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Opportunities and challenges of liver cancer imaging: Achievements and prospects over the past decade in China
CHEN Yidi  JIANG Hanyu  CHEN Jie  QU Yali  YE Zheng  WEI Yi  WEI Hong  SHENG Liuji  SONG Bin 

Cite this article as: Chen YD, Jiang HY, Chen J, et al. Opportunities and challenges of liver cancer imaging: Achievements and prospects over the past decade in China[J]. Chin J Magn Reson Imaging, 2022, 13(10): 71-78. DOI:10.12015/issn.1674-8034.2022.10.010.


[Abstract] Primary liver cancer is a prevalent and lethal malignancy in China, constituting a major public health problem. Noninvasive imaging techniques play an important role throughout the entire clinical workflow of liver cancer. Remarkable achievements have been made by Chinese scholars in liver cancer imaging research over the past decade. The rapid advancements in artificial intelligence (AI), ultra-high magnetic field and hyperpolarized MRI, spectral CT provide reliable technical support for screening, diagnosis, and treatment decision-making of liver cancer patients. Furthermore, the fusion of multi-omics techniques (radiomics, genomics, and proteomics) further reveals the correlations between key clinical, radiological, pathological, and molecular alterations in liver cancer. This paper summarizes major achievements by Chinese scholars in liver cancer imaging over the last ten years, primarily focusing on MRI-based Liver Imaging Reporting and Data System (LI-RADS) modification, hepatobiliary contrast agent application, advancements in diffusion-MRI, functional MRI and CT technologies, and radiomics and AI. We also reflect on limitations of existing works in this field.In the future, it is necessary to carry out targeted research design according to the characteristics of domestic patients and population features, establish multi-center study cohort covering a wide range of populations and strong representation, and build homogeneous and high-quality national imagedatabaseof liver cancer. Furthermore, we should pay attention to the development and application of new imaging technology, scholars can use AI combined with radiomics, genomics and proteomics to in-depth study the pathological characteristics, gene phenotype and prognosis of liver cancer, deeply participate in the whole process of clinical management of liver cancer patients, provide technical support for precision medicine, and help achieve the strategic goal of national health.
[Keywords] primary liver cancer;hepatocellular carcinoma;genetic characteristics;immunophenotype;molecular subtype;efficacy;prognosis;medical imaging;medical imaging technology;magnetic resonance imaging;computed tomography;radiomics;artificial intelligence;deep learning;precision medicine;review

CHEN Yidi1   JIANG Hanyu1   CHEN Jie1   QU Yali1   YE Zheng1   WEI Yi1   WEI Hong1   SHENG Liuji1   SONG Bin1, 2*  

1 Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China

2 Department of Radiology, Sanya People's Hospital, Sanya 572022, China

Song B, E-mail: cjr.songbin@vip.163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 82101997, 81971571); Science and Technology Plan Project of Sichuan Province (No. 2022YFS0071, 2021YFS0021, 2021YFS0141); 1·3·5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (No. ZYGD22004).
Received  2022-09-09
Accepted  2022-10-14
DOI: 10.12015/issn.1674-8034.2022.10.010
Cite this article as: Chen YD, Jiang HY, Chen J, et al. Opportunities and challenges of liver cancer imaging: Achievements and prospects over the past decade in China[J]. Chin J Magn Reson Imaging, 2022, 13(10): 71-78. DOI:10.12015/issn.1674-8034.2022.10.010.

[1]
Wu C, Ren XD, Zhang QB. Incidence, risk factors, and prognosis in patients with primary hepatocellular carcinoma and lung metastasis: a population-based study[J]. Cancer Manag Res, 2019, 11: 2759-2768. DOI: 10.2147/CMAR.S192896.
[2]
Cao MM, Li H, Sun DQ, et al. Cancer burden of major cancers in China: a need for sustainable actions[J]. Cancer Commun (Lond), 2020, 40(5): 205-210. DOI: 10.1002/cac2.12025.
