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image of Leveraging AI and Natural Compounds: Innovative Approaches in the Diagnosis and Treatment of Hepatocellular Carcinoma

Abstract

Liver cancer, particularly Hepatocellular Carcinoma (HCC), remains a significant global health challenge owing to its high incidence and position as the fourth leading cause of cancer-related mortality. HCC represents 75-85% of all liver cancer cases and ranks as the sixth most prevalent cancer globally. Several factors, including late-stage diagnosis, limited treatment effectiveness, resistance to conventional therapies, and adverse side effects, hinder the delivery of life-prolonging care to patients with HCC. Current treatment options such as chemotherapy, immunotherapy, and adjuvant therapy are often associated with severe side effects. Consequently, there is an urgent need for improved diagnostic methods and alternative therapeutic approaches to extend life expectancy and reduce HCC-related mortalities. Artificial Intelligence (AI) is an emerging technology that offers promising advances for the early detection of HCC. In terms of alternative treatments, natural compounds have garnered significant attention because of their diverse biological activities, such as antitumor, antiviral, antimicrobial, antioxidant, anti-inflammatory, hepatoprotective, antimutagenic, and cardioprotective effects, and their relatively lower side effect profiles. These compounds exhibit hepatoprotective properties by modulating key molecular pathways involved in HCC development and progression. This article provides an overview of recent advances in the understanding of liver cancer etiology, therapeutic targets in HCC pathogenesis, the role of AI in its detection, and the potential of natural products, particularly flavonoids, as preventive and therapeutic agents against HCC, highlighting their underlying mechanisms of action.

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2025-02-28
2025-03-30

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References

  1. McGlynn K.A. Petrick J.L. Serag E.H.B. Epidemiology of hepatocellular carcinoma. Hepatology 2021 73 4 13 10.1002/hep.31288
    [Google Scholar]
  2. Mukthinuthalapati K.V. V. P. Sewram V. Ndlovu N. Kimani S. Abdelaziz A. O. Chiao E. Y. Alfa A.G. K. Hepatocellular carcinoma in sub-Saharan Africa. JCO Global Oncol. 2021 7 756 766 10.1200/GO.20.00425
    [Google Scholar]
  3. Ferlay J. Colombet M. Soerjomataram I. Mathers C. Parkin D.M. Piñeros M. Znaor A. Bray F. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int. J. Cancer 2019 144 8 1941 1953 10.1002/ijc.31937 30350310
    [Google Scholar]
  4. WHO, Cancer Today Data visualization tools for exploring the global cancer burden in 2022. Available from: http://gco.iarc.fr/today/data/factsheets/ cancers/11-Liver-fact-sheet.pdf 2022
  5. Suhail M. Sohrab S.S. Kamal M.A. Azhar E.I. Role of hepatitis c virus in hepatocellular carcinoma and neurological disorders: An overview. Front. Oncol. 2022 12 913231 10.3389/fonc.2022.913231 35965577
    [Google Scholar]
  6. Llovet J.M. Rossi Z.J. Pikarsky E. Sangro B. Schwartz M. Sherman M. Gores G. Hepatocellular carcinoma. Nat. Rev. Dis. Primers 2016 2 1 16018 10.1038/nrdp.2016.18 27158749
    [Google Scholar]
  7. Fujiwara N. Friedman S.L. Goossens N. Hoshida Y. Risk factors and prevention of hepatocellular carcinoma in the era of precision medicine. J. Hepatol. 2018 68 3 526 549 10.1016/j.jhep.2017.09.016 28989095
    [Google Scholar]
  8. Forner A. Reig M. Bruix J. Hepatocellular carcinoma. Lancet 2018 391 10127 1301 1314 10.1016/S0140‑6736(18)30010‑2 29307467
    [Google Scholar]
  9. Avila M.A. Dufour J.F. Gerbes A.L. Zoulim F. Bataller R. Burra P. Pinto C.H. Gao B. Gilmore I. Mathurin P. Moreno C. Poznyak V. Schnabl B. Szabo G. Thiele M. Thursz M.R. Recent advances in alcohol-related liver disease (ALD): Summary of a Gut round table meeting. Gut 2020 69 4 764 780 10.1136/gutjnl‑2019‑319720 31879281
    [Google Scholar]
  10. Ho D.W.H. Lo R.C.L. Chan L.K. Ng I.O.L. Molecular pathogenesis of hepatocellular carcinoma. Liver Cancer 2016 5 4 290 302 10.1159/000449340 27781201
