differentiation of normal liver from liver fibrosis.

Conclusion Certain texture features of gadoxetic acid-enhanced MR images, especially the Entropy of non-contrast T1 mapping image, was found to be a useful biomarker for the diagnosis of liver fibrosis.

Key words: liver fibrosis, magnetic resonance imaging, texture analysis, Entropy, T1 mapping

Xu Jia, Wang Xuan, Jin Zhengyu, You Yan, Wang Qin, Wang Shitian, Xue Huadan. Value of Texture Analysis on Gadoxetic Acid-enhanced MR for Detecting Liver Fibrosis in a Rat Model[J].Chinese Medical Sciences Journal, 2019, 34(1): 24-32.

Figures/Tables 5

Figure 1.

Table 1

Table 2

Table 3

Table 4

References 29.

1.	Afdhal NH, Nunes D . Evaluation of liver fibrosis: a concise review. Am J Gastroenterol 2004; 99(6):1160-74. doi: 10.1111/j.1572-0241.2004.30110.x. doi: 10.1111/j.1572-0241.2004.30110.x pmid: 15180741
2.	Nguyen D, Talwalkar JA . Noninvasive assessment of liver fibrosis. Hepatology 2011; 53(6):2107-10. doi: 10.1002/hep.24401. doi: 10.1002/hep.24013 pmid: 21254180
3.	Lubner MG, Smith AD, Sandrasegaran K , et al. CT texture analysis: definitions, applications, biologic correlates, and challenges. RadioGraphics 2017; 37(5):1483-503. doi: 10.1148/rg.2017170056. doi: 10.1148/rg.2017170056 pmid: 28898189
4.	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):739-48. doi: 10.1148/radiol.10100853. doi: 10.1148/radiol.10100853 pmid: 21248235
5.	Barry B, Buch K, Soto JA , et al. Quantifying liver fibrosis through the application of texture analysis to diffusion weighted imaging. Magn Reson Imaging 2014; 32(1):84-90. doi: 10.1016/j.mri.2013.04.006. doi: 10.1016/j.mri.2013.04.006 pmid: 24239337
6.	Bahl G, Cruite I, Wolfson T , et al. Noninvasive classification of hepatic fibrosis based on texture parameters from double contrast-enhanced magnetic resonance images. J Magn Reson Imaging 2012; 36(5):1154-61. doi: 10.1002/jmri.23759. doi: 10.1002/jmri.23759 pmid: 22851409
7.	Jirák D, Dezortová M, Taimr P , et al. Texture analysis of human liver. J Magn Reson Imaging 2002; 15(1):68-74. doi: 10.1002/jmri.10042 pmid: 11793459
8.	Katsube T, Okada M, Kumano S , et al. Estimation of liver function using T1 mapping on Gd-EOB-DTPA-enhanced magnetic resonance imaging. Invest Radiol 2011; 46(4):277-83. doi: 10.1097/RLI.0b013e318200f67d. doi: 10.1097/RLI.0b013e3182804f84 pmid: 21343827
9.	Zhou ZP, Long LL, Huang LJ , et al. Gd-EOB-DTPA-enhanced MRI T1 mapping for assessment of liver function in rabbit fibrosis model: comparison of hepatobiliary phase images obtained at 10 and 20 min. Radiol Med 2017; 122(4):239-47. doi: 10.1007/s11547-016-0719-1. doi: 10.1007/s11547-016-0719-1
10.	Zhou ZP, Long LL, Qiu WJ , et al. Comparison of 10- and 20-min hepatobiliary phase images on Gd-EOB-DTPA-enhanced MRI T1 mapping for liver function assessment in clinic. Abdom Radiol (NY) 2017; 42(9):2272-8. doi: 10.1007/s00261-017-1143-2. doi: 10.1007/s00261-017-1143-2 pmid: 28396918
11.	Ding Y, Rao SX, Zhu T , et al. Liver fibrosis staging using T1 mapping on gadoxetic acid-enhanced MRI compared with DW imaging. Clin Radiol 2015; 70(10):1096-103. doi: 10.1016/j.crad.2015.04.014. doi: 10.1016/j.crad.2015.04.014 pmid: 26164421
12.	Pan S, Wang XQ, Guo QY . Quantitative assessment of hepatic fibrosis in chronic hepatitis B and C: T1 mapping on Gd-EOB-DTPA-enhanced liver magnetic resonance imaging. WJG 2018; 24(18):2024-35. doi: 10.3748/wjg.v24.i18.2024. doi: 10.3748/wjg.v24.i18.2024 pmid: 29760545
13.	Intraobserver and interobserver variations in liver biopsy interpretation in patients with chronic hepatitis C. The French METAVIR Cooperative Study Group. Hepatology 1994; 20(1 Pt 1):15-20. doi: 10.1002/(ISSN)1527-3350
14.	Ganeshan B, Miles KA . Quantifying tumour heterogeneity with CT. Cancer Imaging 2013; 13(1):140-9. doi: 10.1102/1470-7330.2013.0015. doi: 10.1102/1470-7330.2013.0015 pmid: 23545171