[3]
Villanueva A. Hepatocellular Carcinoma[J]. The New England journal of medicine, 2019, 380(15): 1450-1462. DOI: 10.1056/NEJMra1713263.
[4]
Jiang HY, Liu XJ, Song B. Progresses in magnetic resonance imaging of hepatocellular carcinoma[J]. Chin J Magn Reson Imaging, 2015, 6(2): 91-97. DOI: 10.3969/j.issn.1674-8034.2015.02.003.
[5]
Chernyak V, Fowler KJ, Kamaya A, et al. Liver imaging reporting and data system (LI-RADS) version 2018: imaging of hepatocellular carcinoma in At-risk patients[J]. Radiology, 2018, 289(3): 816-830. DOI: 10.1148/radiol.2018181494.
[6]
Kim DH, Choi SH, Park SH, et al. Meta-analysis of the accuracy of Liver Imaging Reporting and Data System category 4 or 5 for diagnosing hepatocellular carcinoma[J]. Gut, 2019, 68(9): 1719-1721. DOI: 10.1136/gutjnl-2019-318555.
[7]
Wei Y, Ye Z, Yuan Y, et al. A new diagnostic criterion with gadoxetic acid-enhanced MRI may improve the diagnostic performance for hepatocellular carcinoma[J]. Liver Cancer, 2020, 9(4): 414-425. DOI: 10.1159/000505696.
[8]
Jiang HY, Song B, Qin Y, et al. Modifying LI-RADS on gadoxetate disodium-enhanced MRI: a secondary analysis of a prospective observational study[J]. J Magn Reson Imaging, 2022, 56(2): 399-412. DOI: 10.1002/jmri.28056.
[9]
Jiang HY, Song B, Qin Y, et al. Data-driven modification of the LI-RADS major feature system on gadoxetate disodium-enhanced MRI: toward better sensitivity and simplicity[J]. J Magn Reson Imaging, 2022, 55(2): 493-506. DOI: 10.1002/jmri.27824.
[10]
Jiang J, Wang W, Cui YN, et al. Diagnostic performance of MRI and CT for hepatocellular carcinoma less than 3 cm based on liver reporting and data system version 2018[J]. Chin J Magn Reson Imaging, 2021, 12(9): 25-29, 44. DOI: 10.12015/issn.1674-8034.2021.09.006.
[11]
Wang C, Guo R, Shi DD, et al. Study on the value of liver-specific contrast agent MRI abbreviated sequence in screening small hepatocellular carcinoma in high-risk population[J]. Chin J Magn Reson Imaging, 2021, 12(9): 20-24. DOI: 10.12015/issn.1674-8034.2021.09.005.
[12]
Jiang HY, Wei JW, Fu FF, et al. Predicting microvascular invasion in hepatocellular carcinoma: a dual-institution study on gadoxetate disodium-enhanced MRI[J]. Liver Int, 2022, 42(5): 1158-1172. DOI: 10.1111/liv.15231.
[13]
Feng ZC, Li HL, Zhao HF, et al. Preoperative CT for characterization of aggressive macrotrabecular-massive subtype and vessels that encapsulate tumor clusters pattern in hepatocellular carcinoma[J]. Radiology, 2021, 300(1): 219-229. DOI: 10.1148/radiol.2021203614.
[14]
Chen J, Xia CC, Duan T, et al. Macrotrabecular-massive hepatocellular carcinoma: imaging identification and prediction based on gadoxetic acid-enhanced magnetic resonance imaging[J]. Eur Radiol, 2021, 31(10): 7696-7704. DOI: 10.1007/s00330-021-07898-7.
[15]
Chou YC, Lao IH, Hsieh PL, et al. Gadoxetic acid-enhanced magnetic resonance imaging can predict the pathologic stage of solitary hepatocellular carcinoma[J]. World J Gastroenterol, 2019, 25(21): 2636-2649. DOI: 10.3748/wjg.v25.i21.2636.
