Share：
Share this content in WeChat
X
[Chinese][PDF]77761
• Reviews •
Application progress of MRI radiomics in the evaluation of liver fibrosis
ZHANG Die  ZHANG Chen  WANG Lifei 

Cite this article as: Zhang D, Zhang C, Wang LF. Application progress of MRI radiomics in the evaluation of liver fibrosis[J]. Chin J Magn Reson Imaging, 2022, 13(3): 162-165. DOI:10.12015/issn.1674-8034.2022.03.039.


[Abstract] Liver fibrosis is an important intermediate process in the development of various chronic liver diseases to liver cirrhosis. Therefore, the noninvasive and accurate evaluation of liver fibrosis can provide important information for timely intervention and post-treatment assessment. Radiomics analysis can extract a variety of features from medical image, and those features were furtherly used to diagnose and evaluate for various diseases. With the development of technology of magnetic resonance (MR) imaging and computer science, MR radiomics analysis shows its outstanding value and application prospect in the diagnosis and staging of liver fibrosis. Among them, the radiomics analysis based on the images derived from conventional MR, enhanced MR and other functional or quantitative MR has achieved excellent performance. However, several important limitations and challenges also needed to be solved, such as the inherent limitations of radiomics analysis and the impact of liver morphology and pathological status. Consequently, this article reviews the use of MR radiomics analysis and objective challenges at present for staging liver fibrosis.
[Keywords] liver fibrosis；chronic liver disease；radiomics；magnetic resonance imaging；quantitative evaluation

ZHANG Die   ZHANG Chen   WANG Lifei*  

Department of Radiology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, the Second Affiliated Hospital, School of Medicine Southern University of Science and Technology, Shenzhen 518000, China

Wang LF, E-mail: wanglf007n@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Sanming Project of Medicine in Shenzhen (No. SZSM201612053).
Received  2021-08-05
Accepted  2022-02-18
DOI: 10.12015/issn.1674-8034.2022.03.039
Cite this article as: Zhang D, Zhang C, Wang LF. Application progress of MRI radiomics in the evaluation of liver fibrosis[J]. Chin J Magn Reson Imaging, 2022, 13(3): 162-165. DOI:10.12015/issn.1674-8034.2022.03.039.