    [Google Scholar]
  11. Calderaro J. Seraphin T.P. Luedde T. Simon T.G. Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma. J. Hepatol. 2022 76 6 1348 1361 10.1016/j.jhep.2022.01.014 35589255
    [Google Scholar]
  12. Hashimoto D.A. Rosman G. Rus D. Meireles O.R. Artificial intelligence in surgery: Promises and perils. Ann. Surg. 2018 268 1 70 76 10.1097/SLA.0000000000002693 29389679
    [Google Scholar]
  13. Taha A. Ochs V. Kayhan L.N. Enodien B. Frey D.M. Krähenbühl L. Mehlitz T.S. Advancements of artificial intelligence in liver-associated diseases and surgery. Medicina 2022 58 4 459 10.3390/medicina58040459 35454298
    [Google Scholar]
  14. Galle P.R. Forner A. Llovet J.M. Mazzaferro V. Piscaglia F. Raoul J-L. Schirmacher P. Vilgrain V. Electronic address, e. e. e.; European association for the study of the, L., EASL Clinical practice guidelines: Management of hepatocellular carcinoma. J. Hepatol. 2018 69 1 182 236 10.1016/j.jhep.2018.03.019 29628281
    [Google Scholar]
  15. Bruix J. Sherman M. Management of hepatocellular carcinoma: An update. Hepatology 2011 53 3 1020 1022 10.1002/hep.24199 21374666
    [Google Scholar]
  16. Kim D.B. Lee D.K. Cheon C. Ribeiro R.I.M.A. Kim B. Natural products for liver cancer treatment: From traditional medicine to modern drug discovery. Nutrients 2022 14 20 4252 10.3390/nu14204252 36296934
    [Google Scholar]
  17. Maiuolo J. Gliozzi M. Carresi C. Musolino V. Oppedisano F. Scarano F. Nucera S. Scicchitano M. Bosco F. Macri R. Ruga S. Cardamone A. Coppoletta A. Mollace A. Cognetti F. Mollace V. Nutraceuticals and cancer: Potential for natural polyphenols. Nutrients 2021 13 11 3834 10.3390/nu13113834 34836091
    [Google Scholar]
  18. Muhammad N. Usmani D. Tarique M. Naz H. Ashraf M. Raliya R. Tabrez S. Zughaibi T.A. Alsaieedi A. Hakeem I.J. Suhail M. The role of natural products and their multitargeted approach to treat solid cancer. Cells 2022 11 14 2209 10.3390/cells11142209 35883653
    [Google Scholar]
  19. López P.F.E. García G.F. Jornet L.P. Combination of 5-florouracil and polyphenol EGCG exerts suppressive effects on oral cancer cells exposed to radiation. Arch. Oral Biol. 2019 101 8 12 10.1016/j.archoralbio.2019.02.018 30851692
    [Google Scholar]
  20. Panji M. Behmard V. Zare Z. Malekpour M. Nejadbiglari H. Yavari S. dizaj N.T. Safaeian A. Bakhshi A. Abazari O. Abbasi M. Khanicheragh P. Shabanzadeh M. Synergistic effects of green tea extract and paclitaxel in the induction of mitochondrial apoptosis in ovarian cancer cell lines. Gene 2021 787 145638 10.1016/j.gene.2021.145638 33848578
    [Google Scholar]
  21. Dash S. Aydin Y. Widmer K.E. Nayak L. Hepatocellular carcinoma mechanisms associated with chronic HCV infection and the impact of direct-acting antiviral treatment. J. Hepatocell. Carcinoma 2020 7 45 76 10.2147/JHC.S221187 32346535
    [Google Scholar]
  22. Chen C. Wang G. Mechanisms of hepatocellular carcinoma and challenges and opportunities for molecular targeted therapy. World J. Hepatol. 2015 7 15 1964 1970 10.4254/wjh.v7.i15.1964 26244070
    [Google Scholar]
  23. Nakamoto Y. Promising new strategies for hepatocellular carcinoma. Hepatol. Res. 2017 47 4 251 265 10.1111/hepr.12795 27558453
    [Google Scholar]
  24. Loba T.A. Manieri E. Terán G.B. Mora A. Vega L.L. Santamans A.M. Becerra R.R. Rodríguez E. Chocano P.A. Feixas F. López J.A. Caballero B. Trakala M. Blanco Ó. Torres J.L. Cosido H.L. Romeral M.V. Matesanz N. Molina R.M. Bernal J.A. Mischo H. León M. Caballero A. Saavedra M.D. Cabello R.J. Nevzorova Y.A. Cubero F.J. Bravo J. Vázquez J. Malumbres M. Marcos M. Osuna S. Sabio G. p38γ is essential for cell cycle progression and liver tumorigenesis. Nature 2019 568 7753 557 560 10.1038/s41586‑019‑1112‑8 30971822
    [Google Scholar]
  25. Udden S.M.N. Kwak Y.T. Godfrey V. Khan M.A.W. Khan S. Loof N. Peng L. Zhu H. Zaki H. NLRP12 suppresses hepatocellular carcinoma via downregulation of cJun N-terminal kinase activation in the hepatocyte. eLife 2019 8 e40396 10.7554/eLife.40396 30990169
    [Google Scholar]