[16]
Liu ZW, Yang SM, Chen HX, et al. Correlation between Gd-EOB-DTPA enhanced MRI T1 mapping and Ki-67 expression in hepatocellular carcinoma[J]. Chin J Magn Reson Imaging, 2022, 13(9): 35-40, 52. DOI: 10.12015/issn.1674-8034.2022.09.007.
[17]
Chen J, Wu ZR, Xia CC, et al. Noninvasive prediction of HCC with progenitor phenotype based on gadoxetic acid-enhanced MRI[J]. Eur Radiol, 2020, 30(2): 1232-1242. DOI: 10.1007/s00330-019-06414-2.
[18]
Wei H, Jiang HY, Zheng TY, et al. LI-RADS category 5 hepatocellular carcinoma: preoperative gadoxetic acid-enhanced MRI for early recurrence risk stratification after curative resection[J]. Eur Radiol, 2021, 31(4): 2289-2302. DOI: 10.1007/s00330-020-07303-9.
[19]
Zhao QY, Qi YG, Guo SF, et al. The value of Gd-EOB-DTPA enhanced magnetic resonance imaging for predicting early recurrence of hepatocellular carcinoma after resection[J]. Chin J Magn Reson Imaging, 2021, 12(12): 18-23. DOI: 10.12015/issn.1674-8034.2021.12.004.
[20]
Gore JC, Xu JZ, Colvin DC, et al. Characterization of tissue structure at varying length scales using temporal diffusion spectroscopy[J]. NMR Biomed, 2010, 23(7): 745-756. DOI: 10.1002/nbm.1531.
[21]
Arlinghaus LR, Li X, Rahman AR, et al. On the relationship between the apparent diffusion coefficient and extravascular extracellular volume fraction in human breast cancer[J]. Magn Reson Imaging, 2011, 29(5): 630-638. DOI: 10.1016/j.mri.2011.02.004.
[22]
Wu LM, Xu JR, Lu Q, et al. A pooled analysis of diffusion-weighted imaging in the diagnosis of hepatocellular carcinoma in chronic liver diseases[J]. J Gastroenterol Hepatol, 2013, 28(2): 227-234. DOI: 10.1111/jgh.12054.
[23]
Wu LM, Hu JN, Gu HY, et al. Can diffusion-weighted magnetic resonance imaging (DW-MRI) alone be used as a reliable sequence for the preoperative detection and characterisation of hepatic metastases? A meta-analysis[J]. Eur J Cancer, 2013, 49(3): 572-584. DOI: 10.1016/j.ejca.2012.08.021.
[24]
Jensen JH, Helpern JA, Ramani A, et al. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging[J]. Magn Reson Med, 2005, 53(6): 1432-1440. DOI: 10.1002/mrm.20508.
[25]
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.
[26]
Wei Y, Gao FF, Wang M, et al. Intravoxel incoherent motion diffusion-weighted imaging for assessment of histologic grade of hepatocellular carcinoma: comparison of three methods for positioning region of interest[J]. Eur Radiol, 2019, 29(2): 535-544. DOI: 10.1007/s00330-018-5638-1.
[27]
Jiang XY, Li H, Xie JP, et al. In vivo imaging of cancer cell size and cellularity using temporal diffusion spectroscopy[J]. Magn Reson Med, 2017, 78(1): 156-164. DOI: 10.1002/mrm.26356.
[28]
Jiang XY, Li H, Xie JP, et al. Quantification of cell size using temporal diffusion spectroscopy[J]. Magn Reson Med, 2016, 75(3): 1076-1085. DOI: 10.1002/mrm.25684.
[29]
Jiang XY, Xu JZ, Gore JC. Mapping hepatocyte size in vivo using temporal diffusion spectroscopy MRI[J]. Magn Reson Med, 2020, 84(5): 2671-2683. DOI: 10.1002/mrm.28299.
