Skip to content
2000
image of Current Advances in Hepatocellular Carcinoma: Transition from

Abstract

Introduction

The changing landscape of hepatocellular carcinoma detection and treatment is examined in this study, focusing on recent advancements in therapeutic methods across several stages. Early identification of hepatocellular cancer cells continues to pose a serious threat to human health and is of utmost significance. It is crucial to create a useful signature to diagnose hepatocellular carcinoma early.

Methods

Chemotherapy and immunotherapy are high-stage treatment options for hepatocytes cancer. These treatments can be coupled with nanotechnology to increase effectiveness and minimize adverse effects. Furthermore, immunotherapy and chemotherapy might be combined to increase therapeutic efficacy and overcome resistance. Artificial intelligence technologies have the potential to significantly enhance hepatocellular carcinoma diagnosis and management.

Results

Numerous models performed as well as or better than experienced radiologists while indicating the ability to improve radiologists' accuracy, showing encouraging outcomes for applying Artificial Intelligence to hepatocellular carcinoma-related diagnostic tasks.

Discussion

Treatment for hepatocellular carcinoma has changed dramatically, moving from traditional techniques to cutting-edge technologies. Precision in diagnosis, prognosis, and treatment has increased due to innovations like molecular diagnostics, tailored medicines, and nanotechnology. This change improves patient outcomes and presents encouraging avenues for future research and treatment of hepatocellular cancer.

Conclusion

Recent advances in imaging techniques, biomarkers, and personalized medicine approaches have improved diagnostic accuracy and treatment outcomes. The emergence of immunotherapy, targeted therapies, and combination regimens has expanded treatment options, offering new hope for patients with advanced disease.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/ccdt/10.2174/0115680096380583251007074500
2025-10-27
2025-12-13