  26. Suhail M. Tarique M. Muhammad N. Naz H. Hafeez A. Zughaibi T.A. Kamal M.A. Rehan M. A critical transcription factor NF-κB as a cancer therapeutic target and its inhibitors as cancer treatment options. Curr. Med. Chem. 2021 28 21 4117 4132 10.2174/1875533XMTExnMzki1 33176636
    [Google Scholar]
  27. Hirsch T.Z. Negulescu A. Gupta B. Caruso S. Noblet B. Couchy G. Bayard Q. Meunier L. Morcrette G. Scoazec J.Y. Blanc J.F. Amaddeo G. Nault J.C. Sage B.P. Ziol M. Beaufrère A. Paradis V. Calderaro J. Imbeaud S. Rossi Z.J. BAP1 mutations define a homogeneous subgroup of hepatocellular carcinoma with fibrolamellar-like features and activated PKA. J. Hepatol. 2020 72 5 924 936 10.1016/j.jhep.2019.12.006 31862487
    [Google Scholar]
  28. Islam u.B. Suhail M. Khan M.S. Ahmad A. Zughaibi T.A. Husain F.M. Rehman M.T. Tabrez S. Flavonoids and PI3K/Akt/mTOR signaling cascade: A potential crosstalk in anticancer treatment. Curr. Med. Chem. 2021 28 39 8083 8097 10.2174/1875533XMTE3iMDAs3 34348607
    [Google Scholar]
  29. Vescovo T. Refolo G. Vitagliano G. Fimia G.M. Piacentini M. Molecular mechanisms of hepatitis C virus–induced hepatocellular carcinoma. Clin. Microbiol. Infect. 2016 22 10 853 861 10.1016/j.cmi.2016.07.019 27476823
    [Google Scholar]
  30. Nault J.C. Ningarhari M. Rebouissou S. Rossi Z.J. The role of telomeres and telomerase in cirrhosis and liver cancer. Nat. Rev. Gastroenterol. Hepatol. 2019 16 9 544 558 10.1038/s41575‑019‑0165‑3 31253940
    [Google Scholar]
  31. Pezzuto F. Buonaguro L. Buonaguro F.M. Tornesello M.L. Frequency and geographic distribution of TERT promoter mutations in primary hepatocellular carcinoma. Infect. Agent. Cancer 2017 12 1 27 10.1186/s13027‑017‑0138‑5 28529542
    [Google Scholar]
  32. Choudhary H.B. Mandlik S.K. Mandlik D.S. Role of p53 suppression in the pathogenesis of hepatocellular carcinoma. World J. Gastrointest. Pathophysiol. 2023 14 3 46 70 10.4291/wjgp.v14.i3.46 37304923
    [Google Scholar]
  33. Kanda T. Goto T. Hirotsu Y. Moriyama M. Omata M. Molecular mechanisms driving progression of liver cirrhosis towards hepatocellular carcinoma in chronic hepatitis B and C infections: A review. Int. J. Mol. Sci. 2019 20 6 1358 10.3390/ijms20061358 30889843
    [Google Scholar]
  34. Wang W. Pan Q. Fuhler G.M. Smits R. Peppelenbosch M.P. Action and function of Wnt/β-catenin signaling in the progression from chronic hepatitis C to hepatocellular carcinoma. J. Gastroenterol. 2017 52 4 419 431 10.1007/s00535‑016‑1299‑5 28035485
    [Google Scholar]
  35. Sun E.J. Wankell M. Palamuthusingam P. McFarlane C. Hebbard L. Targeting the PI3K/Akt/mTOR pathway in hepatocellular carcinoma. Biomedicines 2021 9 11 1639 10.3390/biomedicines9111639 34829868
    [Google Scholar]
  36. Zughaibi T.A. Suhail M. Tarique M. Tabrez S. Targeting PI3K/Akt/mTOR pathway by different flavonoids: A cancer chemopreventive approach. Int. J. Mol. Sci. 2021 22 22 12455 10.3390/ijms222212455 34830339
    [Google Scholar]
  37. Chu Q. Gu X. Zheng Q. Zhu H. Regulatory mechanism of HIF-1α and its role in liver diseases: A narrative review. Ann. Transl. Med. 2022 10 2 109 10.21037/atm‑21‑4222 35282052
    [Google Scholar]
  38. Suhail M. Sohrab S.S. Qureshi A. Tarique M. Hafiz A.H. Ghamdi A.K. Qadri I. Association of HCV mutated proteins and host SNPs in the development of hepatocellular carcinoma. Infect. Genet. Evol. 2018 60 160 172 10.1016/j.meegid.2018.02.034 29501636
    [Google Scholar]
  39. Hassan M. Selimovic D. Ghozlan H. kader A.O. Hepatitis C virus core protein triggers hepatic angiogenesis by a mechanism including multiple pathways #. Hepatology 2009 49 5 1469 1482 10.1002/hep.22849 19235829
    [Google Scholar]
  40. Calderaro J. Žigutytė L. Truhn D. Jaffe A. Kather J.N. Artificial intelligence in liver cancer — new tools for research and patient management. Nat. Rev. Gastroenterol. Hepatol. 2024 21 8 585 599 10.1038/s41575‑024‑00919‑y 38627537
    [Google Scholar]
  41. Xu N. Yang D. Arikawa K. Bai C. Application of artificial intelligence in modern medicine. Clinical eHealth 2023 6 130 137 10.1016/j.ceh.2023.09.001
    [Google Scholar]