[30]
Chen BB, Shih TTF. DCE-MRI in hepatocellular carcinoma-clinical and therapeutic image biomarker[J]. World J Gastroenterol, 2014, 20(12): 3125-3134. DOI: 10.3748/wjg.v20.i12.3125.
[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]
Li BS, Xu AH, Huang YR, et al. Oxygen-challenge blood oxygen level-dependent magnetic resonance imaging for evaluation of early change of hepatocellular carcinoma to chemoembolization: a feasibility study[J]. Acad Radiol, 2021, 28(Suppl 1): S13-S19. DOI: 10.1016/j.acra.2020.06.021.
[33]
Gidener T, Ahmed OT, Larson JJ, et al. Liver stiffness by magnetic resonance elastography predicts future cirrhosis, decompensation, and death in NAFLD[J]. Clin Gastroenterol Hepatol, 2021, 19(9): 1915-1924. DOI: 10.1016/j.cgh.2020.09.044.
[34]
Zhang LN, Chen JB, Jiang H, et al. MR elastography as a biomarker for prediction of early and late recurrence in HBV-related hepatocellular carcinoma patients before hepatectomy[J/OL]. Eur J Radiol, 2022, 152 [2022-09-08]. https://linkinghub.elsevier.com/retrieve/pii/S0720-048X(22)00190-5. DOI: 10.1016/j.ejrad.2022.110340.
[35]
Perkons NR, Kiefer RM, Noji MC, et al. Hyperpolarized metabolic imaging detects latent hepatocellular carcinoma domains surviving locoregional therapy[J]. Hepatology, 2020, 72(1): 140-154. DOI: 10.1002/hep.30970.
[36]
Zheng Y, Bin S, Lee PM, et al. Hyperpolarized carbon 13 MRI in liver diseases: recent advances and future opportunities[J]. Liver Int Off J Int Assoc Study Liver, 2022, 42(5): 973-983. DOI: 10.1111/liv.15222.
[37]
Lambin P, Leijenaar RTH, Deist TM, 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.
[38]
Yang HR, Zhang JT, Ma MM, et al. The study of enhanced MR radiomics combining clinical factors in predicting early recurrence of hepatocellular carcinoma after resection[J]. Chin J Magn Reson Imaging, 2022, 13(4): 49-55. DOI: 10.12015/issn.1674-8034.2022.04.009.
[39]
Chen YD, Zhang L, Zhou ZP, et al. Radiomics and nomogram of magnetic resonance imaging for preoperative prediction of microvascular invasion in small hepatocellular carcinoma[J]. World J Gastroenterol, 2022, 28(31): 4399-4416. DOI: 10.3748/wjg.v28.i31.4399.
[40]
Wu MH, Tan HN, 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.
[41]
Huang XL, Long LL, Wei JQ, et al. Radiomics for diagnosis of dual-phenotype hepatocellular carcinoma using Gd-EOB-DTPA-enhanced MRI and patient prognosis[J]. J Cancer Res Clin Oncol, 2019, 145(12): 2995-3003. DOI: 10.1007/s00432-019-03062-3.
[42]
Wang WT, Gu DS, Wei JW, 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.
[43]
Liao HT, Jiang HY, Chen YT, et al. Predicting genomic alterations of phosphatidylinositol-3 kinase signaling in hepatocellular carcinoma: a radiogenomics study based on next-generation sequencing and contrast-enhanced CT[J/OL]. Ann Surg Oncol, 2022, 29(7) [2022-09-08]. https://link.springer.com/article/10.1245/s10434-022-11505-4. DOI: 10.1245/s10434-022-11505-4.
[44]
Calderaro J, Seraphin TP, 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.
[45]
Zhang F, Yang JL, Nezami N, et al. Liver tissue classification using an auto-context-based deep neural network with a multi-phase training framework[J]. Patch Based Tech Med Imaging (2018), 2018, 11075: 59-66. DOI: 10.1007/978-3-030-00500-9_7.
