REFERENCES

1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209-49.

2. Rumgay H, Ferlay J, de Martel C, et al. Global, regional and national burden of primary liver cancer by subtype. Eur J Cancer. 2022;161:108-18.

3. Liu Y, Zheng J, Hao J, et al. Global burden of primary liver cancer by five etiologies and global prediction by 2035 based on global burden of disease study 2019. Cancer Med. 2022;11:1310-23.

4. Zhang CH, Cheng Y, Zhang S, Fan J, Gao Q. Changing epidemiology of hepatocellular carcinoma in Asia. Liver Int. 2022;42:2029-41.

5. Lazarus JV, Newsome PN, Francque SM, Kanwal F, Terrault NA, Rinella ME. Reply: a multi-society Delphi consensus statement on new fatty liver disease nomenclature. Hepatology. 2024;79:E93-4.

6. Singal AG, Llovet JM, Yarchoan M, et al. AASLD Practice Guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma. Hepatology. 2023;78:1922-65.

7. Reig M, Forner A, Rimola J, et al. BCLC strategy for prognosis prediction and treatment recommendation: the 2022 update. J Hepatol. 2022;76:681-93.

8. Singal AG, Kudo M, Bruix J. Breakthroughs in hepatocellular carcinoma therapies. Clin Gastroenterol Hepatol. 2023;21:2135-49.

9. Seif El Dahan K, Reczek A, Daher D, et al. Multidisciplinary care for patients with HCC: a systematic review and meta-analysis. Hepatol Commun. 2023;7:e0143.

10. Wu J, Liu W, Qiu X, et al. A noninvasive approach to evaluate tumor immune microenvironment and predict outcomes in hepatocellular carcinoma. Phenomics. 2023;3:549-64.

11. Lassau N, Bousaid I, Chouzenoux E, et al. Three artificial intelligence data challenges based on CT and ultrasound. Diagn Interv Imaging. 2021;102:669-74.

12. Lakkaraju H, Bach SH, Jure L. Interpretable decision sets: a joint framework for description and prediction. KDD. 2016;2016:1675-84.

13. Min JK, Kwak MS, Cha JM. Overview of deep learning in gastrointestinal endoscopy. Gut Liver. 2019;13:388-93.

14. Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Deep learning with convolutional neural network in radiology. Jpn J Radiol. 2018;36:257-72.

15. Milea D, Najjar RP, Zhubo J, et al; BONSAI Group. Artificial intelligence to detect papilledema from ocular fundus photographs. N Engl J Med. 2020;382:1687-95.

16. Noorbakhsh J, Farahmand S, Foroughi Pour A, et al. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nat Commun. 2020;11:6367.

17. Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis. 2015;115:211-52.

18. Anwer R, Hussien M, Keshk A. A comparative study of machine learning and deep learning algorithms for speech emotion recognition. IJCI Int J Comput Inform. 2023;10:82-9.

19. Kawka M, Dawidziuk A, Jiao LR, Gall TMH. Artificial intelligence in the detection, characterisation and prediction of hepatocellular carcinoma: a narrative review. Transl Gastroenterol Hepatol. 2022;7:41.

20. Wang S, Summers RM. Machine learning and radiology. Med Image Anal. 2012;16:933-51.

21. Ioannou GN, Tang W, Beste LA, et al. Assessment of a deep learning model to predict hepatocellular carcinoma in patients with hepatitis C cirrhosis. JAMA Netw Open. 2020;3:e2015626.

22. Calderaro J, Seraphin TP, Luedde T, Simon TG. Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma. J Hepatol. 2022;76:1348-61.

23. Omata M, Cheng AL, Kokudo N, et al. Asia-Pacific clinical practice guidelines on the management of hepatocellular carcinoma: a 2017 update. Hepatol Int. 2017;11:317-70.

24. Schattenberg JM, Chalasani N, Alkhouri N. Artificial intelligence applications in hepatology. Clin Gastroenterol Hepatol. 2023;21:2015-25.