  42. Bakrania A. Joshi N. Zhao X. Zheng G. Bhat M. Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases. Pharmacol. Res. 2023 189 106706 10.1016/j.phrs.2023.106706 36813095
    [Google Scholar]
  43. Larrain C. Hernandez T.A. Hewitt D.B. Artificial intelligence, machine learning, and deep learning in the diagnosis and management of hepatocellular carcinoma. Livers 2024 4 1 36 50 10.3390/livers4010004
    [Google Scholar]
  44. Anstee Q.M. Reeves H.L. Kotsiliti E. Govaere O. Heikenwalder M. From NASH to HCC: Current concepts and future challenges. Nat. Rev. Gastroenterol. Hepatol. 2019 16 7 411 428 10.1038/s41575‑019‑0145‑7 31028350
    [Google Scholar]
  45. Alowais S.A. Alghamdi S.S. Alsuhebany N. Alqahtani T. Alshaya A.I. Almohareb S.N. Aldairem A. Alrashed M. Saleh B.K. Badreldin H.A. Yami A.M.S. Harbi A.S. Albekairy A.M. Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Med. Educ. 2023 23 1 689 10.1186/s12909‑023‑04698‑z 37740191
    [Google Scholar]
  46. Shen M. Zou Z. Bao H. Fairley C.K. Canfell K. Ong J.J. Hocking J. Chow E.P.F. Zhuang G. Wang L. Zhang L. Cost-effectiveness of artificial intelligence-assisted liquid-based cytology testing for cervical cancer screening in China. Lancet Reg. Health West. Pac. 2023 34 100726 10.1016/j.lanwpc.2023.100726 37283979
    [Google Scholar]
  47. Mital S. Nguyen H.V. Cost-effectiveness of using artificial intelligence versus polygenic risk score to guide breast cancer screening. BMC Cancer 2022 22 1 501 10.1186/s12885‑022‑09613‑1 35524200
    [Google Scholar]
  48. Ziegelmayer S. Graf M. Makowski M. Gawlitza J. Gassert F. Cost-effectiveness of artificial intelligence support in computed tomography-based lung cancer screening. Cancers 2022 14 7 1729 10.3390/cancers14071729 35406501
    [Google Scholar]
  49. Valvert F. Silva O. Ortiz S.E. Puligandla M. Tala S.M.M. Guyon T. Dixon S.L. López N. López F. Alvarado C.C.C. Terbrueggen R. Stevenson K.E. Natkunam Y. Weinstock D.M. Briercheck E.L. Low-cost transcriptional diagnostic to accurately categorize lymphomas in low- and middle-income countries. Blood Adv. 2021 5 10 2447 2455 10.1182/bloodadvances.2021004347 33988700
    [Google Scholar]
  50. Konerman M.A. Zhang Y. Zhu J. Higgins P.D.R. Lok A.S.F. Waljee A.K. Improvement of predictive models of risk of disease progression in chronic hepatitis C by incorporating longitudinal data. Hepatology 2015 61 6 1832 1841 10.1002/hep.27750 25684666
    [Google Scholar]
  51. Liang Q. Liu H. Wang C. Li B. Phenotypic characterization analysis of human hepatocarcinoma by urine metabolomics approach. Sci. Rep. 2016 6 1 19763 10.1038/srep19763 26805550
    [Google Scholar]
  52. Reddy R. Imler T.D. Artificial neural networks are highly predictive for hepatocellular carcinoma in patients with cirrhosis. Gastroenterology 2017 152 5 S1193 10.1016/S0016‑5085(17)33977‑X
    [Google Scholar]
  53. Van T. 498 – Deep learning models accurately predict development of Hcc in 146,218 patients with chronic hepatitis C. Gastroenterology 2019 156 6 S 1201
    [Google Scholar]
  54. Książek W. Abdar M. Acharya U.R. Pławiak P. A novel machine learning approach for early detection of hepatocellular carcinoma patients. Cogn. Syst. Res. 2019 54 116 127 10.1016/j.cogsys.2018.12.001
    [Google Scholar]
  55. Calderaro J. Laleh G.N. Zeng Q. Maille P. Favre L. Pujals A. Klein C. Bazille C. Heij L.R. Uguen A. Luedde T. Tommaso D.L. Beaufrère A. Chatain A. Gastineau D. Nguyen C.T. Canh N.H. Thi K.N. Gnemmi V. Graham R.P. Charlotte F. Wendum D. Vij M. Allende D.S. Aucejo F. Diaz A. Rivière B. Herrero A. Evert K. Calvisi D.F. Augustin J. Leow W.Q. Leung H.H.W. Boleslawski E. Rela M. François A. Cha A.W.H. Forner A. Reig M. Allaire M. Scatton O. Chatelain D. Rombi B.C. Sturm N. Menahem B. Frouin E. Tougeron D. Tournigand C. Kempf E. Kim H. Ningarhari M. Provost M.S. Gopal P. Brustia R. Vibert E. Schulze K. Rüther D.F. Weidemann S.A. Rhaiem R. Pawlotsky J.M. Zhang X. Luciani A. Mulé S. Laurent A. Amaddeo G. Regnault H. Martin D.E. Sempoux C. Navale P. Westerhoff M. Lo R.C.L. Bednarsch J. Gouw A. Guettier C. Lequoy M. Harada K. Sripongpun P. Wetwittayaklang P. Loménie N. Tantipisit J. Kaewdech A. Shen J. Paradis V. Caruso S. Kather J.N. Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma. Nat. Commun. 2023 14 1 8290 10.1038/s41467‑023‑43749‑3 38092727