15.	Lubner MG, Malecki K, Kloke J , et al. Texture analysis of the liver at MDCT for assessing hepatic fibrosis. Abdom Radiol (NY) 2017; 42(8):2069-78. doi: 10.1007/s00261-017-1096-5. doi: 10.1007/s00261-017-1096-5 pmid: 28314916
16.	Raman SP, Schroeder JL, Huang P , et al. Preliminary data using computed tomography texture analysis for the classification of hypervascular liver lesions: generation of a predictive model on the basis of quantitative spatial frequency measurements—a work in progress. J Comput Assist Tomogr 2015; 39(3):383-95. doi: 10.1097/RCT.0000000000000217. doi: 10.1097/RCT.0000000000000217 pmid: 25700222
17.	Cannella R, Rangaswamy B, Minervini MI , et al. Value of texture analysis on gadoxetic acid-enhanced MRI for differentiating hepatocellular adenoma from focal nodular hyperplasia. AJR Am J Roentgenol 2019; 212(3):538-46. doi: 10.2214/AJR.18.20182. doi: 10.2214/AJR.18.20182
18.	Mulé S, Thiefin G, Costentin C , et al. Advanced hepatocellular carcinoma: pretreatment contrast-enhanced CT texture parameters as predictive biomarkers of survival in patients treated with sorafenib. Radiology 2018; 288(2):445-55. doi: 10.1148/radiol.2018171320. doi: 10.1148/radiol.2018171320
19.	Chen S, Zhu Y, Liu Z , et al. Texture analysis of baseline multiphasic hepatic computed tomography images for the prognosis of single hepatocellular carcinoma after hepatectomy: a retrospective pilot study. Eur J Radiol 2017; 90:198-204. doi: 10.1016/j.ejrad.2017.02.035. doi: 10.1016/j.ejrad.2017.02.035 pmid: 28583634
20.	Canellas R, Mehrkhani F, Patino M , et al. Characterization of portal vein thrombosis (neoplastic versus bland) on CT images using software-based texture analysis and thrombus density (Hounsfield Units). AJR Am J Roentgenol 2016; 207(5):W81-7. doi: 10.2214/AJR.15.15928. doi: 10.2214/AJR.15.15928 pmid: 27490095
21.	Sadot E, Simpson AL, Do RKG , et al. Cholangiocarcinoma: correlation between molecular profiling and imaging phenotypes. PLoS One 2015; 10(7):e0132953. doi: 10.1371/journal.pone.0132953. doi: 10.1371/journal.pone.0132953 pmid: 26207380
22.	Daginawala N, Li B, Buch K , et al. Using texture analyses of contrast enhanced CT to assess hepatic fibrosis. Eur J Radiol 2016; 85(3):511-7. doi: 10.1016/j.ejrad.2015.12.009. doi: 10.1016/j.ejrad.2015.12.009 pmid: 26860661
23.	Romero-Gómez M, Gómez-González E, Madrazo A , et al. Optical analysis of computed tomography images of the liver predicts fibrosis stage and distribution in chronic hepatitis C. Hepatology 2008; 47(3):810-6. doi: 10.1002/hep.22112. doi: 10.1002/hep.22112 pmid: 18098299
24.	Zhang X, Gao X, Liu BJ , et al. Effective staging of fibrosis by the selected texture features of liver: which one is better, CT or MR imaging? Comput Med Imaging Graph 2015; 46 Pt 2: 227-36. doi: 10.1016/j.compmedimag.2015.09.003. doi: 10.1016/j.compmedimag.2015.09.003 pmid: 26455963
25.	Jirák D, Dezortová M, Taimr P , et al. Texture analysis of human liver. J Magn Reson Imaging 2002; 15(1):68-74.
26.	Lubner MG, Stabo N, Lubner SJ , et al. CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging 2015; 40(7):2331-7. doi: 10.1007/s00261-015-0438-4. doi: 10.1007/s00261-015-0438-4 pmid: 25968046
27.	Ng F, Kozarski R, Ganeshan B , et al. Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol 2013; 82(2):342-8. doi: 10.1016/j.ejrad.2012.10.023. doi: 10.1016/j.ejrad.2012.10.023 pmid: 23194641
28.	Yoon JH, Lee JM, Paek M , et al. Quantitative assessment of hepatic function: modified look-locker inversion recovery (MOLLI) sequence for T1 mapping on Gd-EOB-DTPA-enhanced liver MR imaging. Eur Radiol 2016; 26(6):1775-82. doi: 10.1007/s00330-015-3994-7. doi: 10.1007/s00330-015-3994-7 pmid: 26373756
29.	Yoon JH, Lee JM, Kang H-J , et al. Quantitative assessment of liver function by using gadoxetic acid-enhanced MRI: hepatocyte uptake ratio. Radiology 2019; 290(1):125-33. doi: 10.1148/radiol.2018180753. doi: 10.1148/radiol.2018180753

