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Review | Open Access

Application of biological big data and radiomics in hepatocellular carcinoma

Guoxu Fanga,b,1Jianhui Fanc,1Zongren Dinga,b,1Yongyi Zenga( )
Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China
The Big Data Institute of Southeast Hepatobiliary Health Information, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China
Department of Hepatology for Pregnancy, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China

1 These authors contributed equally to this work.

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Abstract

Hepatocellular carcinoma (HCC), one of the most common gastrointestinal cancers, has been considered a worldwide threat due to its high incidence and poor prognosis. In recent years, with the continuous emergence and promotion of new sequencing technologies in omics, genomics, transcriptomics, proteomics, and liquid biopsy are used to assess HCC heterogeneity from different perspectives and become a hotspot in the field of tumor precision medicine. In addition, with the continuous improvement of machine learning algorithms and deep learning algorithms, radiomics has made great progress in the field of ultrasound, CT and MRI for HCC. This article mainly reviews the research progress of biological big data and radiomics in HCC, and it provides new methods and ideas for the diagnosis, prognosis, and therapy of HCC.

References

[1]

Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68: 394–424.

[2]

Suresh A, Dhanasekaran R. Implications of genetic heterogeneity in hepatocellular cancer. Adv Cancer Res 2022;156: 103–35.

[3]

Sarveazad A, Agah S, Babahajian A, et al. Predictors of 5 year survival rate in hepatocellular carcinoma patients. J Res Med Sci 2019;24: 86.

[4]

Osho A, Rich NE, Singal AG. Role of imaging in management of hepatocellular carcinoma: surveillance, diagnosis, and treatment response. Hepatoma Res 2020;6.

[5]

Ludwig DR, Fraum TJ, Cannella R, et al. Expanding the Liver Imaging Reporting and Data System (LI-RADS) v2018 diagnostic population: performance and reliability of LI-RADS for distinguishing hepatocellular carcinoma (HCC) from non-HCC primary liver carcinoma in patients who do not meet strict LI-RADS high-risk criteria. HPB (Oxford) 2019;21: 1697–706.

[6]

Nakamura I, Hatano E, Tada M, et al. Enhanced patterns on intraoperative contrast-enhanced ultrasonography predict outcomes after curative liver resection in patients with hepatocellular carcinoma. Surg Today 2021;51: 764–76.

[7]

Avanzo M, Wei L, Stancanello J, et al. Machine and deep learning methods for radiomics. Med Phys 2020;47: e185–202.

[8]

Harding-Theobald E, Louissaint J, Maraj B, et al. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma. Aliment Pharmacol Ther 2021;54: 890–901.

[9]

Ahn SM, Jang SJ, Shim JH, et al. Genomic portrait of resectable hepatocellular carcinomas: implications of RB1 and FGF19 aberrations for patient stratification. Hepatology 2014;60: 1972–82.

[10]

Cancer Genome Atlas Research Network wbe. Electronic address, N. Cancer genome Atlas research, comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell 2017;169: 1327–1341 e1323.

[11]

Fujimoto A, Totoki Y, Abe T, et al. Whole-genome sequencing of liver cancers identifies etiological influences on mutation patterns and recurrent mutations in chromatin regulators. Nat Genet 2012;44: 760–4.

[12]

Guichard C, Amaddeo G, Imbeaud S, et al. Integrated analysis of somatic mutations and focal copy-number changes identifies key genes and pathways in hepatocellular carcinoma. Nat Genet 2012;44: 694–8.

[13]

Nault JC, Mallet M, Pilati C, et al. High frequency of telomerase reversetranscriptase promoter somatic mutations in hepatocellular carcinoma and preneoplastic lesions. Nat Commun 2013;4: 2218.

[14]

Schulze K, Imbeaud S, Letouze E, et al. Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nat Genet 2015;47: 505–11.

[15]

Totoki Y, Tatsuno K, Covington KR, et al. Trans-ancestry mutational landscape of hepatocellular carcinoma genomes. Nat Genet 2014;46: 1267–73.