    [Google Scholar]
  56. Mansur A. Vrionis A. Charles J.P. Hancel K. Panagides J.C. Moloudi F. Iqbal S. Daye D. The role of artificial intelligence in the detection and implementation of biomarkers for hepatocellular carcinoma: Outlook and opportunities. Cancers 2023 15 11 2928 10.3390/cancers15112928 37296890
    [Google Scholar]
  57. Sato M. Morimoto K. Kajihara S. Tateishi R. Shiina S. Koike K. Yatomi Y. Machine-learning approach for the development of a novel predictive model for the diagnosis of hepatocellular carcinoma. Sci. Rep. 2019 9 1 7704 10.1038/s41598‑019‑44022‑8 31147560
    [Google Scholar]
  58. Gui T. Dong X. Li R. Li Y. Wang Z. Identification of hepatocellular carcinoma-related genes with a machine learning and network analysis. J. Comput. Biol. 2015 22 1 63 71 10.1089/cmb.2014.0122 25247452
    [Google Scholar]
  59. Zhang C. Peng L. Zhang Y. Liu Z. Li W. Chen S. Li G. The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data. Med. Oncol. 2017 34 6 101 10.1007/s12032‑017‑0963‑9 28432618
    [Google Scholar]
  60. Ibrahim R. Yousri N.A. Ismail M.A. Makky E.N.M. Multi-level gene/MiRNA feature selection using deep belief nets and active learning. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2014 2014 3957 3960 10.1109/EMBC.2014.6944490 25570858
    [Google Scholar]
  61. Chaudhary K. Poirion O.B. Lu L. Garmire L.X. Deep learning–based multi-omics integration robustly predicts survival in liver cancer. Clin. Cancer Res. 2018 24 6 1248 1259 10.1158/1078‑0432.CCR‑17‑0853 28982688
    [Google Scholar]
  62. Zhang J. Huang S. Xu Y. Wu J. Diagnostic accuracy of artificial intelligence based on imaging data for preoperative prediction of microvascular invasion in hepatocellular carcinoma: A systematic review and meta-analysis. Front. Oncol. 2022 12 763842 10.3389/fonc.2022.763842 35280776
    [Google Scholar]
  63. Liu S.C. Lai J. Huang J.Y. Cho C.F. Lee P.H. Lu M.H. Yeh C.C. Yu J. Lin W.C. Predicting microvascular invasion in hepatocellular carcinoma: A deep learning model validated across hospitals. Canc. Imag. 2021 21 1 56 10.1186/s40644‑021‑00425‑3 34627393
    [Google Scholar]
  64. Sun S.W. Xu X. Liu Q.P. Chen J.N. Zhu F.P. Liu X.S. Zhang Y.D. Wang J. LiSNet: An artificial intelligence ‐based tool for liver imaging staging of hepatocellular carcinoma aggressiveness. Med. Phys. 2022 49 11 6903 6913 10.1002/mp.15972 36134900
    [Google Scholar]
  65. Chong H.H. Yang L. Sheng R.F. Yu Y.L. Wu D.J. Rao S.X. Yang C. Zeng M.S. Multi-scale and multi-parametric radiomics of gadoxetate disodium–enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma ≤ 5 cm. Eur. Radiol. 2021 31 7 4824 4838 10.1007/s00330‑020‑07601‑2 33447861
    [Google Scholar]
  66. Yan M. Zhang X. Zhang B. Geng Z. Xie C. Yang W. Zhang S. Qi Z. Lin T. Ke Q. Li X. Wang S. Quan X. Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy. Eur. Radiol. 2023 33 7 4949 4961 10.1007/s00330‑023‑09419‑0 36786905
    [Google Scholar]
  67. Safonova A. Ghazaryan G. Stiller S. Knorn M.M. Nendel C. Ryo M. Ten deep learning techniques to address small data problems with remote sensing. Int. J. Appl. Earth Obs. Geoinf. 2023 125 103569 10.1016/j.jag.2023.103569
    [Google Scholar]
  68. Kawka M. Dawidziuk A. Jiao L.R. Gall T.M.H. Artificial intelligence in the detection, characterisation and prediction of hepatocellular carcinoma: A narrative review. Transl. Gastroenterol. Hepatol. 2022 7 41 10.21037/tgh‑20‑242 36300146
    [Google Scholar]
  69. Taghanaki A.S. Abhishek K. Cohen J.P. Adad C.J. Hamarneh G. Deep semantic segmentation of natural and medical images: A review. Artif. Intell. Rev. 2021 54 1 137 178 10.1007/s10462‑020‑09854‑1
    [Google Scholar]
  70. Li D. Yang J. Kreis K. Torralba A. Fidler S. Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization. arxiv 2021 2104.05833 10.1109/CVPR46437.2021.00820
    [Google Scholar]
