Application progress of MRI-based artificial intelligence in the diagnosis of liver fibrosis

Share this content in WeChat

• Review •

FAN Fengxian JIANG Yanli WANG Jun HUANG Wenjing ZHANG Pengfei ZHANG Jing

Cite this article as: Fan FX, Jiang YL, Wang J, et al. Application progress of MRI-based artificial intelligence in the diagnosis of liver fibrosis[J]. Chin J Magn Reson Imaging, 2021, 12(3): 105-108. DOI:10.12015/issn.1674-8034.2021.03.026.

Abstract

[Abstract] Liver fibrosis is a necessary pathway for liver cirrhosis and severe complications and liver cancer in chronic liver diseases. Noninvasive diagnosis of liver fibrosis has been a hot topic in clinical research. With the continuous development of imaging technology and computer science, artificial intelligence techniques such as radiomics and machine learning can extract a large number of quantitative features from medical images which cannot be recognized by human eyes to achieve diagnosis prognosis evaluation and therapeutic prediction of the disease. Recently artificial intelligence technology has been deeply studied in liver fibrosis. This paper reviews the progress of clinical application of radiomics and machine learning based on MR imaging in diagnosis and staging of liver fibrosis.

[Keywords] liver fibrosis；magnetic resonance imaging；radiomics；machine learning；artificial intelligence

Contributor Information

FAN Fengxian^{1,
2}   JIANG Yanli¹   WANG Jun^{1,
2}   HUANG Wenjing^{1,
2}   ZHANG Pengfei^{1,
2}   ZHANG Jing^1*

¹ Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730030, China

² The Second Clinical Medicine College of Lanzhou University, Lanzhou 730000, China

Zhang J, E-mail: lztong2001@163.com

Conflicts of interest None.

Publication Information

ACKNOWLEDGMENTS This article is supported by the Lanzhou Chengguan District Science and Technology Plan Project No. 2019SHFZ0037

Received 2020-10-18

Accepted 2021-01-21

DOI: 10.12015/issn.1674-8034.2021.03.026

Text

References

Hytiroglou P, Theise ND. Regression of human cirrhosis: an update, 18 years after the pioneering article by Wanlesset al. Virchows Arch, 2018, 473(1): 15-22. DOI: 10.1007/s00428-018-2340-2

The French METAVIR Cooperative Study Group. Intraobserver and interobserver variations in liver biopsy interpretation in patients with chronic hepatitis C. Hepatology, 1994, 20(1Pt 1): 15-20.

Rockey DC, Caldwell SH, Goodman ZD, et al. Liver biopsy. Hepatology, 2009, 49(3): 1017-1044. DOI: 10.1002/hep.22742

Hoodeshenas S, Yin M, Venkatesh SK. Magnetic resonance elastography of liver: current update. Top Magn Reson Imaging, 2018, 27(5): 319-333. DOI: 10.1097/rmr.0000000000000177

Jiang H, Chen J, Gao R, et al. Liver fibrosis staging with diffusion-weighted imaging: a systematic review and meta-analysis. Abdom Radiol (NY), 2017, 42(2): 490-501. DOI: 10.1007/s00261-016-0913-6

Ozkurt H, Keskiner F, Karatag O, et al. Diffusion weighted mri for hepatic fibrosis: impact of b-value. Iran J Radiol, 2014, 11(1): e3555. DOI: 10.5812/iranjradiol.3555

Li X, Liu H, Wang R, et al. Gadoxetate-disodium-enhanced magnetic resonance imaging for liver fibrosis staging: a systematic review and meta-analysis. Clin Radiol, 2020, 75(4): e311-319. DOI: 10.1016/j.crad.2019.11.001

Singh A, Reddy D, Haris M, et al. T1ρ MRI of healthy and fibrotic human livers at 1.5T. J Transl Med, 2015, 13(1): 292. DOI: 10.1186/s12967-015-0648-0

Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036

Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169

Yang ZX, Hu XX, Grimm R, et al. Value of whole-liver apparent diffusion coefficient histogram analysis for quantification of liver fibrosis stages. Chin J Acad Radiol, 2019, 1(1): 6-12. DOI: 10.1007/s42058-019-00004-3

Zheng Y, Xu YS, Liu Z, et al. Whole-liver apparent diffusion coefficient histogram analysis for the diagnosis and staging of liver fibrosis. J Magn Reson Imaging, 2020, 51(6): 1745-1754. DOI: 10.1002/jmri.26987

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. Abdom Radiol (NY), 2017, 42(12): 2855-2863. DOI: 10.1007/s00261-017-1208-2

Sheng RF, Jin KP, Yang L, et al. Histogram analysis of diffusion kurtosis magnetic resonance imaging for diagnosis of hepatic fibrosis. Korean J Radiol, 2018, 19(5): 916-922. DOI: 10.3348/kjr.2018.19.5.916