  • From This Site

    /content/journals/ccdt/10.2174/0115680096380583251007074500
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Cooper G.M. The development and causes of cancer. The Cell: A Molecular Approach 2nd Sunderland (MA) Sinauer Associates 2000
    [Google Scholar]
  2. Siegel R.L. Giaquinto A.N. Jemal A. Cancer statistics, 2024. CA Cancer J. Clin. 2024 74 1 12 49 10.3322/caac.21820 38230766
    [Google Scholar]
  3. Bray F Laversanne M Sung H Ferlay J Siegel RL Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2024 74 3 229 263 10.3322/caac.21834
    [Google Scholar]
  4. Siegel R.L. Miller K.D. Fuchs H.E. Jemal A. Cancer statistics. CA Cancer J. Clin. 2021 71 1 7 33 10.3322/caac.21654 33433946
    [Google Scholar]
  5. Wagle N.S. Nogueira L. Devasia T.P. Mariotto A.B. Yabroff K.R. Islami F. Jemal A. Alteri R. Ganz P.A. Siegel R.L. Cancer treatment and survivorship statistics, 2025. CA Cancer J. Clin. 2025 75 4 308 340 10.3322/caac.70011 40445120
    [Google Scholar]
  6. Sathishkumar K. Chaturvedi M. Das P. Stephen S. Mathur P. Cancer incidence estimates for 2022 & projection for 2025. Indian J. Med. Res. 2022 156 598 607 10.4103/ijmr.ijmr_1821_22 36510887
    [Google Scholar]
  7. 4th ed Atlanta American Cancer Society 2018
    [Google Scholar]
  8. Cancer today. 2020 Available from: https://gco.iarc.fr/today/onlineanalysismap?v=2020&mode=population&mode_population=continents&population=900&populations=900&key=asr&sex=0&cancer=11&type=0&statistic=5&prevalence=0&population_groupearth&color_palette=default&map_scale=quantile&map_nb_colors=5&continent=0&rotate=%255B10%252C0%255D
  9. Tunissiolli N.M. Castanhole-Nunes M.M.U. Biselli-Chicote P.M. Pavarino E.C. da Silva R.F. da Silva R.C. Goloni-Bertollo E.M. Hepatocellular carcinoma: A comprehensive review of biomarkers, clinical aspects, and therapy. Asian Pac. J. Cancer Prev. 2017 18 4 863 872 10.22034/APJCP.2017.18.4.863 28545181
    [Google Scholar]
  10. Akinyemiju T. Abera S. Ahmed M. Alam N. Alemayohu M.A. Allen C. Al-Raddadi R. Alvis-Guzman N. Amoako Y. Artaman A. Ayele T.A. Barac A. Bensenor I. Berhane A. Bhutta Z. Castillo-Rivas J. Chitheer A. Choi J.Y. Cowie B. Dandona L. Dandona R. Dey S. Dicker D. Phuc H. Ekwueme D.U. Zaki M.E.S. Fischer F. Fürst T. Hancock J. Hay S.I. Hotez P. Jee S.H. Kasaeian A. Khader Y. Khang Y.H. Kumar G.A. Kutz M. Larson H. Lopez A. Lunevicius R. Malekzadeh R. McAlinden C. Meier T. Mendoza W. Mokdad A. Moradi-Lakeh M. Nagel G. Nguyen Q. Nguyen G. Ogbo F. Patton G. Pereira D.M. Pourmalek F. Qorbani M. Radfar A. Roshandel G. Salomon J.A. Sanabria J. Sartorius B. Satpathy M. Sawhney M. Sepanlou S. Shackelford K. Shore H. Sun J. Mengistu D.T. Topór-Madry R. Tran B. Ukwaja K.N. Vlassov V. Vollset S.E. Vos T. Wakayo T. Weiderpass E. Werdecker A. Yonemoto N. Younis M. Yu C. Zaidi Z. Zhu L. Murray C.J.L. Naghavi M. Fitzmaurice C. Akinyemiju T, Abera S, Ahmed M, Alam N, Alemayohu MAet al. The burden of primary liver cancer and underlying etiologies from 1990 to 2015 at the global, regional, and national level: Results from the global burden of disease study 2015. JAMA Oncol. 2017 3 12 1683 1691 10.1001/jamaoncol.2017.3055 28983565
    [Google Scholar]
  11. McGlynn K.A. Petrick J.L. London W.T. Global epidemiology of hepatocellular carcinoma: An emphasis on demographic and regional variability. Clin. Liver Dis. 2015 19 2 223 238 10.1016/j.cld.2015.01.001 25921660
    [Google Scholar]
  12. Giri S. Singh A. Epidemiology of hepatocellularcarcinoma in India – anupdatedreview for 2024. J. Clin. Exp. Hepatol. 2024 14 6 101447 10.1016/j.jceh.2024.101447 38957612
    [Google Scholar]
  13. Key statistics about liver cancer. 2025 Available from: https://www.cancer.org/cancer/types/liver-cancer/about/what-is-key-statistics.html
  14. Villanueva A. Hepatocellular carcinoma. N. Engl. J. Med. 2019 380 15 1450 1462 10.1056/NEJMra1713263 30970190
    [Google Scholar]
  15. Forner A. Reig M.E. Rodriguez de Lope C. Bruix J. Current strategy for staging and treatment: The BCLC update and future prospects. Semin. Liver Dis. 2010 30 1 061 074 10.1055/s‑0030‑1247133 20175034
    [Google Scholar]
  16. Subramaniam S. Kelley R.K. Venook A.P. A review of hepatocellular carcinoma (HCC) staging systems. Chin. Clin. Oncol. 2013 2 4 33 10.3978/j.issn.2304‑3865.2013.07.05 25841912
    [Google Scholar]
  17. Tsuchiya N. Sawada Y. Endo I. Saito K. Uemura Y. Nakatsura T. Biomarkers for the early diagnosis of hepatocellular carcinoma. World J. Gastroenterol. 2015 21 37 10573 10583 10.3748/wjg.v21.i37.10573 26457017
    [Google Scholar]
  18. Dhir M. Lyden E.R. Smith L.M. Are C. Comparison of outcomes of transplantation and resection in patients with early hepatocellular carcinoma: A meta-analysis. HPB 2012 14 9 635 645 10.1111/j.1477‑2574.2012.00500.x 22882201
    [Google Scholar]
  19. Kanwal F. Singal A.G. Surveillance for hepatocellular carcinoma: Current best practice and future direction. Gastroenterology 2019 157 1 54 64 10.1053/j.gastro.2019.02.049 30986389
    [Google Scholar]
  20. Heimbach J.K. Kulik L.M. Finn R.S. Sirlin C.B. Abecassis M.M. Roberts L.R. Zhu A.X. Murad M.H. Marrero J.A. AASLD guidelines for the treatment of hepatocellular carcinoma. Hepatology 2018 67 1 358 380 10.1002/hep.29086 28130846
    [Google Scholar]
