Advances in MRI application of artificial intelligence in hepatocellular carcinoma

Share this content in WeChat

• Review •

GAO Zihan LUO Yu WU Yaping BAI Yan WANG Meiyun

GAO Z H, LUO Y, WU Y P, et al. Advances in MRI application of artificial intelligence in hepatocellular carcinoma[J]. Chin J Magn Reson Imaging, 2023, 14(8): 154-157, 196. DOI:10.12015/issn.1674-8034.2023.08.027.

Abstract

[Abstract] Hepatocellular carcinoma (HCC) is currently the third leading cause of cancer death worldwide, which poses a major threat to human health. Early diagnosis and prognosis prediction of HCC have become the current research hotspots. In recent years, with the development of computer technology, artificial intelligence has shown great potential in the accurate diagnosis, efficacy evaluation and risk prediction of hepatocellular carcinoma. This article will summarize the MRI image segmentation, auxiliary diagnosis, prognosis prediction, pathological grading and molecular characteristics of HCC, so as to provide new ideas and methods for scientific research and promote the development of clinical diagnosis and treatment towards precision and individualization.

[Keywords] hepatocellular carcinoma；artificial intelligence；magnetic resonance imaging；deep learning；radiomics；image segmentation

Contributor Information

GAO Zihan^{1,
2}   LUO Yu^{1,
2}   WU Yaping²   BAI Yan²   WANG Meiyun^{1,
2*}

¹ Department of Medical Imaging, Zhengzhou University People's Hospital, Zhengzhou 450003, China

² Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou 450003, China

Corresponding author: Wang MY, E-mail: mywang@zzu.edu.cn

Conflicts of interest None.

Publication Information

ACKNOWLEDGMENTS Medical Science and Technology Research Project of Henan Province (No. SBGJ202101002).

Received 2022-12-12

Accepted 2023-06-15

DOI: 10.12015/issn.1674-8034.2023.08.027

Text

References

[1]

SUNG H, FERLAY J, SIEGEL R L, 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]

VILLANUEVA A. Hepatocellular carcinoma[J]. N Engl J Med, 2019, 380(15): 1450-1462. DOI: 10.1056/NEJMra1713263.

[3]

FENG B, MA X H, WANG S, et al. Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: current status and future perspectives[J]. World J Gastroenterol, 2021, 27(32): 5341-5350. DOI: 10.3748/wjg.v27.i32.5341.

[4]

HAMER O W, SCHLOTTMANN K, SIRLIN C B, et al. Technology insight: advances in liver imaging[J]. Nat Clin Pract Gastroenterol Hepatol, 2007, 4(4): 215-228. DOI: 10.1038/ncpgasthep0766.

[5]

AMISHA, MALIK P, PATHANIA M, et al. Overview of artificial intelligence in medicine[J]. J Family Med Prim Care, 2019, 8(7): 2328-2331. DOI: 10.4103/jfmpc.jfmpc_440_19.

[6]

BERA K, BRAMAN N, GUPTA A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology[J]. Nat Rev Clin Oncol, 2022, 19(2): 132-146. DOI: 10.1038/s41571-021-00560-7.

[7]

RAJPURKAR P, CHEN E, BANERJEE O, et al. AI in health and medicine[J]. Nat Med, 2022, 28(1): 31-38. DOI: 10.1038/s41591-021-01614-0.

[8]

CALDERARO J, SERAPHIN T P, LUEDDE T, et al. Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma[J]. J Hepatol, 2022, 76(6): 1348-1361. DOI: 10.1016/j.jhep.2022.01.014.

[9]

HUANG S G, YANG J, FONG S, et al. Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges[J]. Cancer Lett, 2020, 471: 61-71. DOI: 10.1016/j.canlet.2019.12.007.

[10]

CHARTRAND G, CHENG P M, VORONTSOV E, et al. Deep learning: a primer for radiologists[J]. Radiographics, 2017, 37(7): 2113-2131. DOI: 10.1148/rg.2017170077.

[11]

JIANG B B, LI N Y, SHI X M, et al. Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT[J]. Radiology, 2022, 303(1): 202-212. DOI: 10.1148/radiol.210551.

[12]

JIANG B B, GUO N, GE Y H, et al. Development and application of artificial intelligence in cardiac imaging[J/OL]. Br J Radiol, 2020, 93(1113): 20190812 [2023-01-15]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465846. DOI: 10.1259/bjr.20190812.

[13]

GILLIES R J, KINAHAN P E, HRICAK H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169.

[14]

LAMBIN P, LEIJENAAR R T H, DEIST T M, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762. DOI: 10.1038/nrclinonc.2017.141.

[15]

RAMAN A G, JONES C, WEISS C R. Machine learning for hepatocellular carcinoma segmentation at MRI: Radiology in training[J]. Radiology, 2022, 304(3): 509-515. DOI: 10.1148/radiol.212386.