Comparison	Sequence	Texture parameter	SSF	Uvalue	Pvalue
F0 vs. F1-4	Pre-contrast T1 mapping	Mean	4	114.0	0.004
			5	113.0	0.006
			6	109.0	0.013
		Entropy	0	108.0	0.015
			2	107.5	0.015
			3	108.0	0.015
			4	114.0	0.013
			5	113.0	0.015
			6	109.0	0.018
	Post-contrast T1 mapping (20 min)	Mean	6	21.0	0.039
	Post-contrast T1 mapping (60 min)	Kurtosis	6	76.5	0.025
F0-2 vs. F3-4	Pre-contrast T1 mapping	Skewness	4	103.0	0.036

Comparison	Sequence	Texture parameter	SSF	U value	P value
F0 vs. F1-4	Pre-contrast T1W	Mean	2	39.0	0.045
			3	35.0	0.025
			4	37.0	0.034
		MPP	3	34.0	0.021
			4	33.0	0.018
	Post-contrast T1W (20 min)	SD	0	98.0	0.013
		Entropy	0	94.5	0.023
		Kurtosis	2	98.5	0.011
	Post-contrast T1W (60 min)	SD	0	40.0	0.018
		Entropy	0	39.5	0.028
		MPP	3	6.0	0.040
			4	6.0	0.040
		Skewness	3	41.0	0.010
			4	40.0	0.018
F0-2 vs. F3-4	Pre-contrast T1W	Skewness	0	122.0	0.041
	Post-contrast T1W (20 min)	SD	0	112.0	0.004
		Entropy	0	101.5	0.027
		Kurtosis	2	103.0	0.023
	Post-contrast T1W (60 min)	SD	0	52.0	0.004
		Entropy	0	51.5	0.004

Sequence	Comparison	Texture parameter	SSF	AUC	95%CI	Pvalue	Threshold	Sensitivity	Specificity
Pre-contrast T1 mapping	F0 vs. F1-4	Mean	4	0.857	0.711, 1.000	0.006	>-391.855	0.684	1.000
			5	0.850	0.701, 0.999	0.007	>-608.24	0.789	1.000
			6	0.820	0.635, 1.000	0.014	>-745.89	0.789	0.857
		Entropy	0	0.812	0.638, 0.986	0.016	>5.305	0.632	1.000
			2	0.808	0.631, 0.985	0.018	>5.655	0.632	0.857
			3	0.812	0.636, 0.988	0.016	>5.210	0.632	0.857
			4	0.816	0.641, 0.991	0.015	>5.185	0.632	0.857
			5	0.812	0.637, 0.987	0.016	>5.225	0.632	0.857
			6	0.805	0.629, 0.980	0.019	>5.170	0.632	0.857
	F0-2 vs.F3-4	Skewness	4	0.747	0.554, 0.939	0.037	>-0.265	0.900	0.625
Post-contrast T1 mapping (60 min)	F0 vs. F1-4	Kurtosis	6	0.797	0.592, 1.000	0.028	>-0.390	0.833	0.750