[16]

Nault JC, Calderaro J, Di Tommaso L, et al. Telomerase reverse transcriptase promoter mutation is an early somatic genetic alteration in the transformation of premalignant nodules in hepatocellular carcinoma on cirrhosis. Hepatology 2014; 60: 1983–92.

[17]

Oren M. Decision making by p53: life, death and cancer. Cell Death Differ 2003; 10: 431–42.

[18]

Vousden KH, Lu X. Live or let die: the cell's response to p53. Nat Rev Cancer 2002; 2: 594–604.

[19]

Villar S, Ortiz-Cuaran S, Abedi-Ardekani B, et al. Aflatoxin-induced TP53 R249S mutation in hepatocellular carcinoma in Thailand: association with tumors developing in the absence of liver cirrhosis. PLoS One 2012;7: e37707.

[20]

Gouas DA, Villar S, Ortiz-Cuaran S, et al. TP53 R249S mutation, genetic variations in HBX and risk of hepatocellular carcinoma in the Gambia. Carcinogenesis 2012; 33: 1219–24.

[21]

Wang H, Chen L, Zhou T, et al. p53 mutation at serine 249 and its gain of function are highly related to hepatocellular carcinoma after smoking exposure. Public Health Genomics 2021;24: 171–81.

[22]

Lu LC, Shao YY, Lee YH, et al. beta-catenin (CTNNB1) mutations are not associated with prognosis in advanced hepatocellular carcinoma. Oncology 2014; 87: 159–66.

[23]

Wang Z, Sheng YY, Gao XM, et al. beta-catenin mutation is correlated with a favorable prognosis in patients with hepatocellular carcinoma. Mol Clin Oncol 2015;3: 936–40.

[24]

Ding X, Yang Y, Han B, et al. Transcriptomic characterization of hepatocellular carcinoma with CTNNB1 mutation. PLoS One 2014;9: e95307.

[25]

Harding JJ, Nandakumar S, Armenia J, et al. Prospective genotyping of hepatocellular carcinoma: clinical implications of next-generation sequencing for matching patients to targeted and immune therapies. Clin Cancer Res 2019;25: 2116–26.

[26]

Ismail Labgaa AV. Liquid biopsy in liver cancer. Discov Med 2015;105: 263–73.

[27]

Swanton C. Intratumor heterogeneity: evolution through space and time. Cancer Res 2012;72: 4875–82.

[28]

Zhang Y, Liu Z, Ji K, et al. Clinical application value of circulating cell-free DNA in hepatocellular carcinoma. Front Mol Biosci 2021;8: 736330.

[29]

Cai ZX, Chen G, Zeng YY, et al. Circulating tumor DNA profiling reveals clonal evolution and real-time disease progression in advanced hepatocellular carcinoma. Int J Cancer 2017;141: 977–85.

[30]

Cox DR. Regression models and life-tables. J Roy Stat Soc Ser B (Methodological) 1972;34: 187–220.

[31]

Galon J, Bruni D. Tumor immunology and tumor evolution: intertwined histories. Immunity 2020;52: 55–81.

[32]

Melero I, Berman DM, Aznar MA, et al. Evolving synergistic combinations of targeted immunotherapies to combat cancer. Nat Rev Cancer 2015;15: 457–72.

[33]

Huang R, Chen Z, Li W, et al. Immune system-associated genes increase malignant progression and can be used to predict clinical outcome in patients with hepatocellular carcinoma. Int J Oncol 2020;56: 1199–211.

[34]

Li Y, He X, Zhang X, et al. Immune-related microRNA signature for predicting prognosis and the immune microenvironment in hepatocellular carcinoma. Life Sci 2021;265: 118799.

[35]

Zhou P, Lu Y, Zhang Y, et al. Construction of an immune-related six-lncRNA signature to predict the outcomes, immune cell infiltration, and immunotherapy response in patients with hepatocellular carcinoma. Front Oncol 2021;11: 661758.

[36]

Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011; 144: 646–74.

[37]

Feng J, Li J, Wu L, et al. Emerging roles and the regulation of aerobic glycolysis in hepatocellular carcinoma. J Exp Clin Cancer Res 2020;39: 126.