[16]

BOUSABARAH K, LETZEN B, TEFERA J, et al. Automated detection and delineation of hepatocellular carcinoma on Multiphasic contrast-enhanced MRI using deep learning[J]. Abdom Radiol (NY), 2021, 46(1): 216-225. DOI: 10.1007/s00261-020-02604-5.

[17]

HÄNSCH A, CHLEBUS G, MEINE H, et al. Improving automatic liver tumor segmentation in late-phase MRI using multi-model training and 3D convolutional neural networks[J/OL]. Sci Rep, 2022, 12(1): 12262 [2023-01-15]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9293996/. DOI: 10.1038/s41598-022-16388-9.

[18]

ZHENG R C, WANG Q D, LV S Z, et al. Automatic liver tumor segmentation on dynamic contrast enhanced MRI using 4D information: deep learning model based on 3D convolution and convolutional LSTM[J]. IEEE Trans Med Imaging, 2022, 41(10): 2965-2976. DOI: 10.1109/TMI.2022.3175461.

[19]

ZHAO J F, LI D W, XIAO X J, et al. United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI[J/OL]. Med Image Anal, 2021, 73: 102154 [2023-01-15]. https://doi.org/10.1016/j.media.2021.102154. DOI: 10.1016/j.media.2021.102154.

[20]

WU Y C, GE Z Y, ZHANG D H, et al. Mutual consistency learning for semi-supervised medical image segmentation[J/OL]. Med Image Anal, 2022, 81: 102530 [2023-01-15]. https://www.sciencedirect.com/science/article/abs/pii/S1361841522001773?via%3Dihub. DOI: 10.1016/j.media.2022.102530.

[21]

KARIMI D, WARFIELD S K, GHOLIPOUR A. Transfer learning in medical image segmentation: new insights from analysis of the dynamics of model parameters and learned representations[J/OL]. Artif Intell Med, 2021, 116: 102078 [2023-01-15]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8164174/. DOI: 10.1016/j.artmed.2021.102078.

[22]

HAMM C A, WANG C J, SAVIC L J, et al. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI[J]. Eur Radiol, 2019, 29(7): 3338-3347. DOI: 10.1007/s00330-019-06205-9.

[23]

ZHEN S H, CHENG M, TAO Y B, et al. Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data[J/OL]. Front Oncol, 2020, 10: 680 [2023-01-15]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7271965/. DOI: 10.3389/fonc.2020.00680.

[24]

OESTMANN P M, WANG C J, SAVIC L J, et al. Deep learning-assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver[J]. Eur Radiol, 2021, 31(7: 4981-4990. DOI: 10.1007/s00330-020-07559-1.

[25]

XIAO X J, ZHAO J F, LI S. Task relevance driven adversarial learning for simultaneous detection, size grading, and quantification of hepatocellular carcinoma via integrating multi-modality MRI[J/OL]. Med Image Anal, 2022, 81: 102554 [2023-01-15]. https://doi.org/10.1016/j.media.2022.102554. DOI: 10.1016/j.media.2022.102554.

[26]

HONG S B, CHOI S H, KIM S Y, 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.

[27]

ERSTAD D J, TANABE K K. Prognostic and therapeutic implications of microvascular invasion in hepatocellular carcinoma[J]. Ann Surg Oncol, 2019, 26(5: 1474-1493. DOI: 10.1245/s10434-019-07227-9.

[28]

WANG L Y, ZHANG L, JIANG B B, et al. Clinical application of deep learning and radiomics in hepatic disease imaging: a systematic scoping review[J/OL]. Br J Radiol, 2022, 95(1136: 20211136 [2023-01-15]. https://doi.org/10.1259/bjr.20211136. DOI: 10.1259/bjr.20211136.

[29]

SONG D J, WANG Y Y, WANG W T, et al. Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters[J]. J Cancer Res Clin Oncol, 2021, 147(12: 3757-3767. DOI: 10.1007/s00432-021-03617-3.

[30]

ZHANG Y X, LV X F, QIU J L, 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.

[31]

FENG S T, JIA Y M, LIAO B, et al. Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI[J]. Eur Radiol, 2019, 29(9: 4648-4659. DOI: 10.1007/s00330-018-5935-8.

[32]

ZHOU W, JIAN W W, CEN X P, et al. Prediction of microvascular invasion of hepatocellular carcinoma based on contrast-enhanced MR and 3D convolutional neural networks[J/OL]. Front Oncol, 2021, 11: 588010 [2023-01-15]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8040801/. DOI: 10.3389/fonc.2021.588010.

[33]

WANG G Y, JIAN W W, CEN X P, 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.

[34]

LLOVET J M, DE BAERE T, KULIK L, et al. Locoregional therapies in the era of molecular and immune treatments for hepatocellular carcinoma[J]. Nat Rev Gastroenterol Hepatol, 2021, 18(5: 293-313. DOI: 10.1038/s41575-020-00395-0.