25. Heimbach JK, Kulik LM, Finn RS, et al. AASLD guidelines for the treatment of hepatocellular carcinoma. Hepatology. 2018;67:358-80.

26. Marrero JA, Kulik LM, Sirlin CB, et al. Diagnosis, staging, and management of hepatocellular carcinoma: 2018 practice guidance by the American Association for the Study of Liver Diseases. Hepatology. 2018;68:723-50.

27. European Association for the Study of the Liver. EASL clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol. 2018;69:182-236.

28. Schmauch B, Herent P, Jehanno P, et al. Diagnosis of focal liver lesions from ultrasound using deep learning. Diagn Interv Imaging. 2019;100:227-33.

29. 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.

30. Guo LH, Wang D, Qian YY, et al. A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images. Clin Hemorheol Microcirc. 2018;69:343-54.

31. Ta CN, Kono Y, Eghtedari M, et al. Focal liver lesions: computer-aided diagnosis by using contrast-enhanced US cine recordings. Radiology. 2018;286:1062-71.

32. Preis O, Blake MA, Scott JA. Neural network evaluation of PET scans of the liver: a potentially useful adjunct in clinical interpretation. Radiology. 2011;258:714-21.

33. Mokrane FZ, Lu L, Vavasseur A, et al. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. Eur Radiol. 2020;30:558-70.

34. Yasaka K, Akai H, Abe O, Kiryu S. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology. 2018;286:887-96.

35. Shi W, Kuang S, Cao S, et al. Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: choice of four-phase and three-phase CT imaging protocol. Abdom Radiol. 2020;45:2688-97.

36. Chlebus G, Schenk A, Moltz JH, van Ginneken B, Hahn HK, Meine H. Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Sci Rep. 2018;8:15497.

37. Jansen MJA, Kuijf HJ, Veldhuis WB, Wessels FJ, Viergever MA, Pluim JPW. Automatic classification of focal liver lesions based on MRI and risk factors. PLoS One. 2019;14:e0217053.

38. Hamm CA, Wang CJ, Savic LJ, et al. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. Eur Radiol. 2019;29:3338-47.

39. Zhang F, Yang J, Nezami N, et al. Liver tissue classification using an auto-context-based deep neural network with a multi-phase training framework. In: Bai W, Sanroma G, Wu G, Munsell BC, Zhan Y, Coupé P, editors. Patch-based techniques in medical imaging. Cham: Springer International Publishing; 2018. pp. 59-66.

40. Zhen SH, Cheng M, Tao YB, et al. Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data. Front Oncol. 2020;10:680.

41. Wang CJ, Hamm CA, Savic LJ, et al. Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features. Eur Radiol. 2019;29:3348-57.

42. Liao H, Long Y, Han R, et al. Deep learning-based classification and mutation prediction from histopathological images of hepatocellular carcinoma. Clin Transl Med. 2020;10:e102.

43. 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.

44. Calderaro J, Ziol M, Paradis V, Zucman-Rossi J. Molecular and histological correlations in liver cancer. J Hepatol. 2019;71:616-30.

45. Ziol M, Poté N, Amaddeo G, et al. Macrotrabecular-massive hepatocellular carcinoma: a distinctive histological subtype with clinical relevance. Hepatology. 2018;68:103-12.

46. Wang H, Jiang Y, Li B, Cui Y, Li D, Li R. Single-cell spatial analysis of tumor and immune microenvironment on whole-slide image reveals hepatocellular carcinoma subtypes. Cancers. 2020;12:3562.

47. Chen M, Zhang B, Topatana W, et al. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning. NPJ Precis Oncol. 2020;4:14.

