REFERENCES
1. Llovet JM, Kelley RK, Villanueva A, et al. Hepatocellular carcinoma. Nat Rev Dis Primers 2021;7:6.
2. 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.
3. Finn RS, Qin S, Ikeda M, et al. IMbrave150 investigators. atezolizumab plus bevacizumab in unresectable hepatocellular carcinoma. N Engl J Med 2020;382:1894-905.
4. Brar G, Greten TF, Graubard BI, et al. Hepatocellular carcinoma survival by etiology: a seer-medicare database analysis. Hepatol Commun 2020;4:1541-51.
5. Yang JD. Detect or not to detect very early-stage hepatocellular carcinoma? Clin Mol Hepatol 2019;25:335-43.
6. SD; British society of gastroenterology. guidelines for the diagnosis and treatment of hepatocellular carcinoma (HCC) in adults. Gut 2003;52 Suppl 3:iii1-8.
7. Best J, Sydor S, Bechmann LP, Canbay A. Evaluation and impact of different biomarkers for early detection of hepatocellular carcinoma. HR 2020:2020.
8. Simmons O, Fetzer DT, Yokoo T, et al. Predictors of adequate ultrasound quality for hepatocellular carcinoma surveillance in patients with cirrhosis. Aliment Pharmacol Ther 2017;45:169-77.
9. Mazzaferro V, Regalia E, Doci R, et al. Liver transplantation for the treatment of small hepatocellular carcinomas in patients with cirrhosis. N Engl J Med 1996;334:693-9.
11. Burak KW. Prognosis in the early stages of hepatocellular carcinoma: predicting outcomes and properly selecting patients for curative options. Can J Gastroenterol 2011;25:482-4.
12. Farinati F, Sergio A, Baldan A, et al. Early and very early hepatocellular carcinoma: when and how much do staging and choice of treatment really matter? BMC Cancer 2009;9:33.
13. Kim HY, Lampertico P, Nam JY, et al. An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B. J Hepatol 2022;76:311-8.
14. Bharti P, Mittal D, Ananthasivan R. Preliminary study of chronic liver classification on ultrasound images using an ensemble model. Ultrason Imaging 2018;40:357-79.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. Sun SW, Xu X, Liu QP, et al. LiSNet: An artificial intelligence -based tool for liver imaging staging of hepatocellular carcinoma aggressiveness. Med Phys 2022;49:6903-13.
22. Noh B, Park YM, Kwon Y, et al. Machine learning-based survival rate prediction of Korean hepatocellular carcinoma patients using multi-center data. BMC Gastroenterol 2022;22:85.
23. Simsek C, Can Guven D, Koray Sahin T, et al. Artificial intelligence method to predict overall survival of hepatocellular carcinoma. Hepatol Forum 2021;2:64-8.
24. 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.
25. Saillard C, Schmauch B, Laifa O, et al. Predicting survival after hepatocellular carcinoma resection using deep-learning on histological slides. Journal of Hepatology 2020;73:S381.
26. 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. Meth Pro 2014;117:425-34.
27. Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Markets 2021;31:685-95.
28. Jiang T, Gradus JL, Rosellini AJ. Supervised machine learning: a brief primer. Behav Ther 2020;51:675-87.
29. Ghahramani Z. Unsupervised learning. in: bousquet o, von luxburg u, rätsch g, editors. advanced lectures on machine learning. berlin: springer berlin heidelberg; 2004. pp.72-112.
30. Sarker IH. Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput Sci 2021;2:420.
31. Han SH, Kim KW, Kim S, Youn YC. Artificial neural network: understanding the basic concepts without mathematics. Dement Neurocogn Disord 2018;17:83-9.
32. Pai A. CNN vs. RNN vs. ANN – analyzing 3 types of neural networks in deep learning. Available from: https://www.analyticsvidhya.com/blog/2020/02/cnn-vs-rnn-vs-mlp-analyzing-3-types-of-neural-networks-in-deep-learning/ [Last accessed on 23 Mar 2023].