Asayama Y, Nishie A, Ishigami K, et al. Histogram analysis of noncancerous liver parenchyma on gadoxetic acid-enhanced MRI: predictive value for liver function and pathology. Abdom Radiol (NY), 2016, 41(9): 1751-1757. DOI: 10.1007/s00261-016-0753-4

Yang ZX, Liang HY, Hu XX, et al. Feasibility of histogram analysis of susceptibility-weighted MRI for staging of liver fibrosis. Diagn Interv Radiol, 2016, 22(4): 301-307. DOI: 10.5152/dir.2016.15284

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): 227-236. DOI: 10.1016/j.compmedimag.2015.09.003

Yu H, Touret AS, Li B, et al. Application of texture analysis on parametric T1 and T2 maps for detection of hepatic fibrosis. J Magn Reson Imaging, 2017, 45(1): 250-259. DOI: 10.1002/jmri.25328

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

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): 1154-1161. DOI: 10.1002/jmri.23759

Yokoo T, Wolfson T, Iwaisako K, et al. Evaluation of liver fibrosis using texture analysis on combined-contrast-enhanced magnetic resonance images at 3.0 T. Biomed Res Int, 2015, 2015: 387653. DOI: 10.1155/2015/387653

Yu H, Buch K, Li B, et al. Utility of texture analysis for quantifying hepatic fibrosis on proton density MRI. J Magn Reson Imaging, 2015, 42(5): 1259-1265. DOI: 10.1002/jmri.24898

Li B, Jara H, Yu H, et al. Enhanced laws textures: a potential MRI surrogate marker of hepatic fibrosis in a murine model. Magn Reson Imaging, 2017, 37: 33-40. DOI: 10.1016/j.mri.2016.11.008

Park HJ, Lee SS, Park B, et al. Radiomics analysis of gadoxetic acid-enhanced MRI for staging liver fibrosis. Radiology, 2019, 292(1): 269. DOI: 10.1148/radiol.2019194012

Brynolfsson P, Nilsson D, Torheim T, et al. Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters. Sci Rep, 2017, 7(1): 4041. DOI: 10.1038/s41598-017-04151-4

House MJ, Bangma SJ, Thomas M, et al. Texture-based classification of liver fibrosis using MRI. J Magn Reson Imaging, 2015, 41(2): 322-328. DOI: 10.1002/jmri.24536

Hu W, Yang H, Xu H, et al. Radiomics based on artificial intelligence in liver diseases: where we are?Gastroenterol Rep (Oxf), 2020, 8(2): 90-97. DOI: 10.1093/gastro/goaa011

Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762. DOI: 10.1038/nrclinonc.2017.141

He L, Li H, Dudley JA, et al. Machine learning prediction of liver stiffness using clinical and T2-weighted MRI radiomic data. AJR Am J Roentgenol, 2019, 213(3): 592-601. DOI: 10.2214/ajr.19.21082

Schawkat K, Ciritsis A, Von Ulmenstein S, et al. Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology. Eur Radiol, 2020, 30(8): 4675-4685. DOI: 10.1007/s00330-020-06831-8

Wu Z, Matsui O, Kitao A, et al. Hepatitis C related chronic liver cirrhosis: feasibility of texture analysis of MR images for classification of fibrosis stage and necroinflammatory activity grade. PLoS One, 2015, 10(3): e0118297. DOI: 10.1371/journal.pone.0118297

Cannella R, Borhani AA, Tublin M, et al. Diagnostic value of MR-based texture analysis for the assessment of hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD). Abdom Radiol (NY), 2019, 44(5): 1816-1824. DOI: 10.1007/s00261-019-01931-6

Bini SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care?J Arthroplasty, 2018, 33(8): 2358-2361. DOI: 10.1016/j.arth.2018.02.067

Yasaka K, Akai H, Kunimatsu A, et al. Liver fibrosis: deep convolutional neural network for staging by using gadoxetic acid-enhanced hepatobiliary phase MR images. Radiology, 2018, 287(1): 146-155. DOI: 10.1148/radiol.2017171928

Soufi M, Otake Y, Hori M, et al. Liver shape analysis using partial least squares regression-based statistical shape model: application for understanding and staging of liver fibrosis. Int J Comput Assist Radiol Surg, 2019, 14(12): 2083-2093. DOI: 10.1007/s11548-019-02084-z

Yao H, Zhang X, Zhou X, et al. Parallel structure deep neural network using CNN and RNN with an attention mechanism for breast cancer histology image classification. Cancers (Basel), 2019, 11(12): 1901. DOI: 10.3390/cancers11121901

PREV Research progress of background parenchymal enhancement in breast magnetic resonance imaging

NEXT The application of radiomics in intrahepatic cholangiocarcinoma

LINK

Tel & Fax: +8610-67113815 E-mail: editor@cjmri.cn