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Research progress of preoperative prediction of microvascular invasion of hepatocellular carcinoma based on magnetic resonance imaging
HU Guangchao  ZHANG Qianqian  MAO Ning  LI Naixuan 

Cite this article as: Hu GC, Zhang QQ, Mao N, et al. Research progress of preoperative prediction of microvascular invasion of hepatocellular carcinoma based on magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2022, 13(2): 159-162. DOI:10.12015/issn.1674-8034.2022.02.040.


[Abstract] Hepatocellular carcinoma (HCC) is a common malignant tumor in the world, the primary treatment of HCC is surgical resection, but recurrence after surgical treatment is common, a large part of the reason is related to microvascular invasion. Therefore, looking for a non-invasive method to predict microvascular invasion before operation is of great significance for guiding surgical treatment, improving the prognosis and improving the survival rate of patients. Multi-sequence, multimodal magnetic resonance imaging(MRI) and MRI-based radiomics and deep learning technology are developing rapidly, which makes preoperative non-invasive prediction of microvascular invasion in hepatocellular carcinoma possible and highly promising. This paper mainly reviewed in this respect.
[Keywords] hepatocellular carcinoma；microvascular invasion；magnetic resonance imaging；radiomic；deep learning

HU Guangchao1   ZHANG Qianqian2   MAO Ning2   LI Naixuan3*  

1 School of Medical Imaging, Binzhou Medical University, Yantai 264000, China

2 Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai 264000, China

3 Department of Vascular Interventional Surgery, Yantai Affiliated Hospital of Binzhou Medical University, Yantai 264000, China

Li NX, E-mail: xuannaili@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Traditional Chinese Medicine Science and Technology Development Plan of Shandong Province (No. 2019-0501).
Received  2021-09-24
Accepted  2022-01-30
DOI: 10.12015/issn.1674-8034.2022.02.040
Cite this article as: Hu GC, Zhang QQ, Mao N, et al. Research progress of preoperative prediction of microvascular invasion of hepatocellular carcinoma based on magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2022, 13(2): 159-162. DOI:10.12015/issn.1674-8034.2022.02.040.

