REFERENCES
2. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc 2020;92:807-12.
3. Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol 2019;25:1666-83.
4. Nam D, Chapiro J, Paradis V, Seraphin TP, Kather JN. Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction. JHEP Rep 2022;4:100443.
5. Russell SJ, Norvig P. Artificial intelligence. A modern approach. New Jersey: Pearson Education, Inc.,; 2003.
6. 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.
7. Locke S, Bashall A, Al-adely S, Moore J, Wilson A, Kitchen GB. Natural language processing in medicine: a review. Trends Anaesth Crit Care 2021;38:4-9.
8. Feng B, Ma XH, Wang S, Cai W, Liu XB, Zhao XM. Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: current status and future perspectives. World J Gastroenterol 2021;27:5341-50.
9. Cucchetti A, Piscaglia F, Grigioni AD, et al. Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study. J Hepatol 2010;52:880-8.
10. Goyal M, Knackstedt T, Yan S, Hassanpour S. Artificial intelligence-based image classification methods for diagnosis of skin cancer: challenges and opportunities. Comput Biol Med 2020;127:104065.
11. Ting DSW, Cheung CY, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 2017;318:2211-23.
12. Cuocolo R, Cipullo MB, Stanzione A, et al. Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis. Eur Radiol 2020;30:6877-87.
13. Cho SJ, Sunwoo L, Baik SH, Bae YJ, Choi BS, Kim JH. Brain metastasis detection using machine learning: a systematic review and meta-analysis. Neuro Oncol 2021;23:214-25.
14. Barua I, Vinsard DG, Jodal HC, et al. Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis. Endoscopy 2021;53:277-84.
15. Hardalaç F, Uysal F, Peker O, et al. Fracture detection in wrist X-ray images using deep learning-based object detection models. Sensors 2022;22:1285.
16. 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.
17. Association For The Study Of The Liver, European Organisation For Research And Treatment Of Cancer. EASL-EORTC clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol 2012;56:908-43.
18. Mitchell DG, Bruix J, Sherman M, Sirlin CB. LI-RADS (Liver Imaging Reporting and Data System): summary, discussion, and consensus of the LI-RADS Management Working Group and future directions. Hepatology 2015;61:1056-65.
19. Liang Y, Xu F, Guo Y, et al. 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.
20. Llovet JM, Brú C, Bruix J. Prognosis of hepatocellular carcinoma: the BCLC staging classification. Semin Liver Dis 1999;19:329-38.
21. El-Serag HB, Kanwal F. Epidemiology of hepatocellular carcinoma in the United States: Where are we? Hepatology 2014;60:1767-75.
22. Lewis S, Hectors S, Taouli B. Radiomics of hepatocellular carcinoma. Abdom Radiol 2021;46:111-23.
23. Castaldo A, De Lucia DR, Pontillo G, et al. State of the art in artificial intelligence and radiomics in hepatocellular carcinoma. Diagnostics 2021;11:1194.
25. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016;278:563-77.
26. Schoenberg MB, Bucher JN, Koch D, et al. A novel machine learning algorithm to predict disease free survival after resection of hepatocellular carcinoma. Ann Transl Med 2020;8:434.
27. Schoenberg MB, Bucher JN, Vater A, et al. Resection or transplant in early hepatocellular carcinoma. Dtsch Arztebl Int 2017;114:519-26.
28. Lozanovski VJ, Probst P, Arefidoust A, et al. Prognostic role of the donor risk index, the eurotransplant donor risk index, and the balance of risk score on graft loss after liver transplantation. Transpl Int 2021;34:778-800.
29. Kim WR, Mannalithara A, Heimbach JK, et al. MELD 3.0: the model for end-stage liver disease updated for the modern era. Gastroenterology 2021;161:1887-1895.e4.
30. Rana A, Jie T, Porubsky M, et al. The survival outcomes following liver transplantation (SOFT) score: validation with contemporaneous data and stratification of high-risk cohorts. Clin Transplant 2013;27:627-32.