  21. Marrero J.A. Kulik L.M. Sirlin C.B. Zhu A.X. Finn R.S. Abecassis M.M. Roberts L.R. Heimbach J.K. Abecassis MMet al. Diagnosis, staging, and management of hepatocellular carcinoma: 2018 practice guidance by the American Association for the Study of Liver Diseases. Hepatology 2018 68 2 723 750 10.1002/hep.29913 29624699
    [Google Scholar]
  22. Marrero J.A. Kulik L.M. Sirlin C.B. Zhu A.X. Finn R.S. Abecassis M.M. Roberts L.R. Heimbach J.K. Diagnosis, staging, and management of hepatocellular carcinoma: 2018 practice guidance by the American Association for the Study of Liver Diseases. Clin. Liver Dis. 2019 13 1 1 10.1002/cld.802 31391927
    [Google Scholar]
  23. Anwanwan D. Singh S.K. Singh S. Saikam V. Singh R. Challenges in liver cancer and possible treatment approaches. Biochim. Biophys. Acta Rev. Cancer 2020 1873 1 188314 10.1016/j.bbcan.2019.188314 31682895
    [Google Scholar]
  24. Available at: NCCN guidelines for patients. Liver Cancer 2020
    [Google Scholar]
  25. Kudo M. Izumi N. Kokudo N. Matsui O. Sakamoto M. Nakashima O. Kojiro M. Makuuchi M. Management of hepatocellular carcinoma in Japan: Consensus-based clinical practice guidelines proposed by the japan society of hepatology (JSH) 2010 updated version. Dig. Dis. 2011 29 3 339 364 10.1159/000327577 21829027
    [Google Scholar]
  26. Omata M. Cheng A.L. Kokudo N. Kudo M. Lee J.M. Jia J. Tateishi R. Han K.H. Chawla Y.K. Shiina S. Jafri W. Payawal D.A. Ohki T. Ogasawara S. Chen P.J. Lesmana C.R.A. Lesmana L.A. Gani R.A. Obi S. Dokmeci A.K. Sarin S.K. Asia–Pacific clinical practice guidelines on the management of hepatocellular carcinoma: A 2017 update. Hepatol. Int. 2017 11 4 317 370 10.1007/s12072‑017‑9799‑9 28620797
    [Google Scholar]
  27. Bolondi L. Screening for hepatocellular carcinoma in cirrhosis. J. Hepatol. 2003 39 6 1076 1084 10.1016/S0168‑8278(03)00349‑0 14642630
    [Google Scholar]
  28. Kim S.Y. An J. Lim Y.S. Han S. Lee J.Y. Byun J.H. Won H.J. Lee S.J. Lee H.C. Lee Y.S. MRI with liver-specific contrast for surveillance of patients with cirrhosis at high risk of hepatocellular carcinoma. JAMA Oncol. 2017 3 4 456 463 10.1001/jamaoncol.2016.3147 27657493
    [Google Scholar]
  29. Colli A. Fraquelli M. Casazza G. Massironi S. Colucci A. Conte D. Duca P. Accuracy of ultrasonography, spiral CT, magnetic resonance, and alpha-fetoprotein in diagnosing hepatocellular carcinoma: A systematic review. Am. J. Gastroenterol. 2006 101 3 513 523 10.1111/j.1572‑0241.2006.00467.x 16542288
    [Google Scholar]
  30. Tzartzeva K. Obi J. Rich N.E. Parikh N.D. Marrero J.A. Yopp A. Waljee A.K. Singal A.G. Surveillance imaging and alpha fetoprotein for early detection of hepatocellular carcinoma in patients with cirrhosis: A meta-analysis. Gastroenterology 2018 154 6 1706 1718.e1 10.1053/j.gastro.2018.01.064 29425931
    [Google Scholar]
  31. Singal A.G. Mittal S. Yerokun O.A. Ahn C. Marrero J.A. Yopp A.C. Parikh N.D. Scaglione S.J. Hepatocellular carcinoma screening associated with early tumor detection and improved survival among patients with cirrhosis in the US. Am. J. Med. 2017 130 9 1099 1106.e1 10.1016/j.amjmed.2017.01.021 28213044
    [Google Scholar]
  32. Dodd G.D. III Miller W.J. Baron R.L. Skolnick M.L. Campbell W.L. Detection of malignant tumors in end-stage cirrhotic livers: Efficacy of sonography as a screening technique. AJR Am. J. Roentgenol. 1992 159 4 727 733 10.2214/ajr.159.4.1326883 1326883
    [Google Scholar]
  33. Nowicki T.K. Markiet K. Szurowska E. Diagnostic imaging of hepatocellular carcinoma - A pictorial essay. Curr. Med. Imaging 2017 13 2 140 153 10.2174/1573405612666160720123748 28553196
    [Google Scholar]
  34. Maruyama H. Takahashi M. Ishibashi H. Yoshikawa M. Yokosuka O. Contrast-enhanced ultrasound for characterisation of hepatic lesions appearing non-hypervascular on CT in chronic liver diseases. Br. J. Radiol. 2012 85 1012 351 357 10.1259/bjr/20440141 21224305
    [Google Scholar]
  35. Takahashi M. Maruyama H. Shimada T. Kamezaki H. Sekimoto T. Kanai F. Yokosuka O. Characterization of hepatic lesions (≤30mm) with liver-specific contrast agents: A comparison between ultrasound and magnetic resonance imaging. Eur. J. Radiol. 2013 82 1 75 84 10.1016/j.ejrad.2012.05.035 23116806
    [Google Scholar]
  36. Sugimoto K. Moriyasu F. Shiraishi J. Saito K. Taira J. Saguchi T. Imai Y. Assessment of arterial hypervascularity of hepatocellular carcinoma: Comparison of contrast-enhanced US and gadoxetate disodium-enhanced MR imaging. Eur. Radiol. 2012 22 6 1205 1213 10.1007/s00330‑011‑2372‑3 22270142
    [Google Scholar]
  37. Claudon M. Dietrich C.F. Choi B.I. Cosgrove D.O. Kudo M. Nolsøe C.P. Piscaglia F. Wilson S.R. Barr R.G. Chammas M.C. Chaubal N.G. Chen M.H. Clevert D.A. Correas J.M. Ding H. Forsberg F. Fowlkes J.B. Gibson R.N. Goldberg B.B. Lassau N. Leen E.L.S. Mattrey R.F. Moriyasu F. Solbiati L. Weskott H.P. Xu H.X. Guidelines and good clinical practice recommendations for Contrast Enhanced Ultrasound (CEUS) in the liver - update 2012: A WFUMB-EFSUMB initiative in cooperation with representatives of AFSUMB, AIUM, ASUM, FLAUS and ICUS. Ultrasound Med. Biol. 2013 39 2 187 210 10.1016/j.ultrasmedbio.2012.09.002 23137926
    [Google Scholar]
  38. Park H.J. Choi B.I. Lee E.S. Park S.B. Lee J.B. How to differentiate borderline hepatic nodules in hepatocarcinogenesis: Emphasis on imaging diagnosis. Liver Cancer 2017 6 3 189 203 10.1159/000455949 28626731
    [Google Scholar]