48. Kather JN, Pearson AT, Halama N, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019;25:1054-6.

49. Kather JN, Heij LR, Grabsch HI, et al. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat Cancer. 2020;1:789-99.

50. Fu Y, Jung AW, Torne RV, et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat Cancer. 2020;1:800-10.

51. Sangro B, Melero I, Wadhawan S, et al. Association of inflammatory biomarkers with clinical outcomes in nivolumab-treated patients with advanced hepatocellular carcinoma. J Hepatol. 2020;73:1460-9.

52. Haber PK, Castet F, Torres-Martin M, et al. Molecular markers of response to anti-PD1 therapy in advanced hepatocellular carcinoma. Gastroenterology. 2023;164:72-88.e18.

53. Johannet P, Coudray N, Donnelly DM, et al. Using machine learning algorithms to predict immunotherapy response in patients with advanced melanoma. Clin Cancer Res. 2021;27:131-40.

54. Patel SK, George B, Rai V. Artificial intelligence to decode cancer mechanism: beyond patient stratification for precision oncology. Front Pharmacol. 2020;11:1177.

55. Liu S, Yang Z, Li G, et al. Multi-omics analysis of primary cell culture models reveals genetic and epigenetic basis of intratumoral phenotypic diversity. Genomics Proteomics Bioinformatics. 2019;17:576-89.

56. Zeng WZD, Glicksberg BS, Li Y, Chen B. Selecting precise reference normal tissue samples for cancer research using a deep learning approach. BMC Med Genomics. 2019;12:21.

57. Chaudhary K, Poirion OB, Lu L, Garmire LX. Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res. 2018;24:1248-59.

58. Chaudhary K, Poirion OB, Lu L, Huang S, Ching T, Garmire LX. Multimodal meta-analysis of 1,494 hepatocellular carcinoma samples reveals significant impact of consensus driver genes on phenotypes. Clin Cancer Res. 2019;25:463-72.

59. Hwang B, Lee JH, Bang D. Author Correction: Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med. 2021;53:1005.

60. Aizarani N, Saviano A, Sagar, et al. A human liver cell atlas reveals heterogeneity and epithelial progenitors. Nature. 2019;572:199-204.

61. Ramachandran P, Dobie R, Wilson-Kanamori JR, et al. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature. 2019;575:512-8.

62. Zhang Q, He Y, Luo N, et al. Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell. 2019;179:829-45.e20.

63. Papalexi E, Satija R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol. 2018;18:35-45.

64. Kharchenko PV, Silberstein L, Scadden DT. Bayesian approach to single-cell differential expression analysis. Nat Methods. 2014;11:740-2.

65. Hwang B, Lee JH, Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med. 2018;50:1-14.

66. Amodio M, van Dijk D, Srinivasan K, et al. Exploring single-cell data with deep multitasking neural networks. Nat Methods. 2019;16:1139-45.

67. Eraslan G, Simon LM, Mircea M, Mueller NS, Theis FJ. Single-cell RNA-seq denoising using a deep count autoencoder. Nat Commun. 2019;10:390.

68. Genshaft AS, Li S, Gallant CJ, et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol. 2016;17:188.

69. Azer SA. Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: a systematic review. World J Gastrointest Oncol. 2019;11:1218-30.

70. Nam JY, Lee JH, Bae J, et al. Novel model to predict HCC recurrence after liver transplantation obtained using deep learning: a multicenter study. Cancers. 2020;12:2791.

71. Saillard C, Schmauch B, Laifa O, et al. Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides. Hepatology. 2020;72:2000-13.

72. Yamashita R, Long J, Saleem A, Rubin DL, Shen J. Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images. Sci Rep. 2021;11:2047.

73. Lu L, Daigle BJ Jr. Prognostic analysis of histopathological images using pre-trained convolutional neural networks: application to hepatocellular carcinoma. PeerJ. 2020;8:e8668.

74. Saito A, Toyoda H, Kobayashi M, et al. Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning. Mod Pathol. 2021;34:417-25.

75. Ji GW, Zhu FP, Xu Q, et al. Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: a multi-institutional study. EBioMedicine. 2019;50:156-65.