[46]
Zheng RC, Wang QD, Lv SZ, 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.
[47]
Zhen SH, Cheng M, Tao YB, et al. Deep Learning for Accurate Diagnosis of Liver Tumor Based on Magnetic Resonance Imaging and Clinical Data[J/OL]. Frontiers in oncology, 2020 [2022-09-08]. https://www.frontiersin.org/articles/10.3389/fonc.2020.00680/full. DOI: 10.3389/fonc.2020.00680.
[48]
Wei JW, Jiang HY, Zeng MS, et al. Prediction of microvascular invasion in hepatocellular carcinoma via deep learning: a multi-center and prospective validation study[J/OL]. Cancers (Basel), 2021, 13(10) [2022-09-08]. https://www.mdpi.-com/resolver?pii=cancers13102368. DOI: 10.3390/cancers13102368.
[49]
Gao WY, Wang WT, Song DJ, et al. A predictive model integrating deep and radiomics features based on gadobenate dimeglumine-enhanced MRI for postoperative early recurrence of hepatocellular carcinoma[J]. Radiol Med, 2022, 127(3): 259-271. DOI: 10.1007/s11547-021-01445-6.
[50]
National Health Commission of the People's Republic of China. Guidelines for Diagnosis and treatment of primary liver cancer (2022 version)[J]. Chin J Hepatol, 2022(4): 367-388. DOI: 10.3760/cma.j.cn501113-20220413-00193.
[51]
Xiang GS, Jiang ZY, He S, et al. Diagnosis and treatment of liver cancer with energy spectrum CT[J]. Radiol Pract, 2020, 35(6): 810-812. DOI: 10.13609/j.cnki.1000-0313.2020.06.022.
[52]
Wu QY, Liu J, Yang CS, et al. Application value of imaging examinations in the diagnosis of small hepatocellular carcinoma[J]. Chin J Dig Surg, 2022(4): 543-550. DOI: 10.3760/cma.j.cn115610-20220321-00146.
[53]
Yu YX, Lin XZ, Chen KM, et al. Hepatocellular carcinoma and focal nodular hyperplasia of the liver: differentiation with CT spectral imaging[J]. Eur Radiol, 2013, 23(6): 1660-1668. DOI: 10.1007/s00330-012-2747-0.
[54]
Chai B, Xiang DQ, Wang W, et al. Arterial enhancement fraction in evaluating the therapeutic effect and survival for hepatocellular carcinoma patients treated with DEB-TACE[J/OL]. Cancer Imaging, 2022, 22(1) [2022-09-08]. https://cancerimagingjournal.biomedcentral.com/articles/10.1186/s40644-022-00477-z. DOI: 10.1186/s40644-022-00477-z.
[55]
Wang J, Shen JL. Spectral CT in evaluating the therapeutic effect of transarterial chemoembolization for hepatocellular carcinoma: a retrospective study[J/OL]. Medicine, 2017, 96(52) [2022-09-08]. https://doi.org/10.1097/MD.0000000000009236. DOI: 10.1097/MD.0000000000009236.
[56]
Rao SX, Hu DY, Huan Y, et al. Expert consensus on clinical application of hepatobiliary-specific MRI contrast agent Gd-EOB-DTPA[J]. J Clin Hepatol, 2016, 32(12): 2236-2241. DOI: 10.3969/j.issn.1001-5256.2016.12.003.
[57]
Rao SX, Wang J, Wang J, et al. Chinese consensus on the clinical application of hepatobiliary magnetic resonance imaging contrast agent: Gadoxetic acid disodium[J]. J Dig Dis, 2019, 20(2): 54-61. DOI: 10.1111/1751-2980.12707.
[58]
Zech CJ, Ba-Ssalamah A, Berg T, et al. Consensus report from the 8th international forum for liver magnetic resonance imaging[J]. Eur Radiol, 2020, 30(1): 370-382. DOI: 10.1007/s00330-019-06369-4.

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