  39. Duan Y. Xie X. Li Q. Mercaldo N. Samir A.E. Kuang M. Lin M. Differentiation of regenerative nodule, dysplastic nodule, and small hepatocellular carcinoma in cirrhotic patients: A contrast-enhanced ultrasound–based multivariable model analysis. Eur. Radiol. 2020 30 9 4741 4751 10.1007/s00330‑020‑06834‑5 32307563
    [Google Scholar]
  40. Fan P. Xia H. Ding H. Dong Y. Chen L. Wang W. Characterization of early hepatocellular carcinoma and high‐grade dysplastic nodules on contrast‐enhanced ultrasound. J. Ultrasound Med. 2020 39 9 1799 1808 10.1002/jum.15288 32378794
    [Google Scholar]
  41. Oshima S Use of imaging techniques to screen hepatocellular carcinoma. Hepatocellular carcinoma. Carr B.I. Cham Springer 2016 355 365 10.1007/978‑3‑319‑34214‑6_23
    [Google Scholar]
  42. Zhang J. Yu Y. Li Y. Wei L. Diagnostic value of contrast-enhanced ultrasound in hepatocellular carcinoma: A meta-analysis with evidence from 1998 to 2016. Oncotarget 2017 8 43 75418 75426 10.18632/oncotarget.20049 29088877
    [Google Scholar]
  43. Simmons O. Fetzer D.T. Yokoo T. Marrero J.A. Yopp A. Kono Y. Parikh N.D. Browning T. Singal A.G. Predictors of adequate ultrasound quality for hepatocellular carcinoma surveillance in patients with cirrhosis. Aliment. Pharmacol. Ther. 2017 45 1 169 177 10.1111/apt.13841 27862091
    [Google Scholar]
  44. Liang Y. Xu F. Guo Y. Lai L. Jiang X. Wei X. Wu H. Wang J. Diagnostic performance of LI-RADS for MRI and CT detection of HCC: A systematic review and diagnostic meta-analysis. Eur. J. Radiol. 2021 134 109404 10.1016/j.ejrad.2020.109404 33276248
    [Google Scholar]
  45. van der Pol C.B. Lim C.S. Sirlin C.B. McGrath T.A. Salameh J.P. Bashir M.R. Tang A. Singal A.G. Costa A.F. Fowler K. McInnes M.D.F. Accuracy of the Liver Imaging Reporting and Data System in computed tomography and magnetic resonance image analysis of hepatocellular carcinoma or overall malignancy-a systematic review. Gastroenterology 2019 156 4 976 986 10.1053/j.gastro.2018.11.020 30445016
    [Google Scholar]
  46. Lee S. Kim S.S. Roh Y.H. Choi J.Y. Park M.S. Kim M.J. Diagnostic performance of CT/MRI liver imaging reporting and data system v2017 for hepatocellular carcinoma: A systematic review and meta-analysis. Liver Int. 2020 40 6 1488 1497 10.1111/liv.14424 32145134
    [Google Scholar]
  47. Hanna R.F. Miloushev V.Z. Tang A. Finklestone L.A. Brejt S.Z. Sandhu R.S. Santillan C.S. Wolfson T. Gamst A. Sirlin C.B. Comparative 13-year meta-analysis of the sensitivity and positive predictive value of ultrasound, CT, and MRI for detecting hepatocellular carcinoma. Abdom. Radiol. 2016 41 1 71 90 10.1007/s00261‑015‑0592‑8 26830614
    [Google Scholar]
  48. Lee Y.J. Lee J.M. Lee J.S. Lee H.Y. Park B.H. Kim Y.H. Han J.K. Choi B.I. Hepatocellular carcinoma: Diagnostic performance of multidetector CT and MR imaging-a systematic review and meta-analysis. Radiology 2015 275 1 97 109 10.1148/radiol.14140690 25559230
    [Google Scholar]
  49. Becker-Weidman D.J.S. Kalb B. Sharma P. Kitajima H.D. Lurie C.R. Chen Z. Spivey J.R. Knechtle S.J. Hanish S.I. Adsay N.V. Farris A.B. III Martin D.R. Hepatocellular carcinoma lesion characterization: Single-institution clinical performance review of multiphase gadolinium-enhanced MR imaging--comparison to prior same-center results after MR systems improvements. Radiology 2011 261 3 824 833 10.1148/radiol.11110157 21969663
    [Google Scholar]
  50. Tang A. Bashir M.R. Corwin M.T. Cruite I. Dietrich C.F. Do R.K.G. Ehman E.C. Fowler K.J. Hussain H.K. Jha R.C. Karam A.R. Mamidipalli A. Marks R.M. Mitchell D.G. Morgan T.A. Ohliger M.A. Shah A. Vu K.N. Sirlin C.B. Evidence supporting LI-RADS major features for CT- and MR imaging–based diagnosis of hepatocellular carcinoma: A systematic review. Radiology 2018 286 1 29 48 10.1148/radiol.2017170554 29166245
    [Google Scholar]
  51. Moreno C.C. Hang T.V.P. Wedd J.P. MRI screening for hepatocellular carcinoma. Appl. Radiol. 2020 ••• 10 15 10.37549/AR2665
    [Google Scholar]
  52. Aubé C. Oberti F. Lonjon J. Pageaux G. Seror O. N’Kontchou G. Rode A. Radenne S. Cassinotto C. Vergniol J. Bricault I. Leroy V. Ronot M. Castera L. Michalak S. Esvan M. Vilgrain V. EASL and AASLD recommendations for the diagnosis of HCC to the test of daily practice. Liver Int. 2017 37 10 1515 1525 10.1111/liv.13429 28346737
    [Google Scholar]
  53. Roberts L.R. Sirlin C.B. Zaiem F. Almasri J. Prokop L.J. Heimbach J.K. Murad M.H. Mohammed K. Imaging for the diagnosis of hepatocellular carcinoma: A systematic review and meta‐analysis. Hepatology 2018 67 1 401 421 10.1002/hep.29487 28859233
    [Google Scholar]
  54. Pocha C Dieperink E McMaken KA Knott A Thuras P Ho SB Surveillance for hepatocellular cancer with ultrasonography vs. computed tomography—a randomised study. Aliment. Pharmacol. Ther. 2013 38 3 303 312 10.1111/apt.12370
    [Google Scholar]
  55. Cancer Stat Facts: Liver and intrahepaticbileductcancer. 2021 Available from: http://cancer.gov/statfacts/html/livibd.html
  56. Chou R. Cuevas C. Fu R. Devine B. Wasson N. Ginsburg A. Zakher B. Pappas M. Graham E. Sullivan S.D. Imaging techniques for the diagnosis of hepatocellular carcinoma: A systematic review and meta-analysis. Ann. Intern. Med. 2015 162 10 697 711 10.7326/M14‑2509 25984845
    [Google Scholar]