31. Veerankutty FH, Jayan G, Yadav MK, et al. Artificial Intelligence in hepatology, liver surgery and transplantation: emerging applications and frontiers of research. World J Hepatol 2021;13:1977-90.
32. Khorsandi SE, Hardgrave HJ, Osborn T, et al. Artificial intelligence in liver transplantation. Transplant Proc 2021;53:2939-44.
33. Kazemi A, Kazemi K, Sami A, Sharifian R. Identifying factors that affect patient survival after orthotopic liver transplant using machine-learning techniques. Exp Clin Transplant 2019;17:775-83.
34. Wingfield LR, Ceresa C, Thorogood S, Fleuriot J, Knight S. Using artificial intelligence for predicting survival of individual grafts in liver transplantation: a systematic review. Liver Transpl 2020;26:922-34.
35. Briceño J, Cruz-Ramírez M, Prieto M, et al. Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter Spanish study. J Hepatol 2014;61:1020-8.
36. Lau L, Kankanige Y, Rubinstein B, et al. Machine-learning algorithms predict graft failure after liver transplantation. Transplantation 2017;101:e125-32.
37. Liu CL, Soong RS, Lee WC, Jiang GW, Lin YC. Predicting short-term survival after liver transplantation using machine learning. Sci Rep 2020;10:5654.
38. 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.
39. Ivanics T, Nelson W, Patel MS, et al. The Toronto postliver transplantation hepatocellular carcinoma recurrence calculator: a machine learning approach. Liver Transpl 2022;28:593-602.
40. Association for the Study of the Liver. Electronic address: [email protected], European Association for the Study of the Liver. EASL Clinical Practice Guidelines: management of hepatocellular carcinoma. J Hepatol 2018;69:182-236.
41. Balzan S, Belghiti J, Farges O, et al. The “50-50 criteria” on postoperative day 5: an accurate predictor of liver failure and death after hepatectomy. Ann Surg 2005;242:824-9.
42. Wang K, Mamidipalli A, Retson T, et al. Members of the NASH Clinical Research Network. Automated CT and MRI liver segmentation and biometry using a generalized convolutional neural network. Radiol Artif Intell 2019;1:180022.
43. Winkel DJ, Weikert TJ, Breit HC, et al. Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation. Eur J Radiol 2020;126:108918.
44. Mise Y, Hasegawa K, Satou S, et al. How has virtual hepatectomy changed the practice of liver surgery? Ann Surg 2018;268:127-33.
45. Huff TJ, Ludwig PE, Zuniga JM. The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning. Expert Rev Med Devices 2018;15:349-56.
46. Durand F, Valla D. Assessment of the prognosis of cirrhosis: child-pugh versus MELD. J Hepatol 2005;42 Suppl:S100-7.
47. Johnson PJ, Berhane S, Kagebayashi C, et al. Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach-the ALBI grade. J Clin Oncol 2015;33:550-8.
48. Lu LH, Zhang YF, Mu-Yan C, et al. Platelet-albumin-bilirubin grade: Risk stratification of liver failure, prognosis after resection for hepatocellular carcinoma. Dig Liver Dis 2019;51:1430-7.
49. Zhou P, Chen B, Miao XY, et al. Comparison of FIB-4 index and child-pugh score in predicting the outcome of hepatic resection for hepatocellular carcinoma. J Gastrointest Surg 2020;24:823-31.
50. Mai RY, Lu HZ, Bai T, et al. Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma. Surgery 2020;168:643-52.
51. Kudo M. Systemic therapy for hepatocellular carcinoma: 2017 update. Oncology 2017;93 Suppl 1:135-46.
52. Llovet JM, Montal R, Villanueva A. Randomized trials and endpoints in advanced HCC: role of PFS as a surrogate of survival. J Hepatol 2019;70:1262-77.
53. D’Amico F, Schwartz M, Vitale A, et al. Predicting recurrence after liver transplantation in patients with hepatocellular carcinoma exceeding the up-to-seven criteria. Liver Transpl 2009;15:1278-87.