[38]

Chen Z, Zou Y, Zhang Y, et al. A novel prognostic signature based on four glycolysis-related genes predicts survival and clinical risk of hepatocellular carcinoma. J Clin Lab Anal 2021: e24005.

[39]

Bai Y, Lin H, Chen J, et al. Identification of prognostic glycolysis-related lncRNA signature in tumor immune microenvironment of hepatocellular carcinoma. Front Mol Biosci 2021;8: 645084.

[40]

Sangineto M, Villani R, Cavallone F, et al. Lipid metabolism in development and progression of hepatocellular carcinoma. Cancers (Basel) 2020;12.

[41]

Wang W, Zhang C, Yu Q, et al. Development of a novel lipid metabolism-based risk score model in hepatocellular carcinoma patients. BMC Gastroenterol 2021;21: 68.

[42]

Lee DY, Kim EH. Therapeutic effects of amino acids in liver diseases: current studies and future perspectives. J Cancer Prev 2019;24: 72–8.

[43]

Tsun ZY, Possemato R. Amino acid management in cancer. Semin Cell Dev Biol 2015;43: 22–32.

[44]

Lukey MJ, Katt WP, Cerione RA. Targeting amino acid metabolism for cancer therapy. Drug Discov Today 2017;22: 796–804.

[45]

Dejong CHC, van de Poll MCG, Soeters PB, et al. Aromatic amino acid metabolism during liver failure. J Nutr 2007;137: 1579S–85S.

[46]

Zhao Y, Zhang J, Wang S, et al. Identification and validation of a nine-gene amino acid metabolism-related risk signature in HCC. Front Cell Dev Biol 2021;9: 731790.

[47]

Galluzzi L, Vitale I, Aaronson SA, et al. Molecular mechanisms of cell death: recommendations of the nomenclature committee on cell death. Cell Death Differ 2018;25(2018): 486–541.

[48]

Wu J, Wang Y, Jiang R, et al. Ferroptosis in liver disease: new insights into disease mechanisms. Cell Death Dis 2021;7: 276.

[49]

Liang JY, Wang DS, Lin HC, et al. A novel ferroptosis-related gene signature for overall survival prediction in patients with hepatocellular carcinoma. Int J Biol Sci 2020;16: 2430–41.

[50]

Xiong Y, Ouyang Y, Fang K, et al. Prediction of prognosis and molecular mechanism of ferroptosis in hepatocellular carcinoma based on bioinformatics methods. Comput Math Methods Med 2022;2022: 4558782.

[51]

Shi J, Zhao Y, Wang K, et al. Cleavage of GSDMD by inflammatory caspases determines pyroptotic cell death. Nature 2015;526: 660–5.

[52]

Kayagaki N, Stowe IB, Lee BL, et al. Caspase-11 cleaves gasdermin D for noncanonical inflammasome signalling. Nature 2015;526: 666–71.

[53]

Julien O, Wells JA. Caspases and their substrates. Cell Death Differ 2017;24: 1380–9.

[54]

Crawford ED, Wells JA. Caspase substrates and cellular remodeling. Annu Rev Biochem 2011;80: 1055–87.

[55]

Zhang Z, Xia F, Xu Z, et al. Identification and validation of a novel pyroptosis-related lncRNAs signature associated with prognosis and immune regulation of hepatocellular carcinoma. Sci Rep 2022;12: 8886.

[56]

Wu ZH, Li ZW, Yang DL, et al. Development and validation of a pyroptosis-related long non-coding RNA signature for hepatocellular carcinoma. Front Cell Dev Biol 2021;9: 713925.

[57]

Wang Y, Kanneganti TD. From pyroptosis, apoptosis and necroptosis to PANoptosis: a mechanistic compendium of programmed cell death pathways. Comput Struct Biotechnol J 2021;19: 4641–57.

[58]

Scarpitta A, Hacker UT, Buning H, et al. Pyroptotic and necroptotic cell death in the tumor microenvironment and their potential to stimulate anti-tumor immune responses. Front Oncol 2021;11: 731598.

[59]

Kaczmarek A, Vandenabeele P, Krysko DV. Necroptosis: the release of damageassociated molecular patterns and its physiological relevance. Immunity 2013;38: 209–23.