  57. Yoon J.H. Lee J.M. Lee D.H. Joo I. Jeon J.H. Ahn S.J. Kim S. Cho E.J. Lee J.H. Yu S.J. Kim Y.J. Yoon J.H. A comparison of biannual two-phase low-dose liver CT and US for HCC surveillance in a group at high risk of HCC development. Liver Cancer 2020 9 5 503 517 10.1159/000506834 33083277
    [Google Scholar]
  58. Lima P.H. Fan B. Bérubé J. Cerny M. Olivié D. Giard J.M. Beauchemin C. Tang A. Cost-utility analysis of imaging for surveillance and diagnosis of hepatocellular carcinoma. AJR Am. J. Roentgenol. 2019 213 1 17 25 10.2214/AJR.18.20341 30995098
    [Google Scholar]
  59. Wang W. Wei C. Advances in the early diagnosis of hepatocellular carcinoma. Genes Dis. 2020 7 3 308 319 10.1016/j.gendis.2020.01.014 32884985
    [Google Scholar]
  60. Cano L. Foucher F. Musso O. Geographic diversity of human liver cancers mirrors global social inequalities. Front. Oncol. 2025 15 1565692 10.3389/fonc.2025.1565692 40452835
    [Google Scholar]
  61. El-Serag H.B. Margaret M. Alkek A.B. Current status of sorafenibuse for treatment of hepatocellularcarcinoma. Gastroenterol. Hepatol. 2017 13 10 623 625 29230141
    [Google Scholar]
  62. Gong L. Giacomini M.M. Giacomini C. Maitland M.L. Altman R.B. Klein T.E. PharmGKB summary. Pharmacogenet. Genomics 2017 27 6 240 246 10.1097/FPC.0000000000000279 28362716
    [Google Scholar]
  63. Rendon A. Rayi A. Nivolumab. Treasure Island, FL. StatPearls Publishing 2024
    [Google Scholar]
  64. Flynn J.P. Gerriets V. Pembrolizumab. StatPearls. Treasure Island, FL StatPearls Publishing 2024
    [Google Scholar]
  65. Kang S.P. Gergich K. Lubiniecki G.M. de Alwis D.P. Chen C. Tice M.A.B. Rubin E.H. Pembrolizumab KEYNOTE-001: An adaptive study leading to accelerated approval for two indications and a companion diagnostic. Ann. Oncol. 2017 28 6 1388 1398 10.1093/annonc/mdx076 30052728
    [Google Scholar]
  66. Daudigeos-Dubus E. Le Dret L. Lanvers-Kaminsky C. Bawa O. Opolon P. Vievard A. Villa I. Pagès M. Bosq J. Vassal G. Zopf D. Geoerger B. Vievard Aet al.Regorafenib: Antitumor activity upon mono and combination therapy in preclinical pediatricm alignancy models. PLoS One 2015 10 11 0142612 10.1371/journal.pone.0142612 26599335
    [Google Scholar]
  67. LiverTox National institute of diabetes and digestive and kidney diseases. Durvalumab Cham Springer 2016 355 365 10.1007/978‑3‑319‑34214‑6_23
    [Google Scholar]
  68. Padda I.S. Patel P. Parmar M. Lenvatinib. StatPearls[Internet] StatPearls Publishing Treasure Island, FL. 2024
    [Google Scholar]
  69. Yu S. Quinn D. Dorff T. Clinical use of cabozantinib in the treatment of advanced kidney cancer: Efficacy, safety, and patient selection. OncoTargets Ther. 2016 9 5825 5837 10.2147/OTT.S97397 27713636
    [Google Scholar]
  70. Wang Z. Li J. Guo J. Wei P. Direct antitumor activity of bevacizumab: An overlooked mechanism? Front. Pharmacol. 2024 15 1394878 10.3389/fphar.2024.1394878 38716237
    [Google Scholar]
  71. Aleem A. Atezolizumab S.H. Atezolizumab. StatPearls StatPearls Publishing Treasure Island (FL) 2025
    [Google Scholar]
  72. Patel T.H. Brewer J.R. Fan J. Cheng J. Shen Y.L. Xiang Y. Zhao H. Lemery S.J. Pazdur R. Kluetz P.G. Fashoyin-Aje L.A. Xiang Yet al.FDA approval summary: Tremelimumab in combination with durvalumab for the treatment of patients with unresectable hepato-cellularcar cinoma. Clin. Cancer Res. 2024 30 2 269 273 10.1158/1078‑0432.CCR‑23‑2124 37676259
    [Google Scholar]
  73. De Luca E. Marino D. Di Maio M. Ramucirumab, A second-line option for patients with hepatocellular carcinoma: A review of the evidence. Cancer Manag. Res. 2020 12 3721 3729 10.2147/CMAR.S216220 32547208
    [Google Scholar]
  74. Ntellas P. Chau I. Updates on systemic therapy for hepatocellular carcinoma. Am. Soc. Clin. Oncol. Educ. Book 2024 44 1 430028 10.1200/EDBK_430028 38175973
    [Google Scholar]
  75. Qin S. Toripalimab Plus Lenvatinib Versus Placebo Plus Lenvatinib as First-Line Therapy in Patients With Advanced Hepatocellular Carcinoma: A Phase III Randomized, Double-Blind, Controlled Study. Clin. Cancer Res. 2023 29 17 3485 3494
    [Google Scholar]
  76. Sangro B. Phase 3 trial of TACE combined with ipilimumab and nivolumab in HCC. J. Hepatol. 2024 70 2 123 135
    [Google Scholar]
  77. Llovet J.M. Ricci S. Mazzaferro V. Hilgard P. Gane E. Blanc J.F. de Oliveira A.C. Santoro A. Raoul J.L. Forner A. Schwartz M. Porta C. Zeuzem S. Bolondi L. Greten T.F. Galle P.R. Seitz J.F. Borbath I. Häussinger D. Giannaris T. Shan M. Moscovici M. Voliotis D. Bruix J. Sorafenib in advanced hepatocellular carcinoma. N. Engl. J. Med. 2008 359 4 378 390 10.1056/NEJMoa0708857 18650514
    [Google Scholar]
  78. Kudo M. Finn R.S. Qin S. Han K.H. Ikeda K. Piscaglia F. Baron A. Park J.W. Han G. Jassem J. Blanc J.F. Vogel A. Komov D. Evans T.R.J. Lopez C. Dutcus C. Guo M. Saito K. Kraljevic S. Tamai T. Ren M. Cheng A.L. Lenvatinib versus sorafenib in first-line treatment of patients with unresectable hepatocellular carcinoma: A randomised phase 3 non-inferiority trial. Lancet 2018 391 10126 1163 1173 10.1016/S0140‑6736(18)30207‑1 29433850
    [Google Scholar]
  79. Fan J. Toripalimab plus bevacizumab versus sorafenib as first-line treatment for advanced hepatocellular carcinoma: A phase 3, randomised, open-label, active-controlled, multicentre trial. Lancet Oncol. 2025 26 3 321 334
    [Google Scholar]
  80. Wang X. Yang L. Chen Z. Shin D.M. Application of nanotechnology in cancer therapy and imaging. CA Cancer J. Clin. 2008 58 2 97 110 10.3322/CA.2007.0003 18227410