54. Ling Q, Liu J, Zhuo J, et al. Development of models to predict early post-transplant recurrence of hepatocellular carcinoma that also integrate the quality and characteristics of the liver graft: a national registry study in China. Surgery ;2018:155-64.
55. Agopian VG, Harlander-Locke M, Zarrinpar A, et al. A novel prognostic nomogram accurately predicts hepatocellular carcinoma recurrence after liver transplantation: analysis of 865 consecutive liver transplant recipients. J Am Coll Surg 2015;220:416-27.
56. Mehta N, Heimbach J, Harnois DM, et al. Validation of a risk estimation of tumor recurrence after transplant (RETREAT) score for hepatocellular carcinoma recurrence after liver transplant. JAMA Oncol 2017;3:493-500.
57. El-Gebaly F, Abou-Saif S, Elkadeem M, et al. Study of serum soluble programmed death ligand 1 as a prognostic factor in hepatocellular carcinoma in egyptian patients. Curr Cancer Drug Targets 2019;19:896-905.
58. Wang S, Zhang JH, Wang H, et al. A novel multidimensional signature predicts prognosis in hepatocellular carcinoma patients. J Cell Physiol 2019;234:11610-9.
59. Erstad DJ, Tanabe KK. Prognostic and therapeutic implications of microvascular invasion in hepatocellular carcinoma. Ann Surg Oncol 2019;26:1474-93.
60. Ma X, Wei J, Gu D, et al. Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT. Eur Radiol 2019;29:3595-605.
61. Xu X, Zhang HL, Liu QP, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol 2019;70:1133-44.
62. Zhang Z, Chen J, Jiang H, et al. Gadoxetic acid-enhanced MRI radiomics signature: prediction of clinical outcome in hepatocellular carcinoma after surgical resection. Ann Transl Med 2020;8:870.
63. 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.
64. Ho WH, Lee KT, Chen HY, Ho TW, Chiu HC. Disease-free survival after hepatic resection in hepatocellular carcinoma patients: a prediction approach using artificial neural network. PLoS One 2012;7:e29179.
65. 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.
66. 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.
67. Zheng BH, Liu LZ, Zhang ZZ, et al. Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients. BMC Cancer 2018;18:1148.
68. Shen JX, Zhou Q, Chen ZH, et al. Longitudinal radiomics algorithm of posttreatment computed tomography images for early detecting recurrence of hepatocellular carcinoma after resection or ablation. Transl Oncol 2021;14:100866.
69. Tiong L, Maddern GJ. Systematic review and meta-analysis of survival and disease recurrence after radiofrequency ablation for hepatocellular carcinoma. Br J Surg 2011;98:1210-24.
70. Hung HH, Chiou YY, Hsia CY, et al. Survival rates are comparable after radiofrequency ablation or surgery in patients with small hepatocellular carcinomas. Clin Gastroenterol Hepatol 2011;9:79-86.
71. Kao WY, Chiou YY, Hung HH, et al. Risk factors for long-term prognosis in hepatocellular carcinoma after radiofrequency ablation therapy: the clinical implication of aspartate aminotransferase-platelet ratio index. Eur J Gastroenterol Hepatol 2011;23:528-36.
72. Kao WY, Chiou YY, Hung HH, et al. Serum alpha-fetoprotein response can predict prognosis in hepatocellular carcinoma patients undergoing radiofrequency ablation therapy. Clin Radiol 2012;67:429-36.
73. Wu CF, Wu YJ, Liang PC, Wu CH, Peng SF, Chiu HW. Disease-free survival assessment by artificial neural networks for hepatocellular carcinoma patients after radiofrequency ablation. J Formos Med Assoc 2017;116:765-73.
74. Liang JD, Ping XO, Tseng YJ, Huang GT, Lai F, Yang PM. Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods. Comput Methods Programs Biomed 2014;117:425-34.