[60]

Liu L, Li H, Hu D, et al. Insights into N6-methyladenosine and programmed cell death in cancer. Mol Cancer 2022;21: 32.

[61]

Meng T, Wang Q, Yang Y, et al. Construction of a necroptosis-related miRNA signature for predicting the prognosis of patients with hepatocellular carcinoma. Front Genet 2022;13: 825261.

[62]

Chen C, Wu Y, Chen K, et al. Identification and validation of necroptosis-related LncRNA signature in hepatocellular carcinoma for prognosis estimation and microenvironment status. Front Genet 2022;13: 898507.

[63]

Levy JMM, Towers CG, Thorburn A. Targeting autophagy in cancer. Nat Rev Cancer 2017;17: 528–42.

[64]

Amaravadi RK, Kimmelman AC, Debnath J. Targeting autophagy in cancer: recent advances and future directions. Cancer Discov 2019;9: 1167–81.

[65]

Huo X, Qi J, Huang K, et al. Identification of an autophagy-related gene signature that can improve prognosis of hepatocellular carcinoma patients. BMC Cancer 2020;20: 771.

[66]

Wanli Yang LN, Zhao Xinhui, Duan Lili, et al. Development and validation of a survival model based on autophagy-associated genes for predicting prognosis of hepatocellular carcinoma. Am J Transl Res 2020;12: 6705–22.

[67]

Yang S, Zhou Y, Zhang X, et al. The prognostic value of an autophagy-related lncRNA signature in hepatocellular carcinoma. BMC Bioinf 2021;22: 217.

[68]

Nieto MA, Huang RY, Jackson RA, et al. Emt: 2016. Cell 2016;166(1): 21–45.

[69]

Brabletz T. To differentiate or not–routes towards metastasis. Nat Rev Cancer 2012;12: 425–36.

[70]

De Craene B, Berx G. Regulatory networks defining EMT during cancer initiation and progression. Nat Rev Cancer 2013;13: 97–110.

[71]

Huang S, Li D, Zhuang L, et al. Identification of an epithelial-mesenchymal transition-related long non-coding RNA prognostic signature to determine the prognosis and drug treatment of hepatocellular carcinoma patients. Front Med (Lausanne) 2022;9: 850343.

[72]

Xu BH, Jiang JH, Luo T, et al. Signature of prognostic epithelial-mesenchymal transition related long noncoding RNAs (ERLs) in hepatocellular carcinoma. Medicine (Baltimore) 2021;100: e26762.

[73]

Jing X, Yang F, Shao C, et al. Role of hypoxia in cancer therapy by regulating the tumor microenvironment. Mol Cancer 2019;18: 157.

[74]

Zhou C, Zhang H, Lu L. Identification and validation of hypoxia-related lncRNA signature as a prognostic model for hepatocellular carcinoma. Front Genet 2021; 12: 744113.

[75]

Tang P, Qu W, Wang T, et al. Identifying a hypoxia-related long non-coding RNAs signature to improve the prediction of prognosis and immunotherapy response in hepatocellular carcinoma. Front Genet 2021;12: 785185.

[76]

Anderson NL, Anderson NG. Proteome and proteomics: new technologies, new concepts, and new words. Electrophoresis 1998;19: 1853–61.

[77]

Xing X, Liang D, Huang Y, et al. The application of proteomics in different aspects of hepatocellular carcinoma research. J Proteonomics 2016;145: 70–80.

[78]

Bergstrand CG, Czar B. Demonstration of a new protein fraction in serum from the human fetus. Scand J Clin Lab Invest 1956;8: 174.

[79]

Hanif H, Ali MJ, Susheela AT, et al. Update on the applications and limitations of alpha-fetoprotein for hepatocellular carcinoma. World J Gastroenterol 2022;28: 216–29.

[80]

Marrero JA, Feng Z, Wang Y, et al. Alpha-fetoprotein, des-gamma carboxyprothrombin, and lectin-bound alpha-fetoprotein in early hepatocellular carcinoma. Gastroenterology 2009;137: 110–8.

[81]

Cheng J, Wang W, Zhang Y, et al. Prognostic role of pre-treatment serum AFP-L3% in hepatocellular carcinoma: systematic review and meta-analysis. PLoS One 2014;9: e87011.