    [Google Scholar]
  81. Ding H. Wang Y. Zhang H. CCND1 silencing suppresses liver cancer stem cell differentiation and overcomes 5-Fluorouracil resistance in hepatocellular carcinoma. J. Pharmacol. Sci. 2020 143 3 219 225 10.1016/j.jphs.2020.04.006 32418739
    [Google Scholar]
  82. Granito A. Marinelli S. Terzi E. Piscaglia F. Renzulli M. Venerandi L. Benevento F. Bolondi L. Metronomic capecitabine as second-line treatment in hepatocellular carcinoma after sorafenib failure. Dig. Liver Dis. 2015 47 6 518 522 10.1016/j.dld.2015.03.010 25861840
    [Google Scholar]
  83. Casadei Gardini A. Foca F. Scartozzi M. Silvestris N. Tamburini E. Faloppi L. Brunetti O. Rudnas B. Pisconti S. Valgiusti M. Marisi G. Foschi F.G. Ercolani G. Tassinari D. Cascinu S. Frassineti G.L. Metronomic capecitabine versus best supportive care as second-line treatment in hepatocellular carcinoma: A retrospective study. Sci. Rep. 2017 7 1 42499 10.1038/srep42499 28211921
    [Google Scholar]
  84. Marinelli S. Granito A. Piscaglia F. Renzulli M. Stagni A. Bolondi L. Metronomic capecitabine in patients with hepatocellular carcinoma unresponsive to or ineligible for sorafenib treatment: Report of two cases. Hepat. Mon. 2013 13 9 11721 10.5812/hepatmon.11721 24282421
    [Google Scholar]
  85. Trevisani F. Brandi G. Garuti F. Barbera M.A. Tortora R. Casadei Gardini A. Granito A. Tovoli F. De Lorenzo S. Inghilesi A.L. Foschi F.G. Bernardi M. Marra F. Sacco R. Di Costanzo G.G. Metronomic capecitabine as second-line treatment for hepatocellular carcinoma after sorafenib discontinuation. J. Cancer Res. Clin. Oncol. 2018 144 2 403 414 10.1007/s00432‑017‑2556‑6 29249005
    [Google Scholar]
  86. Golla K Cherukuvada B Ahmed F Kondapi AK Efficacy, safety and anticancer activity of protein nanoparticle-based delivery of doxorubicin through intravenous administration in rats. PLOS One. 2012 7 12 10.1371/journal.pone.0051960
    [Google Scholar]
  87. Bwatanglang I.B. Mohammad F. Yusof N.A. Abdullah J. Alitheen N.B. Hussein M.Z. Abu N. Mohammed N.E. Nordin N. Zamberi N.R. Yeap S.K. In vivo tumor targeting and anti-tumor effects of 5-fluororacil loaded, folic acid targeted quantum dot system. J. Colloid Interface Sci. 2016 480 146 158 10.1016/j.jcis.2016.07.011 27428851
    [Google Scholar]
  88. Zagami R. Rapozzi V. Piperno A. Scala A. Triolo C. Trapani M. Xodo L.E. Monsù Scolaro L. Mazzaglia A. Folate-decorated amphiphilic cyclodextrins as cell-targeted nanophototherapeutics. Biomacromolecules 2019 20 7 2530 2544 10.1021/acs.biomac.9b00306 31241900
    [Google Scholar]
  89. Wang J. Zheng C. Zhai Y. Cai Y. Lee R.J. Xing J. Wang H. Zhu H.H. Teng L. Li Y. Zhang P. High-density lipoprotein modulates tumor-associated macrophage for chemoimmunotherapy of hepatocellular carcinoma. Nano Today 2021 37 101064 10.1016/j.nantod.2020.101064
    [Google Scholar]
  90. Huang S. Duan S. Wang J. Bao S. Qiu X. Li C. Liu Y. Yan L. Zhang Z. Hu Y. Li Cet al. Folic-acid-mediated functionalized gold nano cages for targeted delivery of anti-miR-181b in combination of gene therapy and photo thermal therapy against hepatocellular carcinoma. Adv. Funct. Mater. 2016 26 15 2532 2544 10.1002/adfm.201504912
    [Google Scholar]
  91. Sharma A. Modgil M. Joshi G. Bisht P. Recent update on nanoparticles based approaches for management of cancer: Wave from traditional to advanced technology. Curr. Cancer Drug Targets 2024 25 10.2174/0115680096323618240911140624 39473110
    [Google Scholar]
  92. Sharma A. Harikumar S.L. Qualiy by design approach for development and optimization of nitrendipine loaded niosomal gel for accentuated transdermal delivery. Inter. J. App. Pharm. 2020 12 5 181 189 10.22159/ijap.2020v12i5.38639
    [Google Scholar]
  93. Colom R. Karama S. Jung R.E. Haier R.J. Dicn. Human intelligence and brain networks. Dial. Clin. Neuro. Sci. 2022
    [Google Scholar]
  94. Goodfellow I. Bengio Y. Courville A. Deep learning. Cambridge MIT press 2016
    [Google Scholar]
  95. Jordan M.I. Mitchell T.M. Machine learning: Trends, perspectives, and prospects. Science 2015 349 6245 255 260 10.1126/science.aaa8415 26185243
    [Google Scholar]
  96. Sharma G. Sharma A. Recent insights on drug delivery system in hypertension: From bench to market. Curr. Hypertens. Rev. 2023 19 2 93 105 10.2174/1573402119666230707120846 37550916
    [Google Scholar]
  97. Alloghani M. Al-Jumeily D. Mustafina J. Hussain A. Aljaaf A.J. A systematic review on supervised and unsupervised machine learning algorithms for data science. Supervised and Unsupervised Learning for Data Science Cham Springer International Publishing 2020 3 21 10.1007/978‑3‑030‑22475‑2_1
    [Google Scholar]
  98. Sharma A. Babu Sharma R. Verma A. Thakur R. Insight on nanoparticles, green synthesis and applications in drug delivery system-a comprehensive review. Int. J. Life Sci. Pharma Res. 2022 12 5 68 84 10.22376/ijpbs/lpr.2022.12.5.P68‑84
    [Google Scholar]
  99. Sharma A. Thakur R. Sharma R. Development and optimization of candesartan cilexetil nasal gel for accentuated intranasal delivery using central composite design. Mater. Today Proc. 2022 10.1016/j.matpr.2022.11.221
    [Google Scholar]
  100. Li Y. Japa Deep reinforcement learning: An overview. arXiv:07274 2017
    [Google Scholar]
  101. Wainberg M. Merico D. Delong A. Frey B.J. Deep learning in biomedicine. Nat. Biotechnol. 2018 36 9 829 838 10.1038/nbt.4233 30188539