[1]
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.
[2]
Expert consensus on multidisciplinary diagnosis and treatment of precancerous lesions of hepatocellular carcinoma (2020 edition)[J]. J Clin Hepatol, 2020, 28(1): 14-20. DOI: 10.3760/cma.j.issn.1007-3418.2020.01.005.
[3]
National Health Commission of the People's Republic of China Medical Administration and Hospital Administration. Standardization for diagnosis and treatment of hepatocellular carcinoma (2019 edition)[J]. Chin J Dig Surg, 2020, 19(1): 1-20. DOI: 10.3760/cma.j.issn.1673-9752.2020.01.001.
[4]
Nuta, Shingaki N, Ida Y, et al. Irregular defects in hepatocellular carcinomas during the kupffer phase of contrast-enhanced ultrasonography with perfluorobutane microbubbles: pathological features and metastatic recurrence after surgical resection[J]. Ultrasound Med Biol, 2017, 43(9): 1829-1836. DOI: 10.1016/j.ultrasmedbio.2017.04.023.
[5]
Chou CT, Chen RC, Lee CW, et al. Prediction of microvascular invasion of hepatocellular carcinoma by pre-operative CT imaging[J]. Br J Radiol, 2012, 85(1014): 778-783. DOI: 10.1259/bjr/65897774.
[6]
Kim AY, Sinn DH, Jeong WK, et al. Hepatobiliary MRI as novel selection criteria in liver transplantation for hepatocellular carcinoma[J]. J Hepatol, 2018, 68(6): 1144-1152. DOI: 10.1016/j.jhep.2018.01.024.
[7]
Ariizumi S, Kitagawa K, Kotera Y, et al. A non-smooth tumor margin in the hepatobiliary phase of gadoxetic acid disodium (Gd-EOB-DTPA)-enhanced magnetic resonance imaging predicts microscopic portal vein invasion, intrahepatic metastasis, and early recurrence after hepatectomy in patients with hepatocellular carcinoma[J]. J Hepatobiliary Pancreat Sci, 2011, 18(4): 575-585. DOI: 10.1007/s00534-010-0369-y.
[8]
Lee S, Kim SH, Lee JE, et al. Preoperative gadoxetic acid-enhanced MRI for predicting microvascular invasion in patients with single hepatocellular carcinoma[J]. J Hepatol, 2017, 67(3): 526-534. DOI: 10.1016/j.jhep.2017.04.024.
[9]
Huang MQ, Liao B, Xu P, et al. Prediction of microvascular invasion in hepatocellular carcinoma: preoperative Gd-EOB-DTPA-dynamic enhanced MRI and histopathological correlation[J]. Contrast Media Mol Imaging, 2018, 2018: 9674565. DOI: 10.1155/2018/9674565.
[10]
Ryu T, Takami Y, Wada Y, et al. A clinical scoring system for predicting microvascular invasion in patients with hepatocellular carcinoma within the Milan criteria[J]. J Gastrointest Surg, 2019, 23(4): 779-787. DOI: 10.1007/s11605-019-04134-y.
[11]
Hong SB, Choi SH, Kim SY, et al. MRI features for predicting microvascular invasion of hepatocellular carcinoma: a systematic review and meta-analysis[J]. Liver Cancer, 2021, 10(2): 94-106. DOI: 10.1159/000513704.
[12]
Choi JY, Lee JM, Sirlin CB. CT and MR imaging diagnosis and staging of hepatocellular carcinoma: part Ⅱ. Extracellular agents, hepatobiliary agents, and ancillary imaging features[J]. Radiology, 2014, 273(1): 30-50. DOI: 10.1148/radiol.14132362.
[13]
Nakashima Y, Nakashima O, Tanaka M, et al. Portal vein invasion and intrahepatic micrometastasis in small hepatocellular carcinoma by gross type[J]. Hepatol Res, 2003, 26(2): 142-147. DOI: 10.1016/s1386-6346(03)00007-x.
[14]
Shirabe K, Kajiyama K, Abe T, et al. Predictors of microscopic portal vein invasion by hepatocellular carcinoma: measurement of portal perfusion defect area ratio[J]. J Gastroenterol Hepatol, 2009, 24(8): 1431-1436. DOI: 10.1111/j.1440-1746.2009.05847.x.
[15]
Jing MY, Cao YT, Deng J, et al. The value of minimum apparent diffusion coefficient in evaluating the invasiveness of hepatocellular carcinoma[J]. Chin J Magn Reson Imaging, 2021, 12(5): 16-20. DOI: 10.12015/issn.1674-8034.2021.05.004.
[16]
Zhao JK, Li XB, Zhang K, et al. Prediction of microvascular invasion of hepatocellular carcinoma with preoperative diffusion-weighted imaging: a comparison of mean and minimum apparent diffusion coefficient values[J]. Medicine (Baltimore), 2017, 96(33): e7754. DOI: 10.1097/MD.0000000000007754.
[17]
Surov A, Pech M, Omari J, et al. Diffusion-weighted imaging reflects tumor grading and microvascular invasion in hepatocellular carcinoma[J]. Liver Cancer, 2021, 10(1): 10-24. DOI: 10.1159/000511384.
[18]
Yang C, Wang HQ, Tang YB, et al. ADC similarity predicts microvascular invasion of bifocal hepatocellular carcinoma[J]. Abdom Radiol (NY), 2018, 43(9): 2295-2302. DOI: 10.1007/s00261-018-1469-4.
[19]
Iima M, le Bihan D. Clinical intravoxel incoherent motion and diffusion MR imaging: past, present, and future[J]. Radiology, 2016, 278(1): 13-32. DOI: 10.1148/radiol.2015150244.
[20]
Wei Y, Huang ZX, Tang HH, et al. IVIM improves preoperative assessment of microvascular invasion in HCC[J]. Eur Radiol, 2019, 29(10): 5403-5414. DOI: 10.1007/s00330-019-06088-w.
[21]