  71. Kiani A. Uyumazturk B. Rajpurkar P. Wang A. Gao R. Jones E. Yu Y. Langlotz C.P. Ball R.L. Montine T.J. Martin B.A. Berry G.J. Ozawa M.G. Hazard F.K. Brown R.A. Chen S.B. Wood M. Allard L.S. Ylagan L. Ng A.Y. Shen J. Impact of a deep learning assistant on the histopathologic classification of liver cancer. NPJ Digit. Med. 2020 3 1 23 10.1038/s41746‑020‑0232‑8 32140566
    [Google Scholar]
  72. User-generated visual guide for the classification of images. U.S. Patent No. 11,182,646, 2021
  73. Kuzina A. Egorov E. Burnaev E. Bayesian generative models for knowledge transfer in MRI semantic segmentation problems. Front. Neurosci. 2019 13 844 10.3389/fnins.2019.00844 31496928
    [Google Scholar]
  74. Chen L-C. Zhu Y. Papandreou G. Schroff F. Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation. Lect. Not. Comp. Sci. Ferrari V. Hebert M. Sminchisescu C. Weiss Y. Springer, Cham 2018 11211 833 851
    [Google Scholar]
  75. Ronneberger O. Fischer P. Brox T. U-Net: Convolutional networks for biomedical image segmentation. Springer International Publishing 2015 234 241
    [Google Scholar]
  76. Gentile D. Donadon M. Lleo A. Aghemo A. Roncalli M. Tommaso d.L. Torzilli G. Surgical treatment of hepatocholangiocarcinoma: A systematic review. Liver Canc. 2020 9 1 15 27 10.1159/000503719 32071906
    [Google Scholar]
  77. Wong T. Lo C. Resection strategies for hepatocellular carcinoma. Semin. Liver Dis. 2013 33 3 273 281 10.1055/s‑0033‑1351782 23943107
    [Google Scholar]
  78. Leowattana W. Leowattana T. Leowattana P. Systemic treatment for unresectable hepatocellular carcinoma. World J. Gastroenterol. 2023 29 10 1551 1568 10.3748/wjg.v29.i10.1551 36970588
    [Google Scholar]
  79. Gordan J.D. Kennedy E.B. Alfa A.G.K. Beal E. Finn R.S. Gade T.P. Goff L. Gupta S. Guy J. Hoang H.T. Iyer R. Jaiyesimi I. Jhawer M. Karippot A. Kaseb A.O. Kelley R.K. Kortmansky J. Leaf A. Remak W.M. Sohal D.P.S. Taddei T.H. Woods W.A. Yarchoan M. Rose M.G. Systemic therapy for advanced hepatocellular carcinoma: ASCO guideline update. J. Clin. Oncol. 2024 42 15 1830 1850 10.1200/JCO.23.02745 38502889
    [Google Scholar]
  80. Rimassa L. Pressiani T. Merle P. Systemic treatment options in hepatocellular carcinoma. Liver Cancer 2019 8 6 427 446 10.1159/000499765 31799201
    [Google Scholar]
  81. Mandlik D.S. Mandlik S.K. Herbal and natural dietary products: Upcoming therapeutic approach for prevention and treatment of hepatocellular carcinoma. Nutr. Cancer 2021 73 11-12 2130 2154 10.1080/01635581.2020.1834591 33073617
    [Google Scholar]
  82. Zhou D.D. Luo M. Shang A. Mao Q.Q. Li B.Y. Gan R.Y. Li H.B. Antioxidant food components for the prevention and treatment of cardiovascular diseases: Effects, mechanisms, and clinical studies. Oxid. Med. Cell. Longev. 2021 2021 1 6627355 10.1155/2021/6627355 33574978
    [Google Scholar]
  83. Graf B.L. Raskin I. Cefalu W.T. Ribnicky D.M. Plant-derived therapeutics for the treatment of metabolic syndrome. Curr. Opin. Investig. Drugs 2010 11 10 1107 1115 20872313
    [Google Scholar]
  84. Rad S.M. Kumar A.N.V. Zucca P. Varoni E.M. Dini L. Panzarini E. Rajkovic J. Fokou T.P.V. Azzini E. Peluso I. Mishra P.A. Nigam M. Rayess E.Y. Beyrouthy M.E. Polito L. Iriti M. Martins N. Martorell M. Docea A.O. Setzer W.N. Calina D. Cho W.C. Rad S.J. Lifestyle, oxidative stress, and antioxidants: Back and forth in the pathophysiology of chronic diseases. Front. Physiol. 2020 11 694 10.3389/fphys.2020.00694 32714204
    [Google Scholar]
  85. Bailly C. Molecular and cellular basis of the anticancer activity of the prenylated flavonoid icaritin in hepatocellular carcinoma. Chem. Biol. Interact. 2020 325 109124 10.1016/j.cbi.2020.109124 32437694
    [Google Scholar]
  86. Fan Y. Li S. Ding X. Yue J. Jiang J. Zhao H. Hao R. Qiu W. Liu K. Li Y. Wang S. Zheng L. Ye B. Meng K. Xu B. First-in-class immune-modulating small molecule Icaritin in advanced hepatocellular carcinoma: Preliminary results of safety, durable survival and immune biomarkers. BMC Cancer 2019 19 1 279 10.1186/s12885‑019‑5471‑1 30922248
    [Google Scholar]