[1]
Afdhal NH, Nunes D. Evaluation of liver fibrosis: a concise review[J]. Am J Gastroenterol, 2004, 99(6): 1160-1174. DOI: 10.1111/j.1572-0241.2004.30110.x.
[2]
Regev A, Berho M, Jeffers LJ, et al. Sampling error and intraobserver variation in liver biopsy in patients with chronic HCV infection[J]. Am J Gastroenterol, 2002, 97(10): 2614-2618. DOI: 10.1111/j.1572-0241.2002.06038.x.
[3]
Goldin RD, Goldin JG, Burt AD, et al. Intra-observer and inter-observer variation in the histopathological assessment of chronic viral hepatitis[J]. J Hepatol, 1996, 25(5): 649-654. DOI: 10.1016/s0168-8278(96)80234-0.
[4]
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[5]
Park HJ, Park B, Lee SS. Radiomics and deep learning: hepatic applications[J]. Korean J Radiol, 2020, 21(4): 387-401. DOI: 10.3348/kjr.2019.0752.
[6]
Zhang XJ, Xin G, Liu BJ, et al. Effective staging of fibrosis by the selected texture features of liver: which one is better, CT or MR imaging?[J]. Comput Med Imaging Graph, 2015, 46Pt 2: 227-236. DOI: 10.1016/j.compmedimag.2015.09.003.
[7]
Mao LT, Chen H, Liang MZ, et al. Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening[J]. Quant Imaging Med Surg, 2019, 9(2): 263-272. DOI: 10.21037/qims.2019.02.02.
[8]
Yu HS, Touret AS, Li BJ, et al. Application of texture analysis on parametric T1 and T2 maps for detection of hepatic fibrosis[J]. J Magn Reson Imaging, 2017, 45(1): 250-259. DOI: 10.1002/jmri.25328.
[9]
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[J]. Eur Radiol, 2020, 30(8): 4675-4685. DOI: 10.1007/s00330-020-06831-8.
[10]
Zhu XC. Editorial for "radiomics approaches for predicting liver fibrosis with nonenhanced T1-weighted imaging: comparison of different radiomics models"[J]. J Magn Reson Imaging, 2021, 53(4): 1090-1091. DOI: 10.1002/jmri.27425.
[11]
House MJ, Bangma SJ, Thomas M, et al. Texture-based classification of liver fibrosis using MRI[J]. J Magn Reson Imaging, 2015, 41(2): 322-328. DOI: 10.1002/jmri.24536.
[12]
Zhao R, Gong XJ, Ge YQ, et al. Use of texture analysis on noncontrast MRI in classification of early stage of liver fibrosis[J]. Can J Gastroenterol Hepatol, 2021, 2021: 6677821. DOI: 10.1155/2021/6677821.
[13]
Aguirre DA, Behling CA, Alpert E, et al. Liver fibrosis: noninvasive diagnosis with double contrast material-enhanced MR imaging[J]. Radiology, 2006, 239(2): 425-437. DOI: 10.1148/radiol.2392050505.
[14]
Hughes-Cassidy F, Chavez AD, Schlang A, et al. Superparamagnetic iron oxides and low molecular weight gadolinium chelates are synergistic for direct visualization of advanced liver fibrosis[J]. J Magn Reson Imaging, 2007, 26(3): 728-737. DOI: 10.1002/jmri.21066.
[15]
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]. J Magn Reson Imaging, 2012, 36(5): 1154-1161. DOI: 10.1002/jmri.23759.
[16]
Yokoo T, Wolfson T, Iwaisako K, et al. Evaluation of liver fibrosis using texture analysis on combined-contrast-enhanced magnetic resonance images at 3.0T[J]. Biomed Res Int, 2015, 2015: 387653. DOI: 10.1155/2015/387653.
[17]
Zhou W, Hu HJ, Shen B, et al. Application values of gadolinium-ethoxybenzyl diethylenetriaminepentaacetic acid enhanced magnetic resonance imaging-based radiomics in the quantitative assessment of liver reserve function of patients with liver cirrhosis[J]. Acta Acad Med Sin, 2020, 42(4): 459-467. DOI: 10.3881/j.issn.1000-503X.12620.
[18]
Park HJ, Lee SS, Park B, et al. Radiomics analysis of gadoxetic acid-enhanced MRI for staging liver fibrosis[J]. Radiology, 2019, 292(1): 269. DOI: 10.1148/radiol.2019194012.
[19]
Zheng RC, Shi CZ, Wang CY, et al. Imaging-based staging of hepatic fibrosis in patients with hepatitis B: a dynamic radiomics model based on Gd-EOB-DTPA-enhanced MRI[J]. Biomolecules, 2021, 11(2): 307. DOI: 10.3390/biom11020307.
[20]
Yu HS, Buch K, Li BJ, et al. Utility of texture analysis for quantifying hepatic fibrosis on proton density MRI[J]. J Magn Reson Imaging, 2015, 42(5): 1259-1265. DOI: 10.1002/jmri.24898.
[21]
Yang ZX, Hu XX, Grimm R, et al. Value of whole-liver apparent diffusion coefficient histogram analysis for quantification of liver fibrosis stages[J]. Chin J Acad Radiol, 2019, 1(1): 6-12. DOI: 10.1007/s42058-019-00004-3.
[22]
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.
[23]
Qiu QT, Zhang J, Duan JH, et al. Development and validation of radiomics model built by incorporating machine learning for identifying liver fibrosis and early-stage cirrhosis[J]. Chin Med J (Engl), 2020, 133(22): 2653-2659. DOI: 10.1097/CM9.0000000000001113.
[24]
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.
[25]
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.
[26]