76. Jiang YQ, Cao SE, Cao S, et al. Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning. J Cancer Res Clin Oncol. 2021;147:821-33.

77. Zhang Y, Lv X, Qiu J, et al. Deep learning with 3D convolutional neural network for noninvasive prediction of microvascular invasion in hepatocellular carcinoma. J Magn Reson Imaging. 2021;54:134-43.

78. Abajian A, Murali N, Savic LJ, et al. Predicting treatment response to intra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning-an artificial intelligence concept. J Vasc Interv Radiol. 2018;29:850-7.e1.

79. Liu QP, Xu X, Zhu FP, Zhang YD, Liu XS. Prediction of prognostic risk factors in hepatocellular carcinoma with transarterial chemoembolization using multi-modal multi-task deep learning. EClinicalMedicine. 2020;23:100379.

80. Zhang L, Xia W, Yan ZP, et al. Deep learning predicts overall survival of patients with unresectable hepatocellular carcinoma treated by transarterial chemoembolization plus sorafenib. Front Oncol. 2020;10:593292.

81. Peng J, Kang S, Ning Z, et al. Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging. Eur Radiol. 2020;30:413-24.

82. Oezdemir I, Wessner CE, Shaw C, Eisenbrey JR, Hoyt K. Tumor vascular networks depicted in contrast-enhanced ultrasound images as a predictor for transarterial chemoembolization treatment response. Ultrasound Med Biol. 2020;46:2276-86.

83. Aujay G, Etchegaray C, Blanc JF, et al. Comparison of MRI-based response criteria and radiomics for the prediction of early response to transarterial radioembolization in patients with hepatocellular carcinoma. Diagn Interv Imaging. 2022;103:360-6.

84. Wang G, Jian W, Cen X, et al. Prediction of microvascular invasion of hepatocellular carcinoma based on preoperative diffusion-weighted MR using deep learning. Acad Radiol. 2021;28 Suppl 1:S118-27.

85. Song D, Wang Y, Wang W, et al. Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters. J Cancer Res Clin Oncol. 2021;147:3757-67.

86. Liu D, Liu F, Xie X, et al. Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound. Eur Radiol. 2020;30:2365-76.

87. Shan QY, Hu HT, Feng ST, et al. CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation. Cancer Imaging. 2019;19:11.

88. Nachev P, Herron D, McNally N, Rees G, Williams B. Redefining the research hospital. NPJ Digit Med. 2019;2:119.

89. Panch T, Mattie H, Celi LA. The “inconvenient truth” about AI in healthcare. NPJ Digit Med. 2019;2:77.

90. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:195.

91. Wei L, Niraula D, Gates EDH, et al. Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. Br J Radiol. 2023;96:20230211.

92. Hosseiniyan Khatibi SM, Najjarian F, Homaei Rad H, et al. Key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approaches. Sci Rep. 2023;13:3840.

93. Ducreux M, Abou-Alfa GK, Bekaii-Saab T, et al. The management of hepatocellular carcinoma. Current expert opinion and recommendations derived from the 24th ESMO/World Congress on Gastrointestinal Cancer, Barcelona, 2022. ESMO Open. 2023;8:101567.

94. Sun H, Yang H, Mao Y. Personalized treatment for hepatocellular carcinoma in the era of targeted medicine and bioengineering. Front Pharmacol. 2023;14:1150151.

95. Sato M, Moriyama M, Fukumoto T, et al. Development of a transformer model for predicting the prognosis of patients with hepatocellular carcinoma after radiofrequency ablation. Hepatol Int. 2024;18:131-7.

96. Christou CD, Tsoulfas G. Challenges involved in the application of artificial intelligence in gastroenterology: the race is on! World J Gastroenterol. 2023;29:6168-78.

97. Najjar R. Redefining radiology: a review of artificial intelligence integration in medical imaging. Diagnostics. 2023;13:2760.