75. Pesapane F, Nezami N, Patella F, Geschwind JF. New concepts in embolotherapy of HCC. Med Oncol 2017;34:58.
76. Kim BK, Kim SU, Kim KA, et al. Complete response at first chemoembolization is still the most robust predictor for favorable outcome in hepatocellular carcinoma. J Hepatol 2015;62:1304-10.
77. Marcacuzco Quinto A, Nutu OA, San Román Manso R, et al. Complications of transarterial chemoembolization (TACE) in the treatment of liver tumors. Cir Esp 2018;96:560-7.
78. Sieghart W, Hucke F, Pinter M, et al. The ART of decision making: retreatment with transarterial chemoembolization in patients with hepatocellular carcinoma. Hepatology 2013;57:2261-73.
79. Adhoute X, Penaranda G, Naude S, et al. Retreatment with TACE: the ABCR SCORE, an aid to the decision-making process. J Hepatol 2015;62:855-62.
80. Mähringer-Kunz A, Weinmann A, Schmidtmann I, et al. Validation of the SNACOR clinical scoring system after transarterial chemoembolisation in patients with hepatocellular carcinoma. BMC Cancer 2018;18:489.
81. Mähringer-Kunz A, Wagner F, Hahn F, et al. Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network: a pilot study. Liver Int 2020;40:694-703.
82. 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.
83. 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.
84. 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.
85. 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.
86. Abajian A, Murali N, Savic LJ, et al. Predicting treatment response to image-guided therapies using machine learning: an example for trans-arterial treatment of hepatocellular carcinoma. J Vis Exp 2018; doi: 10.3791/58382.
87. Altman BJ, Rathmell JC. Metabolic stress in autophagy and cell death pathways. Cold Spring Harb Perspect Biol 2012;4:a008763.
88. Ziv E, Yarmohammadi H, Boas FE, et al. Gene signature associated with upregulation of the Wnt/β-catenin signaling pathway predicts tumor response to transarterial embolization. J Vasc Interv Radiol 2017;28:349-355.e1.
89. Chen B, Garmire L, Calvisi DF, Chua MS, Kelley RK, Chen X. Harnessing big “omics” data and AI for drug discovery in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 2020;17:238-51.
90. Wilhelm S, Carter C, Lynch M, et al. Discovery and development of sorafenib: a multikinase inhibitor for treating cancer. Nat Rev Drug Discov 2006;5:835-44.
91. Abou-Alfa GK, Schwartz L, Ricci S, et al. Phase II study of sorafenib in patients with advanced hepatocellular carcinoma. J Clin Oncol 2006;24:4293-300.
92. Zhu YJ, Zheng B, Wang HY, Chen L. New knowledge of the mechanisms of sorafenib resistance in liver cancer. Acta Pharmacol Sin 2017;38:614-22.
93. Abuhelwa AY, Badaoui S, Yuen HY, et al. A clinical scoring tool validated with machine learning for predicting severe hand-foot syndrome from sorafenib in hepatocellular carcinoma. Cancer Chemother Pharmacol 2022;89:479-85.
94. Sangro B, Sarobe P, Hervás-Stubbs S, Melero I. Advances in immunotherapy for hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 2021;18:525-43.
95. Chen D, Liu J, Zang L, et al. Integrated machine learning and bioinformatic analyses constructed a novel stemness-related classifier to predict prognosis and immunotherapy responses for hepatocellular carcinoma patients. Int J Biol Sci 2022;18:360-73.
96. 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.
97. Zeng Q, Klein C, Caruso S, et al. Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology. J Hepatol 2022;77:116-27.
98. Lui TKL, Cheung KS, Leung WK. Machine learning models in the prediction of 1-year mortality in patients with advanced hepatocellular cancer on immunotherapy: a proof-of-concept study. Hepatol Int 2022;16:879-91.
99. Ferrero E, Dunham I, Sanseau P. In silico prediction of novel therapeutic targets using gene-disease association data. J Transl Med 2017;15:182.
100. Tong Z, Zhou Y, Wang J. Identifying potential drug targets in hepatocellular carcinoma based on network analysis and one-class support vector machine. Sci Rep 2019;9:10442.