  87. Howells L.M. Berry D.P. Elliott P.J. Jacobson E.W. Hoffmann E. Hegarty B. Brown K. Steward W.P. Gescher A.J. Phase I randomized, double-blind pilot study of micronized resveratrol (SRT501) in patients with hepatic metastases--safety, pharmacokinetics, and pharmacodynamics. Cancer Prev. Res. 2011 4 9 1419 1425 10.1158/1940‑6207.CAPR‑11‑0148 21680702
    [Google Scholar]
  88. Faghihzadeh F. Adibi P. Rafiei R. Hekmatdoost A. Resveratrol supplementation improves inflammatory biomarkers in patients with nonalcoholic fatty liver disease. Nutr. Res. 2014 34 10 837 843 10.1016/j.nutres.2014.09.005 25311610
    [Google Scholar]
  89. Siegel A.B. Narayan R. Rodriguez R. Goyal A. Jacobson J.S. Kelly K. Ladas E. Lunghofer P.J. Hansen R.J. Gustafson D.L. Flaig T.W. Tsai W.Y. Wu D.P.H. Lee V. Greenlee H. A phase I dose-finding study of silybin phosphatidylcholine (milk thistle) in patients with advanced hepatocellular carcinoma. Integr. Cancer Ther. 2014 13 1 46 53 10.1177/1534735413490798 23757319
    [Google Scholar]
  90. Lu M. Fei Z. Zhang G. Synergistic anticancer activity of 20(S)- Ginsenoside Rg3 and Sorafenib in hepatocellular carcinoma by modulating PTEN/Akt signaling pathway. Biomed. Pharmacother. 2018 97 1282 1288 10.1016/j.biopha.2017.11.006 29156516
    [Google Scholar]
  91. Wei Q. Ren Y. Zheng X. Yang S. Lu T. Ji H. Hua H. Shan K. Ginsenoside Rg3 and sorafenib combination therapy relieves the hepatocellular carcinomaprogression through regulating the HK2-mediated glycolysis and PI3K/Akt signaling pathway. Bioengineered 2022 13 5 13919 13928 10.1080/21655979.2022.2074616 35719058
    [Google Scholar]
  92. Hung C.H. Kee K.M. Chen C.H. Tseng P. Tsai M.C. Chen C.H. Wang J.H. Chang K.C. Kuo Y.H. Yen Y.H. Hu T.H. Lu S.N. A randomized controlled trial of glycyrrhizin plus tenofovir vs. tenofovir in chronic hepatitis B with severe acute exacerbation. Clin. Transl. Gastroenterol. 2017 8 6 e104 10.1038/ctg.2017.29 28662023
    [Google Scholar]
  93. Yan H.M. Xia M.F. Wang Y. Chang X.X. Yao X.Z. Rao S.X. Zeng M.S. Tu Y.F. Feng R. Jia W.P. Liu J. Deng W. Jiang J.D. Gao X. Efficacy of berberine in patients with non-alcoholic fatty liver disease. PLoS One 2015 10 8 e0134172 10.1371/journal.pone.0134172 26252777
    [Google Scholar]
  94. Zhao L. Cang Z. Sun H. Nie X. Wang N. Lu Y. Berberine improves glucogenesis and lipid metabolism in nonalcoholic fatty liver disease. BMC Endocr. Disord. 2017 17 1 13 10.1186/s12902‑017‑0165‑7 28241817
    [Google Scholar]
  95. Tanaka T. Sato T. Nishiofuku H. Masada T. Tatsumoto S. Marugami N. Otsuji T. Kanno M. Koyama F. Sho M. Kichikawa K. Selective TACE with irinotecan-loaded 40 μm microspheres and FOLFIRI for colorectal liver metastases: Phase I dose escalation pharmacokinetic study. BMC Cancer 2019 19 1 758 10.1186/s12885‑019‑5862‑3 31370815
    [Google Scholar]
  96. Brandi G. Biasco G. Mirarchi M.G. Golfieri R. Paolo D.A. Borghi A. Fanello S. Derenzini E. Agostini V. Giampalma E. Cappelli A. Pini P. Costantini S. Danesi R. Bolondi L. Piscaglia F. A phase I study of continuous hepatic arterial infusion of Irinotecan in patients with locally advanced hepatocellular carcinoma. Dig. Liver Dis. 2011 43 12 1015 1021 10.1016/j.dld.2011.08.005 21917536
    [Google Scholar]
  97. Bie B. Sun J. Guo Y. Li J. Jiang W. Yang J. Huang C. Li Z. Baicalein: A review of its anti-cancer effects and mechanisms in hepatocellular carcinoma. Biomed. Pharmacother. 2017 93 1285 1291 10.1016/j.biopha.2017.07.068 28747003
    [Google Scholar]
  98. Amagase H. Sun B. Borek C. Lycium barbarum (goji) juice improves in vivo antioxidant biomarkers in serum of healthy adults. Nutr. Res. 2009 29 1 19 25 10.1016/j.nutres.2008.11.005 19185773
    [Google Scholar]
  99. Polyak S.J. Oberlies N.H. Pécheur E.I. Dahari H. Ferenci P. Pawlotsky J.M. Silymarin for HCV infection. Antivir. Ther. 2013 18 2 141 147 10.3851/IMP2402 23011959
    [Google Scholar]
  100. Yang P. Jiang Y. Pan Y. Ding X. Rhea P. Ding J. Hawke D.H. Felsher D. Narla G. Lu Z. Lee R.T. Mistletoe extract Fraxini inhibits the proliferation of liver cancer by down-regulating c-Myc expression. Sci. Rep. 2019 9 1 6428 10.1038/s41598‑019‑41444‑2 31015523
