Page 103 - Read Online

Page 61 Xu et al. Art Int Surg 2023;3:48-63 https://dx.doi.org/10.20517/ais.2022.33

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. DOI PubMed PMC
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. DOI PubMed PMC
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. DOI PubMed
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. DOI
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. DOI
PubMed
27. Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Markets 2021;31:685-95. DOI
28. Jiang T, Gradus JL, Rosellini AJ. Supervised machine learning: a brief primer. Behav Ther 2020;51:675-87. DOI PubMed PMC
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. DOI
30. Sarker IH. Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput Sci
2021;2:420. DOI PubMed PMC
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. DOI PubMed PMC
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. DOI
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. DOI
35. Savage N. Breaking into the black box of artificial intelligence. Nature 2022. DOI PubMed
36. Chartrand G, Cheng PM, Vorontsov E, et al. Deep learning: a primer for radiologists. Radiographics 2017;37:2113-31. DOI PubMed
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. DOI PubMed PMC
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. DOI PubMed PMC
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. DOI
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. DOI PubMed PMC
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. DOI PubMed
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. DOI PubMed PMC
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. DOI PubMed PMC
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. DOI
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. DOI PubMed
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. DOI PubMed
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. DOI PubMed PMC
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. DOI PubMed
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. DOI PubMed PMC
50. Yeom SK, Lee CH, Cha SH, Park CM. Prediction of liver cirrhosis, using diagnostic imaging tools. World J Hepatol 2015;7:2069-79.
DOI PubMed PMC
51. association for the study of the liver. electronic address: easloffice@easloffice.eu, European association for the study of the liver.
EASL clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol 2018;69:182-236. DOI
52. Tanaka H. Current role of ultrasound in the diagnosis of hepatocellular carcinoma. J Med Ultrason 2020;47:239-55. DOI PubMed