Sequence	Comparison	Texture parameter	SSF	AUC	95%CI	Pvalue	Threshold	Sensitivity	Specificity
Pre-contrast T1W	F0 vs. F1-4	Mean	2	0.745	0.556, 0.934	0.043	<131.57	0.765	0.556
			3	0.771	0.586, 0.856	0.025	<186.005	0.706	0.778
			4	0.758	0.561, 0.955	0.033	<255	0.647	0.556
		MPP	3	0.778	0.592, 0.964	0.022	<227.22	0.706	0.778
			4	0.784	0.600, 0.969	0.019	<320.61	0.765	0.667
Post-contrast T1W (20 min)	F0 vs. F1-4	SD	0	0.817	0.642, 0.991	0.014	>53.785	0.667	1.000
		Entropy	0	0.788	0.599, 0.976	0.026	>5.04	0.667	0.825
		Kurtosis	2	0.821	0.644, 0.998	0.013	>0.700	0.600	0.875
	F0-2 vs. F3-4	SD	0	0.848	0.689, 1.000	0.005	>53.785	0.727	0.833
		Entropy	0	0.769	0,572, 0.966	0.029	>5.04	0.727	0.750
		Kurtosis	2	0.780	0.582, 0.979	0.023	>0.155	0.818	0.593
Post-contrast TIW (60 min)	F0 vs. F1-4	SD	0	0.909	0.739, 1.000	0.019	>36.375	0.909	1.000
		Entropy	0	0.898	0.735, 1.000	0.022	>4.920	0.727	1.000
		MPP	3	0.864	0.670, 1.000	0.037	<303.140	0.909	0.750
			4	0.864	0.671, 1.000	0.037	<351.545	0.818	1.000
		Skewness	3	0.932	0.794, 1.000	0.013	>-0.115	0.909	1.000
			4	0.909	0.754, 1.000	0.019	>-0.160	0.818	1.000
	F0-2 vs. F3-4	SD	0	0.929	0.782, 1.000	0.005	>37.345	1.000	0.857
		Entropy	0	0.920	0.781, 1.000	0.007	>4.920	0.875	0.857
			3	0.804	0.569, 1.000	0.049	>6.050	0.750	0.857
			4	0.804	0.573, 1.000	0.049	>6.095	0.625	0.857