[82]

Inagaki Y, Tang W, Makuuchi M, et al. Clinical and molecular insights into the hepatocellular carcinoma tumour marker des-gamma-carboxyprothrombin. Liver Int 2011;31: 22–35.

[83]

Matsubara M, Shiraha H, Kataoka J, et al. Des-gamma-carboxyl prothrombin is associated with tumor angiogenesis in hepatocellular carcinoma. J Gastroenterol Hepatol 2012;27: 1602–8.

[84]

Beale G, Chattopadhyay D, Gray J, et al. SCCA-1 and follisatin as surveillance biomarkers for hepatocellular cancer in non-alcoholic and alcoholic fatty liver disease. BMC Cancer 2008;8: 200.

[85]

Seo SI, Kim HS, Kim WJ, et al. Diagnostic value of PIVKA-Ⅱ and alpha-fetoprotein in hepatitis B virus-associated hepatocellular carcinoma. World J Gastroenterol 2015;21: 3928–35.

[86]

Nakamura S, Nouso K, Sakaguchi K, et al. Sensitivity and specificity of desgamma-carboxy prothrombin for diagnosis of patients with hepatocellular carcinomas varies according to tumor size. Am J Gastroenterol 2006;101: 2038–43.

[87]

Lee S, Rhim H, Kim YS, et al. Post-ablation des-gamma-carboxy prothrombin level predicts prognosis in hepatitis B-related hepatocellular carcinoma. Liver Int 2016; 36: 580–7.

[88]

Shang S, Plymoth A, Ge S, et al. Identification of osteopontin as a novel marker for early hepatocellular carcinoma. Hepatology 2012;55: 483–90.

[89]

Qin L. Osteopontin is a promoter for hepatocellular carcinoma metastasis: a summary of 10 years of studies. Front Med 2014;8: 24–32.

[90]

Zhu M, Zheng J, Wu F, et al. OPN is a promising serological biomarker for hepatocellular carcinoma diagnosis. J Med Virol 2020;92: 3596–603.

[91]

Capurro M, Wanless IR, Sherman M, et al. Glypican-3: a novel serum and histochemical marker for hepatocellular carcinoma. Gastroenterology 2003;125: 89–97.

[92]

Hey-Chi Hsu WC, Lai Po-Lin. Cloning and expression of a developmentally regulated transcript MXR7 in hepatocellular carcinoma: biological significance and temporospatial distribution. Cancer Res 1997;57: 5179–84.

[93]

Zhou F, Shang W, Yu X, et al. Glypican-3: a promising biomarker for hepatocellular carcinoma diagnosis and treatment. Med Res Rev 2018;38: 741–67.

[94]

Lu Q, Li J, Cao H, et al. Comparison of diagnostic accuracy of Midkine and AFP for detecting hepatocellular carcinoma: a systematic review and meta-analysis. Biosci Rep 2020;40.

[95]

Bin Sun CH, Yang Zhibin, Zhang Xiaofeng, et al. Midkine promotes hepatocellular carcinoma metastasis by elevating anoikis resistance of circulating tumor cells. Oncotarget 2017;8: 32523–35.

[96]

Pontisso P, Calabrese F, Benvegnu L, et al. Overexpression of squamous cell carcinoma antigen variants in hepatocellular carcinoma. Br J Cancer 2004;90: 833–7.

[97]

Chen H, Wong CC, Liu D, et al. APLN promotes hepatocellular carcinoma through activating PI3K/Akt pathway and is a druggable target. Theranostics 2019;9: 5246–60.

[98]

Okoror LE, Ajayi AO, Ijalana OB. Elevated serum beta2-microglobulin in individuals coinfected with hepatitis B and hepatitis D virus in a rural settings in Southwest Nigeria. BMC Res Notes 2017;10: 719.

[99]

Zhang R, Lin HM, Broering R, et al. Dickkopf-1 contributes to hepatocellular carcinoma tumorigenesis by activating the Wnt/beta-catenin signaling pathway. Signal Transduct Targeted Ther 2019;4: 54.