98. Guo D, Gu D, Wang H, et al. Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation. Eur J Radiol. 2019;117:33-40.

99. Erstad DJ, Tanabe KK. Prognostic and therapeutic implications of microvascular invasion in hepatocellular carcinoma. Ann Surg Oncol. 2019;26:1474-93.

100. Dong Y, Zhou L, Xia W, et al. Preoperative prediction of microvascular invasion in hepatocellular carcinoma: initial application of a radiomic algorithm based on grayscale ultrasound images. Front Oncol. 2020;10:353.

101. Famularo S, Donadon M, Cipriani F, et al; HE.RC.O.LE.S. Group. Machine learning predictive model to guide treatment allocation for recurrent hepatocellular carcinoma after surgery. JAMA Surg. 2023;158:192-202.

102. Dohan A, Barat M, Coriat R, Soyer P. A step toward a better understanding of hepatocellular progression after transarterial embolization. Diagn Interv Imaging. 2022;103:125-6.

103. Morshid A, Elsayes KM, Khalaf AM, et al. A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization. Radiol Artif Intell. 2019;1:e180021.

104. Young S, Rivard M, Kimyon R, Sanghvi T. Accuracy of liver ablation zone prediction in a single 2450MHz 100 Watt generator model microwave ablation system: an in human study. Diagn Interv Imaging. 2020;101:225-33.

105. An C, Jiang Y, Huang Z, et al. Assessment of ablative margin after microwave ablation for hepatocellular carcinoma using deep learning-based deformable image registration. Front Oncol. 2020;10:573316.

106. Parra NS, Ross HM, Khan A, et al. Advancements in the diagnosis of hepatocellular carcinoma. IJTM. 2023;3:51-65.

107. Mohan P, Lochan R, Shetty S. Biomarker in hepatocellular carcinoma. Indian J Surg Oncol. 2024;15:261-8.

108. Han Y, Akhtar J, Liu G, Li C, Wang G. Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning. Comput Struct Biotechnol J. 2023;21:3478-89.

109. Qin R, Jin T, Xu F. Biomarkers predicting the efficacy of immune checkpoint inhibitors in hepatocellular carcinoma. Front Immunol. 2023;14:1326097.

110. Gerke S, Babic B, Evgeniou T, Cohen IG. The need for a system view to regulate artificial intelligence/machine learning-based software as medical device. NPJ Digit Med. 2020;3:53.

111. Hwang TJ, Kesselheim AS, Vokinger KN. Lifecycle regulation of artificial intelligence- and machine learning-based software devices in medicine. JAMA. 2019;322:2285-6.

112. Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology. 2018;286:800-9.

113. Dupuis M, Delbos L, Veil R, Adamsbaum C. External validation of a commercially available deep learning algorithm for fracture detection in children. Diagn Interv Imaging. 2022;103:151-9.

114. Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ. 2020;370:m3210.

115. Vigdorovits A, Köteles MM, Olteanu GE, Pop O. Breaking barriers: AI’s influence on pathology and oncology in resource-scarce medical systems. Cancers. 2023;15:5692.

116. Bhat M, Rabindranath M, Chara BS, Simonetto DA. Artificial intelligence, machine learning, and deep learning in liver transplantation. J Hepatol. 2023;78:1216-33.

117. Abdullah R, Fakieh B. Health care employees’ perceptions of the use of artificial intelligence applications: survey study. J Med Internet Res. 2020;22:e17620.

118. Fan W, Liu J, Zhu S, Pardalos PM. Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Ann Oper Res. 2020;294:567-92.

119. Schmidt P, Biessmann F, Teubner T. Transparency and trust in artificial intelligence systems. J Decis Syst. 2020;29:260-78.

120. Guidotti R, Monreale A, Ruggieri S, Turini F, Giannotti F, Pedreschi D. A survey of methods for explaining black box models. ACM Comput Surv. 2019;51:1-42.

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