33. Indolia S, Goswami AK, Mishra S, Asopa P. Conceptual understanding of convolutional neural network- a deep learning approach. Procedia Computer Science 2018;132:679-88.
34. Marhon SA, Cameron CJF, Kremer SC. Recurrent neural networks. in: bianchini m, maggini m, jain lc, editors. handbook on neural information processing. berlin: springer berlin heidelberg; 2013.p.29-65.
35. Savage N. Breaking into the black box of artificial intelligence. Nature 2022; doi: 10.1038/d41586-022-00858-1.
36. Chartrand G, Cheng PM, Vorontsov E, et al. Deep learning: a primer for radiologists. Radiographics 2017;37:2113-31.
37. 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.
38. Liu X, Song JL, Wang SH, Zhao JW, Chen YQ. Learning to diagnose cirrhosis with liver capsule guided ultrasound image classification. Sensors 2017;17:149.
39. Książek W, Abdar M, Acharya UR, Pławiak P. A novel machine learning approach for early detection of hepatocellular carcinoma patients. Csri 2019;54:116-27.
40. Brehar R, Mitrea DA, Vancea F, et al. Comparison of deep-learning and conventional machine-learning methods for the automatic recognition of the hepatocellular carcinoma areas from ultrasound images. Sensors 2020;20:3085.
41. 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.
42. 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.
43. Streba CT, Ionescu M, Gheonea DI, et al. Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors. World J Gastroenterol 2012;18:4427-34.
44. Hassan TM, Elmogy M, Sallam E. Diagnosis of focal liver diseases based on deep learning technique for ultrasound images. Arab J Sci Eng 2017;42:3127-40.
45. 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.
46. 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.
47. 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.
48. 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.
49. Tunissiolli NM, Castanhole-Nunes MMU, Biselli-Chicote PM, et al. Hepatocellular carcinoma: a comprehensive review of biomarkers, clinical aspects, and therapy. Asian Pac J Cancer Prev 2017;18:863-72.
50. Yeom SK, Lee CH, Cha SH, Park CM. Prediction of liver cirrhosis, using diagnostic imaging tools. World J Hepatol 2015;7:2069-79.
51. 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.
52. Tanaka H. Current role of ultrasound in the diagnosis of hepatocellular carcinoma. J Med Ultrason 2020;47:239-55.
53. Bhogadi Y, Brown E, Lee SY. Contrast-enhanced ultrasound in the diagnosis of infiltrative hepatocellular carcinoma: a report of three cases. Radiol Case Rep 2021;16:448-56.
54. Shen J, Wen J, Li C, et al. The prognostic value of microvascular invasion in early-intermediate stage hepatocelluar carcinoma: a propensity score matching analysis. BMC Cancer 2018;18:278.
55. 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.
56. 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.
57. Liu F, Liu D, Wang K, et al. Deep learning radiomics based on contrast-enhanced ultrasound might optimize curative treatments for very-early or early-stage hepatocellular carcinoma patients. Liver Cancer 2020;9:397-413.
58. 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.
59. Gotra A, Sivakumaran L, Chartrand G, et al. Liver segmentation: indications, techniques and future directions. Insights Imaging 2017;8:377-92.
60. Al-kababji A, Bensaali F, Dakua SP, Himeur Y. Automated liver tissues delineation techniques: a systematic survey on machine learning current trends and future orientations. Eng Appl Artif Intell 2023;117:105532.
61. Liang F, Qian P, Su KH, et al. Abdominal, multi-organ, auto-contouring method for online adaptive magnetic resonance guided radiotherapy: An intelligent, multi-level fusion approach. Artif Intell Med 2018;90:34-41.
62. Gibson E, Giganti F, Hu Y, et al. Automatic multi-organ segmentation on abdominal CT with dense v-networks. IEEE Trans Med Imaging 2018;37:1822-34.