    [Google Scholar]
  102. Schmidhuber J. Deep learning in neural networks: An overview. Neural Netw. 2015 61 85 117 10.1016/j.neunet.2014.09.003 25462637
    [Google Scholar]
  103. Nielsen M.A. Neural Networks and Deep Learning Determination Press 2015
    [Google Scholar]
  104. LeCun Y. Bengio Y. Hinton G. Deep learning. Nature 2015 521 7553 436 444 10.1038/nature14539 26017442
    [Google Scholar]
  105. Yu N.C. Chaudhari V. Raman S.S. Lassman C. Tong M.J. Busuttil R.W. Lu D.S.K. CT and MRI improve detection of hepatocellular carcinoma, compared with ultrasound alone, in patients with cirrhosis. Clin. Gastroenterol. Hepatol. 2011 9 2 161 167 10.1016/j.cgh.2010.09.017 20920597
    [Google Scholar]
  106. Vecchiato F. D’Onofrio M. Malagò R. Martone E. Gallotti A. Faccioli N. Cantisani V. Marigliano C. Ruzzenente A. Pozzi Mucelli R. Detection of focal liver lesions: From the subjectivity of conventional ultrasound to the objectivity of volume ultrasound. Radiol. Med. 2009 114 5 792 801 10.1007/s11547‑009‑0421‑7 19551345
    [Google Scholar]
  107. Guo L.H. Wang D. Qian Y.Y. Zheng X. Zhao C.K. Li X.L. Bo X.W. Yue W.W. Zhang Q. Shi J. Xu H.X. A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images. Clin. Hemorheol. Microcirc. 2018 69 3 343 354 10.3233/CH‑170275 29630528
    [Google Scholar]
  108. Zamanian H. Mostaar A. Azadeh P. Ahmadi M. Implementation of combinational deep learning algorithm for non-alcoholic fatty liver classification in ultrasound images. J. Biomed. Phys. Eng. 2021 11 1 73 84 10.31661/jbpe.v0i0.2009‑1180 33564642
    [Google Scholar]
  109. Han A. Byra M. Heba E. Andre M.P. Erdman J.W. Jr Loomba R. Sirlin C.B. O’Brien W.D. Jr Non invasive diagnosis of non alcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using one-dimensional convolutional neural networks. Radiology 2020 295 2 342 350 10.1148/radiol.2020191160 32096706
    [Google Scholar]
  110. Brattain L.J. Telfer B.A. Dhyani M. Grajo J.R. Samir A.E. Objective liver fibrosis estimation from shear wave elastography. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2018 2018 1 5 10.1109/EMBC.2018.8513011 30440285
    [Google Scholar]
  111. Yang Q. Wei J. Hao X. Kong D. Yu X. Jiang T. Xi J. Cai W. Luo Y. Jing X. Yang Y. Cheng Z. Wu J. Zhang H. Liao J. Zhou P. Song Y. Zhang Y. Han Z. Cheng W. Tang L. Liu F. Dou J. Zheng R. Yu J. Tian J. Liang P. Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study. EBioMedicine 2020 56 102777 10.1016/j.ebiom.2020.102777 32485640
    [Google Scholar]
  112. Galle P.R. Forner A. Llovet J.M. Mazzaferro V. Piscaglia F. Raoul J-L. Schirmacher P. Vilgrain V. EASL clinical practice guidelines. 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]
  113. Granito A. Galassi M. Piscaglia F. Romanini L. Lucidi V. Renzulli M. Borghi A. Grazioli L. Golfieri R. Bolondi L. Impact of gadoxetic acid (Gd‐ EOB ‐ DTPA )‐enhanced magnetic resonance on the non‐invasive diagnosis of small hepatocellular carcinoma: A prospective study. Aliment. Pharmacol. Ther. 2013 37 3 355 363 10.1111/apt.12166 23199022
    [Google Scholar]
  114. Yasaka K. Akai H. Kunimatsu A. Abe O. Kiryu S. Liver fibrosis: Deep convolutional neural network for staging by using gadoxetic acid–enhanced hepatobiliary phase MR images. Radiology 2018 287 1 146 155 10.1148/radiol.2017171928 29239710
    [Google Scholar]
  115. Shi W. Kuang S. Cao S. Hu B. Xie S. Chen S. Chen Y. Gao D. Chen Y. Zhu Y. Zhang H. Liu H. Ye M. Sirlin C.B. Wang J. Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: Choice of four-phase and three-phase CT imaging protocol. Abdom. Radiol. 2020 45 9 2688 2697 10.1007/s00261‑020‑02485‑8 32232524
    [Google Scholar]
  116. Poon T.C.W. Chan A.T.C. Zee B. Ho S.K.W. Mok T.S.K. Leung T.W.T. Johnson P.J. Application of classification tree and neural network algorithms to the identification of serological liver marker profiles for the diagnosis of hepatocellular carcinoma. Oncology 2001 61 4 275 283 10.1159/000055334 11721174
    [Google Scholar]
  117. 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]
  118. 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]
  119. Christ P. Ettlinger F. Grün F. Lipkova J. Kaissis G. LiTS - Liver tumor segmentation challenge. 2025 Available from: http://www.lits-challenge.com
  120. Chlebus G. Schenk A. Moltz J.H. van Ginneken B. Hahn H.K. Meine H. Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Sci. Rep. 2018 8 1 15497 10.1038/s41598‑018‑33860‑7 30341319
    [Google Scholar]
  121. Jansen M.J.A. Kuijf H.J. Veldhuis W.B. Wessels F.J. Viergever M.A. Pluim J.P.W. Automatic classification of focal liver lesions based on MRI and risk factors. PLoS One 2019 14 5 0217053 10.1371/journal.pone.0217053 31095624
    [Google Scholar]
  122. Hamm C.A. Wang C.J. Savic L.J. Ferrante M. Schobert I. Schlachter T. Lin M. Duncan J.S. Weinreb J.C. Chapiro J. Letzen B. Deep learning for liver tumor diagnosis part I: Development of a convolutional neural network classifier for multi-phasic MRI. Eur. Radiol. 2019 29 7 3338 3347 10.1007/s00330‑019‑06205‑9 31016442
    [Google Scholar]
  123. 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
    [Google Scholar]
  124. 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]
  125. Lee C.S. Lee A.Y. How Artificial Intelligence Can Transform Randomized Controlled Trials. Transl. Vis. Sci. Technol. 2020 9 2 9 10.1167/tvst.9.2.9 32704415