[1]	Atefeh Beigi-khoozani, Amirmohammad Merajikhah, Mahdieh Soleimani. Magnetic Resonance Imaging Findings of Olfactory Bulb in Anosmic Patients with COVID-19: A Systematic Review [J]. Chinese Medical Sciences Journal, 2022, 37(1): 23-30.
[2]	Jia Xu, Xuan Wang, Zhengyu Jin, Qin Wang, Yan You, Shitian Wang, Tianyi Qian, Huadan Xue. Assessing Liver Function by T1 Maps on Gd-EOB-DTPA-Enhanced MRI for up to 50 Min in Rat Models of Liver Fibrosis: A Longer Hepatobiliary Time Period may Help [J]. Chinese Medical Sciences Journal, 2021, 36(2): 110-119.
[3]	Jian Cao, Guorong Wang, Zhiwei Wang, Zhengyu Jin. CT Texture Analysis: A Potential Biomarker for Evaluating KRAS Mutational Status in Colorectal Cancer [J]. Chinese Medical Sciences Journal, 2020, 35(4): 306-314.
[4]	Wang Xuedan, Wang Shiwei, Wang Botao, Chen Zhiye. Effect of MR Field Strength on the Texture Features of Cerebral T2-FLAIR Images: A Pilot Study [J]. Chinese Medical Sciences Journal, 2020, 35(3): 248-253.
[5]	Xu Yanhong,Yang Jia,Meng Jie,Wang Han. Targeted MR Imaging Adopting T1-Weighted Ultra-Small Iron Oxide Nanoparticles for Early Hepatocellular Carcinoma: An in vitro and in vivo Study [J]. Chinese Medical Sciences Journal, 2020, 35(2): 142-150.
[6]	Wang Yingwei, Zhang Xinghua, Wang Botao, Wang Ye, Liu Mengqi, Wang Haiyi, Ye Huiyi, Chen Zhiye. Value of Texture Analysis of Intravoxel Incoherent Motion Parameters in Differential Diagnosis of Pancreatic Neuroendocrine Tumor and Pancreatic Adenocarcinoma [J]. Chinese Medical Sciences Journal, 2019, 34(1): 1-9.
[7]	Wang Botao, Liu Mingxia, Chen Zhiye. Differential Diagnostic Value of Texture Feature Analysis of Magnetic Resonance T2 Weighted Imaging between Glioblastoma and Primary Central Neural System Lymphoma [J]. Chinese Medical Sciences Journal, 2019, 34(1): 10-17.
[8]	Liu Hongjuan, Zhou Huanfen, Zong Linxiong, Liu Mengqi, Wei Shihui, Chen Zhiye. MRI Histogram Texture Feature Analysis of the Optic Nerve in the Patients with Optic Neuritis [J]. Chinese Medical Sciences Journal, 2019, 34(1): 18-23.
[9]	Wang Botao, Fan Wenping, Xu Huan, Li Lihui, Zhang Xiaohuan, Wang Kun, Liu Mengqi, You Junhao, Chen Zhiye. Value of Magnetic Resonance Imaging Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors [J]. Chinese Medical Sciences Journal, 2019, 34(1): 33-37.
[10]	Wang Guorong, Wang Zhiwei, Jin Zhengyu. Application and Progress of Texture Analysis in the Therapeutic Effect Prediction and Prognosis of Neoadjuvant Chemoradiotherapy for Colorectal Cancer [J]. Chinese Medical Sciences Journal, 2019, 34(1): 45-50.
[11]	Li Ping, Zhu Liang, Wang Xuan, Xue Huadan, Wu Xin, Jin Zhengyu. Imaging Diagnosis of Type Ⅲ Choledochal Cyst: A Case Report [J]. Chinese Medical Sciences Journal, 2018, 33(3): 194-203.
[12]	Chen Zhiye, Liu Mengqi, Ma Lin. Cortical Thinning Pattern of Bulbar- and Spinal-onset Amyotrophic Lateral Sclerosis: a Surface-based Morphometry Study [J]. Chinese Medical Sciences Journal, 2018, 33(2): 100-106.
[13]	Li Lihui, Huang Houbin, Chen Zhiye. Early Diagnosis of Recurrent Optic Neuritis Using Contrast-Enhanced T2 Fluid-attenuated Inversion Recovery Imaging: a Case Report [J]. Chinese Medical Sciences Journal, 2018, 33(2): 130-134.
[14]	Chen Zhiye,Liu Mengqi,Ma Lin. Gray Matter Volume Changes over the Whole Brain in the Bulbar- and Spinal-onset Amyotrophic Lateral Sclerosis: a Voxel-based Morphometry Study [J]. Chinese Medical Sciences Journal, 2018, 33(1): 20-28.
[15]	Liu Mengqi, Chen Zhiye, Ma Lin. Reliability of Three Dimentional Pseudo-continuous Arterial Spin Labeling: A Volumetric Cerebral Perfusion Imaging with Different Post-labeling Time and Functional State in Health Adults [J]. Chinese Medical Sciences Journal, 2018, 33(1): 38-44.

Early edition

Current issue

Archive

Specialties & Topics

Author center

Reviewer center

Value of Texture Analysis on Gadoxetic Acid-enhanced MR for Detecting Liver Fibrosis in a Rat Model

RichHTML

PDF (PC)

Knowledge

Highlight

Abstract

Cite this article

share this article

Figures/Tables 5

References 29.

Related Articles 15

Metrics

Recommended 10