63. Llovet JM, Castet F, Heikenwalder M, et al. Immunotherapies for hepatocellular carcinoma. Nat Rev Clin Oncol 2022;19:151-72.
64. Muhammed A, D'Alessio A, Enica A, et al. Predictive biomarkers of response to immune checkpoint inhibitors in hepatocellular carcinoma. Expert Rev Mol Diagn 2022;22:253-64.
65. He Y, Lu M, Che J, Chu Q, Zhang P, Chen Y. Biomarkers and future perspectives for hepatocellular carcinoma immunotherapy. Front Oncol 2021;11:716844.
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. 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.
68. Malinchoc M, Kamath PS, Gordon FD, Peine CJ, Rank J, ter Borg PC. A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts. Hepatology 2000;31:864-71.
69. Kamath PS, Wiesner RH, Malinchoc M, et al. A model to predict survival in patients with end-stage liver disease. Hepatology 2001;33:464-70.
70. Bertsimas D, Kung J, Trichakis N, Wang Y, Hirose R, Vagefi PA. Development and validation of an optimized prediction of mortality for candidates awaiting liver transplantation. Am J Transplant 2019;19:1109-18.
71. Yu YD, Lee KS, Man Kim J, et al. Korean organ transplantation registry study group. Artificial intelligence for predicting survival following deceased donor liver transplantation: Retrospective multi-center study. Int J Surg 2022;105:106838.
72. 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.
73. Guijo-Rubio D, Briceño J, Gutiérrez PA, Ayllón MD, Ciria R, Hervás-Martínez C. Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation. PLoS One 2021;16:e0252068.
74. 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.
75. 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.
76. 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.
77. Kant I. Critique of Pure Reason. Available from: https://play.google.com/store/books [Last accessed on 23 Mar 2023].
78. Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021;596:583-9.
79. Stokes JM, Yang K, Swanson K, et al. A deep learning approach to antibiotic discovery. Cell 2020;180:688-702.e13.
80. Marwaha JS, Kvedar JC. Crossing the chasm from model performance to clinical impact: the need to improve implementation and evaluation of AI. NPJ Digit Med 2022;5:25.
81. Jiang L, Wu Z, Xu X, et al. Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies. J Int Med Res 2021;49:3000605211000157.
82. Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ 2019;5:e13930.
83. FDA. Current good manufacturing practice (CGMP) regulations. Available from: https://www.fda.gov/drugs/pharmaceutical-quality-resources/current-good-manufacturing-practice-cgmp-regulations [Last accessed on 23 Mar 2023].
84. Dockès J, Varoquaux G, Poline JB. Preventing dataset shift from breaking machine-learning biomarkers. Gigascience 2021:10.
85. Haixiang G, Yijing L, Shang J, Mingyun G, Yuanyue H, Bing G. Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications 2017;73:220-39.
86. Storkey AJ. When training and test sets are different: characterising learning transfer. Available from:https://homepages.inf.ed.ac.uk/amos/publications/Storkey2009TrainingTestDifferent.pdf [Last accessed on 23 Mar 2023].
87. Shah NH, Milstein A, Bagley PhD SC. Making machine learning models clinically useful. JAMA 2019;322:1351-2.
88. Luo W, Phung D, Tran T, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res 2016;18:e323.
89. Mathrani A, Susnjak T, Ramaswami G, Barczak A. Perspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics. Comput Educ 2021;2:100060.
90. Petch J, Di S, Nelson W. Opening the black box: the promise and limitations of explainable machine learning in cardiology. Can J Cardiol 2022;38:204-13.
91. Suzuki K, Reyes M, Syeda-Mahmood T, Konukoglu E, Glocker B, Wiest R, et al. Interpretability of machine intelligence in medical image computing and multimodal learning for clinical decision support: second international workshop, iMIMIC 2019, and 9th international workshop, ML-CDS 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings. Available from: https://play.google.com/store/books/details?id=Vvm4DwAAQBAJ [Last accessed on 23 Mar 2023]