Zhao W, Liu WG, Liu HP, et al. Preoperative prediction of microvascular invasion of hepatocellular carcinoma with IVIM diffusion-weighted MR imaging and Gd-EOB-DTPA-enhanced MR imaging[J]. PLoS One, 2018, 13(5): e0197488. DOI: 10.1371/journal.pone.0197488.
[22]
Li HX, Zhang J, Zheng ZY, et al. Preoperative histogram analysis of intravoxel incoherent motion (IVIM) for predicting microvascular invasion in patients with single hepatocellular carcinoma[J]. Eur J Radiol, 2018, 105: 65-71. DOI: 10.1016/j.ejrad.2018.05.032.
[23]
Wang WT, Yang L, Yang ZX, et al. Assessment of microvascular invasion of hepatocellular carcinoma with diffusion kurtosis imaging[J]. Radiology, 2018, 286(2): 571-580. DOI: 10.1148/radiol.2017170515.
[24]
Cao LK, Chen J, Duan T, et al. Diffusion kurtosis imaging (DKI) of hepatocellular carcinoma: correlation with microvascular invasion and histologic grade[J]. Quant Imaging Med Surg, 2019, 9(4): 590-602. DOI: 10.21037/qims.2019.02.14.
[25]
Zhang L, Yu X, Wei WX, et al. Prediction of HCC microvascular invasion with gadobenate-enhanced MRI: correlation with pathology[J]. Eur Radiol, 2020, 30(10): 5327-5336. DOI: 10.1007/s00330-020-06895-6.
[26]
Wang XX, Zhang ZQ, Zhou XY, et al. Computational quantitative measures of Gd-EOB-DTPA enhanced MRI hepatobiliary phase images can predict microvascular invasion of small HCC[J]. Eur J Radiol, 2020, 133: 109361. DOI: 10.1016/j.ejrad.2020.109361.
[27]
Jajamovich GH, Huang W, Besa C, et al. DCE-MRI of hepatocellular carcinoma: perfusion quantification with Tofts model versus shutter-speed model—initial experience[J]. Magn Reson Mater Phys Biol Med, 2016, 29(1): 49-58. DOI: 10.1007/s10334-015-0513-4.
[28]
Ahn SY, Lee JM, Joo I, et al. Prediction of microvascular invasion of hepatocellular carcinoma using gadoxetic acid-enhanced MR and (18)F-FDG PET/CT[J]. Abdom Imaging, 2015, 40(4): 843-851. DOI: 10.1007/s00261-014-0256-0.
[29]
Kim KA, Kim MJ, Jeon HM, et al. Prediction of microvascular invasion of hepatocellular carcinoma: usefulness of peritumoral hypointensity seen on gadoxetate disodium-enhanced hepatobiliary phase images[J]. J Magn Reson Imaging, 2012, 35(3): 629-634. DOI: 10.1002/jmri.22876.
[30]
Song Q, Ma J, Rao SX, et al. Study of microcirculation function status of HCC using 3D-DCE-MRI perfusion Tofts model[J]. Radiol Pract, 2013, 28(6): 662-665. DOI: 10.13609/j.cnki.1000-0313.2013.06.012.
[31]
Chen J, Chen CY, Xia CC, et al. Quantitative free-breathing dynamic contrast-enhanced MRI in hepatocellular carcinoma using gadoxetic acid: correlations with Ki67 proliferation status, histological grades, and microvascular density[J]. Abdom Radiol (NY), 2018, 43(6): 1393-1403. DOI: 10.1007/s00261-017-1320-3.
[32]
Kumar V, Gu YH, Basu S, et al. Radiomics: the process and the challenges[J]. Magn Reson Imaging, 2012, 30(9): 1234-1248. DOI: 10.1016/j.mri.2012.06.010.
[33]
Meng XP, Wang YC, Zhou JY, et al. Comparison of MRI and CT for the prediction of microvascular invasion in solitary hepatocellular carcinoma based on a non-radiomics and radiomics method: which imaging modality is better?[J]. J Magn Reson Imaging, 2021, 54(2): 526-536. DOI: 10.1002/jmri.27575.
[34]
Dai HJ, Lu MH, Huang BS, et al. Considerable effects of imaging sequences, feature extraction, feature selection, and classifiers on radiomics-based prediction of microvascular invasion in hepatocellular carcinoma using magnetic resonance imaging[J]. Quant Imaging Med Surg, 2021, 11(5): 1836-1853. DOI: 10.21037/qims-20-218.
[35]
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis[J]. Med Image Anal, 2017, 42: 60-88. DOI: 10.1016/j.media.2017.07.005.
[36]
Wang GY, Jian WW, Cen XP, et al. Prediction of microvascular invasion of hepatocellular carcinoma based on preoperative diffusion-weighted MR using deep learning[J]. Acad Radiol, 2021, 28(Suppl 1): S118-S127. DOI: 10.1016/j.acra.2020.11.014.
[37]
Zhang YX, Lv XF, Qiu JL, et al. Deep learning with 3D convolutional neural network for noninvasive prediction of microvascular invasion in hepatocellular carcinoma[J]. J Magn Reson Imaging, 2021, 54(1): 134-143. DOI: 10.1002/jmri.27538.
[38]
Jhaveri KS, Cleary SP, Fischer S, et al. Blood oxygen level-dependent liver MRI: can it predict microvascular invasion in HCC?[J]. J Magn Reson Imaging, 2013, 37(3): 692-699. DOI: 10.1002/jmri.23858.
[39]
Chen W, DelProposto Z, Liu W, et al. Susceptibility-weighted imaging for the noncontrast evaluation of hepatocellular carcinoma: a prospective study with histopathologic correlation[J]. PLoS One, 2014, 9(5): e98303. DOI: 10.1371/journal.pone.0098303.
[40]
Chang SX, Li GW, Chen Y, et al. Characterizing venous vasculatures of hepatocellular carcinoma using a multi-breath-hold two-dimensional susceptibility weighted imaging[J]. PLoS One, 2013, 8(6): e65895. DOI: 10.1371/journal.pone.0065895.

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