[100]

Sun W, Zhang Y, Wong KC, et al. Increased expression of GATA zinc finger domain containing 1 through gene amplification promotes liver cancer by directly inducing phosphatase of regenerating liver 3. Hepatology 2018;67: 2302–19.

[101]

da Costa AN, Plymoth A, Santos-Silva D, et al. Osteopontin and latent-TGF beta binding-protein 2 as potential diagnostic markers for HBV-related hepatocellular carcinoma. Int J Cancer 2015;136: 172–81.

[102]

Liu D, Wong CC, Fu L, et al. Squalene epoxidase drives NAFLD-induced hepatocellular carcinoma and is a pharmaceutical target. Sci Transl Med 2018; 10(437): eaap9840.

[103]

Korhani Kangi A, Bahrampour A. Predicting the survival of gastric cancer patients using artificial and bayesian neural networks. Asian Pac J Cancer Prev APJCP 2018;19: 487–90.

[104]

Liu S, Wang Y, Yang X, et al. Deep learning in medical ultrasound analysis: a review. Engineering 2019;5: 261–75.

[105]

Li W, Lv XZ, Zheng X, et al. Machine learning-based ultrasomics improves the diagnostic performance in differentiating focal nodular hyperplasia and atypical hepatocellular carcinoma. Front Oncol 2021;11: 544979.

[106]

Yang Q, Wei J, Hao X, et al. Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: a multicentre study. EBioMedicine 2020;56: 102777.

[107]

Kulkarni NM, Fung A, Kambadakone AR, et al. Computed tomography techniques, protocols, advancements, and future directions in liver diseases. Magn Reson Imag Clin N Am 2021;29: 305–20.

[108]

Cao SE, Zhang LQ, Kuang SC, et al. Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography. World J Gastroenterol 2020;26: 3660–72.

[109]

Zhang H, Luo K, Deng R, et al. Deep learning-based CT imaging for the diagnosis of liver tumor. Comput Intell Neurosci 2022;2022: 3045370.

[110]

Zhao C, Dai H, Shao J, et al. Accuracy of various forms of contrast-enhanced MRI for diagnosing hepatocellular carcinoma: a systematic review and meta-analysis. Front Oncol 2021;11: 680691.

[111]

Wang SH, Han XJ, Du J, et al. Saliency-based 3D convolutional neural network for categorising common focal liver lesions on multisequence MRI. Insights Imaging 2021;12: 173.

[112]

Liang W, Shao J, Liu W, et al. Differentiating hepatic epithelioid angiomyolipoma from hepatocellular carcinoma and focal nodular hyperplasia via radiomics models. Front Oncol 2020;10: 564307.

[113]

Zhao X, Zhou Y, Zhang Y, et al. Radiomics based on contrast-enhanced MRI in differentiation between fat-poor angiomyolipoma and hepatocellular carcinoma in noncirrhotic liver: a multicenter analysis. Front Oncol 2021;11: 744756.

[114]

Webster JD, Dunstan RW. Whole-slide imaging and automated image analysis: considerations and opportunities in the practice of pathology. Vet Pathol 2014;51: 211–23.

[115]

Bera K, Schalper KA, Rimm DL, et al. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat Rev Clin Oncol 2019;16: 703–15.

[116]

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521: 436–44.

[117]

Cheng N, Ren Y, Zhou J, et al. Deep learning-based classification of hepatocellular nodular lesions on whole-slide histopathologic images. Gastroenterology 2022; 162: 1948–1961 e1947.

[118]

Kiani A, Uyumazturk B, Rajpurkar P, et al. Impact of a deep learning assistant on the histopathologic classification of liver cancer. NPJ Digit Med 2020;3: 23.

iLIVER
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Cite this article:
Fang G, Fan J, Ding Z, et al. Application of biological big data and radiomics in hepatocellular carcinoma. iLIVER, 2023, 2(1): 41-49. https://doi.org/10.1016/j.iliver.2023.01.003

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Received: 06 November 2022
Revised: 10 January 2023
Accepted: 27 January 2023
Published: 04 February 2023
© 2023 Published by Elsevier Ltd on behalf of Tsinghua University Press.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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