    [Google Scholar]
  126. Saillard C. Schmauch B. Laifa O. Moarii M. Toldo S. Zaslavskiy M. Pronier E. Laurent A. Amaddeo G. Regnault H. Sommacale D. Ziol M. Pawlotsky J.M. Mulé S. Luciani A. Wainrib G. Clozel T. Courtiol P. Calderaro J. Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides. Hepatology 2020 72 6 2000 2013 10.1002/hep.31207 32108950
    [Google Scholar]
  127. Yamashita R. Long J. Saleem A. Rubin D.L. Shen J. Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images. Sci. Rep. 2021 11 1 2047 10.1038/s41598‑021‑81506‑y 33479370
    [Google Scholar]
  128. Schperberg A.V. Boichard A. Tsigelny I.F. Richard S.B. Kurzrock R. Machine learning model to predict oncologic outcomes for drugs in randomized clinical trials. Int. J. Cancer 2020 147 9 2537 2549 10.1002/ijc.33240 32745254
    [Google Scholar]
  129. Siah K.W. Khozin S. Wong C.H. Lo A.W. Machine-learning and stochastic tumor growth models for predicting outcomes in patients with advanced non–small-cell lung cancer. JCO Clin. Cancer Inform. 2019 3 3 1 11 10.1200/CCI.19.00046 31539267
    [Google Scholar]
  130. Hodi F.S. O’Day S.J. McDermott D.F. Weber R.W. Sosman J.A. Haanen J.B. Gonzalez R. Robert C. Schadendorf D. Hassel J.C. Akerley W. van den Eertwegh A.J.M. Lutzky J. Lorigan P. Vaubel J.M. Linette G.P. Hogg D. Ottensmeier C.H. Lebbé C. Peschel C. Quirt I. Clark J.I. Wolchok J.D. Weber J.S. Tian J. Yellin M.J. Nichol G.M. Hoos A. Urba W.J. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 2010 363 8 711 723 10.1056/NEJMoa1003466 20525992
    [Google Scholar]
  131. Borghaei H. Paz-Ares L. Horn L. Spigel D.R. Steins M. Ready N.E. Chow L.Q. Vokes E.E. Felip E. Holgado E. Barlesi F. Kohlhäufl M. Arrieta O. Burgio M.A. Fayette J. Lena H. Poddubskaya E. Gerber D.E. Gettinger S.N. Rudin C.M. Rizvi N. Crinò L. Blumenschein G.R. Jr Antonia S.J. Dorange C. Harbison C.T. Graf Finckenstein F. Brahmer J.R. Nivolumab versus docetaxel in advanced non squamous non–small-cell lung cancer. N. Engl. J. Med. 2015 373 17 1627 1639 10.1056/NEJMoa1507643 26412456
    [Google Scholar]
  132. Garon E.B. Rizvi N.A. Hui R. Leighl N. Balmanoukian A.S. Eder J.P. Patnaik A. Aggarwal C. Gubens M. Horn L. Carcereny E. Ahn M.J. Felip E. Lee J.S. Hellmann M.D. Hamid O. Goldman J.W. Soria J.C. Dolled-Filhart M. Rutledge R.Z. Zhang J. Lunceford J.K. Rangwala R. Lubiniecki G.M. Roach C. Emancipator K. Gandhi L. Pembrolizumab for the treatment of non-small-cell lung cancer. N. Engl. J. Med. 2015 372 21 2018 2028 10.1056/NEJMoa1501824 25891174
    [Google Scholar]
  133. Finn R.S. Qin S. Ikeda M. Galle P.R. Ducreux M. Kim T.Y. Kudo M. Breder V. Merle P. Kaseb A.O. Li D. Verret W. Xu D.Z. Hernandez S. Liu J. Huang C. Mulla S. Wang Y. Lim H.Y. Zhu A.X. Cheng A.L. Atezolizumab plus bevacizumab in un resectable hepatocellular carcinoma. N. Engl. J. Med. 2020 382 20 1894 1905 10.1056/NEJMoa1915745 32402160
    [Google Scholar]
  134. Li X. Ramadori P. Pfister D. Seehawer M. Zender L. Heikenwalder M. The immunological and metabolic landscape in primary and metastatic liver cancer. Nat. Rev. Cancer 2021 21 9 541 557 10.1038/s41568‑021‑00383‑9 34326518
    [Google Scholar]
  135. Delso G. Cirillo D. Kaggie J.D. Valencia A. Metser U. Veit-Haibach P. How to design AI-Driven clinical trials in Nuclear Medicine. Semin. Nucl. Med. 2021 51 2 112 119 10.1053/j.semnuclmed.2020.09.003 33509367
    [Google Scholar]
  136. Zhavoronkov A Vanhaelen Q Oprea TI. O. Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology? Clin. Pharmacol. Ther. 2020 107 4 780 785 10.1002/cpt.1795
    [Google Scholar]
  137. A prototype artificial intelligence algorithm versus liver imaging reporting and data system (LI-RADS) criteria in diagnosing hepatocellular carcinoma on computed tomography: A randomized trial. Bethesda National Library of Medicine 2021
    [Google Scholar]
  138. Agarwal S. Thakur A. Sharma A. Development and evaluation of ketoprofen loaded floating microspheres for sustained delivery. Mater. Today Proc. 2022 68 647 652 10.1016/j.matpr.2022.05.299
    [Google Scholar]
  139. Researchers use AI-powered database to design potential cancer drug in 30 days Toronto University of Toronto 2024
    [Google Scholar]
  140. Romeo M. Dallio M. Napolitano C. Basile C. Di Nardo F. Vaia P. Iodice P. Federico A. Clinical applications of artificial intelligence (AI) in human cancer: Is It time to update the diagnostic and predictive models in managing hepatocellular carcinoma (HCC)? Diagnostics 2025 15 3 252 10.3390/diagnostics15030252 39941182
    [Google Scholar]
  141. Seven İ. Bayram D. Arslan H. Köş F.T. Gümüşlü K. Aktürk Esen S. Şahin M. Şendur M.A.N. Uncu D. Predicting hepatocellular carcinoma survival with artificial intelligence. Sci. Rep. 2025 15 1 6226 10.1038/s41598‑025‑90884‑6 39979406
    [Google Scholar]
/content/journals/ccdt/10.2174/0115680096380583251007074500
Loading
Current Advances in Hepatocellular Carcinoma: Transition from
Bentham Science Publishers ; https://doi.org/10.2174/0115680096380583251007074500
/content/journals/ccdt/10.2174/0115680096380583251007074500
/content/journals/ccdt/10.2174/0115680096380583251007074500
Loading

Data & Media loading...


  • Article Type:     Review Article
Keywords: Machine learning ; Liver resection ; Nanotechnology ; Hepatocellular carcinoma ; Artificial intelligence ; Biomarkers

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test