    [Google Scholar]
  101. Zhou B. Yan Z. Liu R. Shi P. Qian S. Qu X. Zhu L. Zhang W. Wang J. Prospective study of transcatheter arterial chemoembolization (TACE) with ginsenoside Rg3 versus TACE alone for the treatment of patients with advanced hepatocellular carcinoma. Radiology 2016 280 2 630 639 10.1148/radiol.2016150719 26885681
    [Google Scholar]
  102. Wu L. Li J. Liu T. Li S. Feng J. Yu Q. Zhang J. Chen J. Zhou Y. Ji J. Chen K. Mao Y. Wang F. Dai W. Fan X. Wu J. Guo C. Quercetin shows anti‐tumor effect in hepatocellular carcinoma LM3 cells by abrogating JAK2/STAT3 signaling pathway. Cancer Med. 2019 8 10 4806 4820 10.1002/cam4.2388 31273958
    [Google Scholar]
  103. Zhang S. Yang Y. Liang Z. Duan W. Yang J. Yan J. Wang N. Feng W. Ding M. Nie Y. Jin Z. Silybin-mediated inhibition of Notch signaling exerts antitumor activity in human hepatocellular carcinoma cells. PLoS One 2013 8 12 e83699 10.1371/journal.pone.0083699 24386256
    [Google Scholar]
  104. Thomas C.E. Luu H.N. Wang R. Haduch A.J. Jin A. Koh W.P. Yuan J.M. Association between dietary tomato intake and the risk of hepatocellular carcinoma: The singapore chinese health study. Cancer Epidemiol. Biomarkers Prev. 2020 29 7 1430 1435 10.1158/1055‑9965.EPI‑20‑0051 32284341
    [Google Scholar]
  105. Saraswati S. Alhaider A. Abdelgadir A.M. Tanwer P. Korashy H.M. Phloretin attenuates STAT-3 activity and overcomes sorafenib resistance targeting SHP-1–mediated inhibition of STAT3 and Akt/VEGFR2 pathway in hepatocellular carcinoma. Cell Commun. Signal. 2019 17 1 127 10.1186/s12964‑019‑0430‑7 31619257
    [Google Scholar]
  106. Zhang Q. Huang H. Zheng F. Liu H. Qiu F. Chen Y. Liang C.L. Dai Z. Resveratrol exerts antitumor effects by downregulating CD8 + CD122 + Tregs in murine hepatocellular carcinoma. OncoImmunology 2020 9 1 1829346 10.1080/2162402X.2020.1829346 33150044
    [Google Scholar]
  107. Rawat D. Shrivastava S. Naik R.A. Chhonker S.K. Mehrotra A. Koiri R.K. An overview of natural plant products in the treatment of hepatocellular carcinoma. Anticancer. Agents Med. Chem. 2019 18 13 1838 1859 10.2174/1871520618666180604085612 29866017
    [Google Scholar]
  108. Zhao Z. Malhotra A. Seng W.Y. Curcumin modulates hepatocellular carcinoma by reducing UNC119 expression. J. Environ. Pathol. Toxicol. Oncol. 2019 38 3 195 203 10.1615/JEnvironPatholToxicolOncol.2019029549 31679307
    [Google Scholar]
  109. Zhou R. Wang X.W. Sun Q. Ye Z.J. Liu J. Zhou D.H. Tang Y. Anticancer effects of emodin on HepG2 cell: Evidence from bioinformatic analysis. BioMed Res. Int. 2019 2019 1 14 10.1155/2019/3065818 31236404
    [Google Scholar]
  110. Sengupta A. Ghosh S. Bhattacharjee S. Allium vegetables in cancer prevention: An overview. Asian Pac. J. Cancer Prev. 2004 5 3 237 245 15373701
    [Google Scholar]
  111. Ng K.T.P. Guo D.Y. Cheng Q. Geng W. Ling C.C. Li C.X. Liu X.B. Ma Y.Y. Lo C.M. Poon R.T.P. Fan S.T. Man K. A garlic derivative, S-allylcysteine (SAC), suppresses proliferation and metastasis of hepatocellular carcinoma. PLoS One 2012 7 2 e31655 10.1371/journal.pone.0031655 22389672
    [Google Scholar]
  112. Maru G.B. Hudlikar R.R. Kumar G. Gandhi K. Mahimkar M.B. Understanding the molecular mechanisms of cancer prevention by dietary phytochemicals: From experimental models to clinical trials. World J. Biol. Chem. 2016 7 1 88 99 10.4331/wjbc.v7.i1.88 26981198
    [Google Scholar]
/content/journals/cpd/10.2174/0113816128364693250117060342
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Leveraging AI and Natural Compounds: Innovative Approaches in the Diagnosis and Treatment of Hepatocellular Carcinoma
Bentham Science Publishers ; https://doi.org/10.2174/0113816128364693250117060342
/content/journals/cpd/10.2174/0113816128364693250117060342
/content/journals/cpd/10.2174/0113816128364693250117060342
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  • Article Type:     Review Article
Keywords: phytochemicals ; artificial intelligence (AI) ; hepatocellular carcinoma (HCC) ; liver cancer ; Natural compounds

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