Liu HF, Wang Q, du YN, et al. Whole-liver histogram analysis of blood oxygen level-dependent functional magnetic resonance imaging in evaluating hepatic fibrosis[J]. Ann Palliat Med, 2021, 10(3): 2567-2576. DOI: 10.21037/apm-20-1753.
[27]
Xu XX, Zhu H, Li RK, et al. Whole-liver histogram and texture analysis on T1 maps improves the risk stratification of advanced fibrosis in NAFLD[J]. Eur Radiol, 2021, 31(3): 1748-1759. DOI: 10.1007/s00330-020-07235-4.
[28]
Yoshikawa M, Kudo K, Harada T, et al. Quantitative susceptibility mapping versus R2*-based histogram analysis for evaluating liver fibrosis: preliminary results[J]. Magn Reson Med Sci, 2021, DOI: . DOI: 10.2463/mrms.mp.2020-0175.
[29]
Avanzo M, Wei LS, Stancanello J, et al. Machine and deep learning methods for radiomics[J]. Med Phys, 2020, 47(5): e185-e202. DOI: 10.1002/mp.13678.
[30]
Korte JC, Cardenas C, Hardcastle N, et al. Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer[J]. Sci Rep, 2021, 11(1): 17633. DOI: 10.1038/s41598-021-96600-4.
[31]
Foy JJ, Robinson KR, Li H, et al. Variation in algorithm implementation across radiomics software[J]. J Med Imaging (Bellingham), 2018, 5(4): 044505. DOI: 10.1117/1.JMI.5.4.044505.
[32]
Traverso A, Wee L, Dekker A, et al. Repeatability and reproducibility of radiomic features: a systematic review[J]. Int J Radiat Oncol Biol Phys, 2018, 102(4): 1143-1158. DOI: 10.1016/j.ijrobp.2018.05.053.
[33]
Homayounieh F, Saini S, Mostafavi L, et al. Accuracy of radiomics for differentiating diffuse liver diseases on non-contrast CT[J]. Int J Comput Assist Radiol Surg, 2020, 15(10): 1727-1736. DOI: 10.1007/s11548-020-02212-0.
[34]
Zhou WY, Li XJ, Zhang N, et al. The combination of conventional ultrasound and shear-wave elastography in evaluating the segmental heterogeneity of liver fibrosis in biliary atresia patients after Kasai portoenterostomy[J]. Pediatr Surg Int, 2021, 37(8): 1099-1108. DOI: 10.1007/s00383-021-04920-3.
[35]
Rezvani Habibabadi R, Khoshpouri P, Ghadimi M, et al. Comparison between ROI-based and volumetric measurements in quantifying heterogeneity of liver stiffness using MR elastography[J]. Eur Radiol, 2020, 30(3): 1609-1615. DOI: 10.1007/s00330-019-06478-0.
[36]
Yeung J, Ganeshan B, Endozo R, et al. Equilibrium CT texture analysis for the evaluation of hepatic fibrosis: preliminary evaluation against histopathology and extracellular volume fraction[J]. J Pers Med, 2020, 10(2): E46. DOI: 10.3390/jpm10020046.
[37]
Ozaki K, Matsui O, Kobayashi S, et al. Selective atrophy of the middle hepatic venous drainage area in hepatitis C-related cirrhotic liver: morphometric study by using multidetector CT[J]. Radiology, 2010, 257(3): 705-714. DOI: 10.1148/radiol.10100468.
[38]
Pickhardt PJ, Malecki K, Kloke J, et al. Accuracy of liver surface nodularity quantification on MDCT as a noninvasive biomarker for staging hepatic fibrosis[J]. AJR Am J Roentgenol, 2016, 207(6): 1194-1199. DOI: 10.2214/AJR.16.16514.
[39]
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[J]. Int J Comput Assist Radiol Surg, 2019, 14(12): 2083-2093. DOI: 10.1007/s11548-019-02084-z.
[40]
Hori M, Okada T, Higashiura K, et al. Quantitative imaging: quantification of liver shape on CT using the statistical shape model to evaluate hepatic fibrosis[J]. Acad Radiol, 2015, 22(3): 303-309. DOI: 10.1016/j.acra.2014.10.001.
[41]
Powell EE, Ali A, Clouston AD, et al. Steatosis is a cofactor in liver injury in hemochromatosis[J]. Gastroenterology, 2005, 129(6): 1937-1943. DOI: 10.1053/j.gastro.2005.09.015.
[42]
Zhao RY, Hamilton G, Brittain JH, et al. Design and evaluation of quantitative MRI phantoms to mimic the simultaneous presence of fat, iron, and fibrosis in the liver[J]. Magn Reson Med, 2021, 85(2): 734-747. DOI: 10.1002/mrm.28452.
[43]
Hoad CL, Palaniyappan N, Kaye P, et al. A study of T₁ relaxation time as a measure of liver fibrosis and the influence of confounding histological factors[J]. NMR Biomed, 2015, 28(6): 706-714. DOI: 10.1002/nbm.3299.
[44]
Verloh N, Probst U, Utpatel K, et al. Influence of hepatic fibrosis and inflammation: correlation between histopathological changes and Gd-EOB-DTPA-enhanced MR imaging[J]. PLoS One, 2019, 14(5): e0215752. DOI: 10.1371/journal.pone.0215752.
[45]
Rehm JL, Wolfgram PM, Hernando D, et al. Proton density fat-fraction is an accurate biomarker of hepatic steatosis in adolescent girls and young women[J]. Eur Radiol, 2015, 25(10): 2921-2930. DOI: 10.1007/s00330-015-3724-1.
[46]
Horng DE, Hernando D, Reeder SB. Quantification of liver fat in the presence of iron overload[J]. J Magn Reson Imaging, 2017, 45(2): 428-439. DOI: 10.1002/jmri.25382.

PREV Application progress of MRI radiomics in the efficacy and prognosis of neoadjuvant chemotherapy for breast cancer
NEXT Research advances in radiogenomics
  


LINK


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