[35]

VOIZARD N, CERNY M, ASSAD A, et al. Assessment of hepatocellular carcinoma treatment response with LI-RADS: a pictorial review[J/OL]. Insights Imaging, 2019, 10(1: 121 [2023-01-15]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6920285/. DOI: 10.1186/s13244-019-0801-z.

[36]

ABAJIAN A, MURALI N, SAVIC L J, et al. Predicting treatment response to intra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning—an artificial intelligence concept[J/OL]. J Vasc Interv Radiol, 2018, 29(6: 850-857.e1 [2023-01-15]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5970021/. DOI: 10.1016/j.jvir.2018.01.769.

[37]

SVECIC A, MANSOUR R, TANG A, et al. Prediction of post transarterial chemoembolization MR images of hepatocellular carcinoma using spatio-temporal graph convolutional networks[J/OL]. PLoS One, 2021, 16(12: e0259692 [2023-01-15]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8651128/. DOI: 10.1371/journal.pone.0259692.

[38]

MÄHRINGER-KUNZ A, WAGNER F, HAHN F, et al. Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network: a Pilot Study[J]. Liver Int, 2020, 40(3: 694-703. DOI: 10.1111/liv.14380.

[39]

MUHAMMAD H, TEHREEM A, TING P S, et al. Hepatocellular carcinoma and the role of liver transplantation: a review[J]. J Clin Transl Hepatol, 2021, 9(5: 738-748. DOI: 10.14218/JCTH.2021.00125.

[40]

MAZZAFERRO V, LLOVET J M, MICELI R, et al. Predicting survival after liver transplantation in patients with hepatocellular carcinoma beyond the Milan criteria: a retrospective, exploratory analysis[J]. Lancet Oncol, 2009, 10(1: 35-43. DOI: 10.1016/S1470-2045(08)70284-5.

[41]

KIM W R, LAKE J R, SMITH J M, et al. OPTN/SRTR 2017 annual data report: liver[J]. Am J Transplant, 2019, 19(Suppl 2): 184-283. DOI: 10.1111/ajt.15276.

[42]

MAZZAFERRO V, REGALIA E, DOCI R, et al. Liver transplantation for the treatment of small hepatocellular carcinomas in patients with cirrhosis[J]. N Engl J Med, 1996, 334(11: 693-699. DOI: 10.1056/NEJM199603143341104.

[43]

WIESNER R, EDWARDS E, FREEMAN R, et al. Model for end-stage liver disease (MELD) and allocation of donor livers[J]. Gastroenterology, 2003, 124(1: 91-96. DOI: 10.1053/gast.2003.50016.

[44]

HE T C, FONG J N, MOORE L W, et al. An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer[J/OL]. Comput Med Imaging Graph, 2021, 89: 101894 [2023-01-15]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8054468. DOI: 10.1016/j.compmedimag.2021.101894.

[45]

GONG X Q, TAO Y Y, WU Y K, et al. Progress of MRI radiomics in hepatocellular carcinoma[J]. Front Oncol, 2021, 11: 698373 [2023-01-15]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8488263. DOI: 10.3389/fonc.2021.698373.

[46]

WU M H, TAN H N, GAO F, et al. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature[J]. Eur Radiol, 2019, 29(6: 2802-2811. DOI: 10.1007/s00330-018-5787-2.

[47]

MAO Y F, WANG J C, ZHU Y, et al. Gd-EOB-DTPA-enhanced MRI radiomic features for predicting histological grade of hepatocellular carcinoma[J]. Hepatobiliary Surg Nutr, 2022, 11(1: 13-24. DOI: 10.21037/hbsn-19-870.

[48]

WANG W T, GU D S, WEI J W, et al. A radiomics-based biomarker for cytokeratin 19 status of hepatocellular carcinoma with gadoxetic acid-enhanced MRI[J]. Eur Radiol, 2020, 30(5: 3004-3014. DOI: 10.1007/s00330-019-06585-y.

[49]

ZHUO J Y, LU D, TAN W Y, et al. CK19-positive hepatocellular carcinoma is a characteristic subtype[J]. J Cancer, 2020, 11(17: 5069-5077. DOI: 10.7150/jca.44697.

[50]

YANG F, WAN Y D, XU L, et al. MRI-radiomics prediction for cytokeratin 19-positive hepatocellular carcinoma: a multicenter study[J/OL]. Front Oncol, 2021, 11: 672126 [2023-01-15]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8406635. DOI: 10.3389/fonc.2021.672126.

[51]

CHEN Y Y, CHEN J, ZHANG Y, et al. Preoperative prediction of cytokeratin 19 expression for hepatocellular carcinoma with deep learning radiomics based on gadoxetic acid-enhanced magnetic resonance imaging[J/OL]. J Hepatocell Carcinoma, 2021, 8: 795-808 [2023-01-15]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8314931. DOI: 10.2147/JHC.S313879.

PREV Research progress of magnetic resonance imaging in diagnosis of thyroid nodules

NEXT Application progress of diffusion-weighted imaging basded on mono-exponential and intravoxel incoherent motion imaging in autoimmune diseases

LINK

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