Page 17 - Read Online

Bektaş et al. Art Int Surg 2022;2:132-43 https://dx.doi.org/10.20517/ais.2022.20 Page 142

41. Hamamoto I, Okada S, Hashimoto T, Wakabayashi H, Maeba T, Maeta H. Prediction of the early prognosis of the hepatectomized
patient with hepatocellular carcinoma with a neural network. Computers in Biology and Medicine 1995;25:49-59. DOI PubMed
42. Zhu HB, Xu D, Ye M, et al. Deep learning-assisted magnetic resonance imaging prediction of tumor response to chemotherapy in
patients with colorectal liver metastases. Int J Cancer 2021;148:1717-30. DOI PubMed
43. Chen Y, Liu Z, Mo Y, et al. Prediction of post-hepatectomy liver failure in patients with hepatocellular carcinoma based on radiomics
using Gd-EOB-DTPA-enhanced MRI: the liver failure model. Front Oncol 2021;11:605296. DOI PubMed PMC
44. Zhu WS, Shi SY, Yang ZH, Song C, Shen J. Radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting
liver failure. World J Gastroenterol 2020;26:1208-20. DOI PubMed PMC
45. 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. DOI PubMed
46. Mai RY, Zeng J, Mo YS, et al. Artificial neural network model for liver cirrhosis diagnosis in patients with hepatitis B virus-related
hepatocellular carcinoma. Ther Clin Risk Manag 2020;16:639-49. DOI PubMed PMC
47. Kato H, Kanematsu M, Zhang X, et al. Computer-aided diagnosis of hepatic fibrosis: preliminary evaluation of MRI texture analysis
using the finite difference method and an artificial neural network. AJR Am J Roentgenol 2007;189:117-22. DOI PubMed
48. Zhang T, Wei Y, He X, et al. Prediction of remnant liver regeneration after right hepatectomy in patients with hepatocellular
carcinoma using preoperative CT texture analysis and clinical features. Contrast Media Mol Imaging 2021;2021:5572470. DOI
PubMed PMC
49. Merath K, Hyer JM, Mehta R, et al. Use of machine learning for prediction of patient risk of postoperative complications after liver,
pancreatic, and colorectal surgery. J Gastrointest Surg 2020;24:1843-51. DOI PubMed
50. Lei L, Wang Y, Xue Q, Tong J, Zhou CM, Yang JJ. A comparative study of machine learning algorithms for predicting acute kidney
injury after liver cancer resection. PeerJ 2020;8:e8583. DOI PubMed PMC
51. Tsilimigras DI, Mehta R, Moris D, et al. Utilizing machine learning for pre- and postoperative assessment of patients undergoing
resection for BCLC-0, A and B hepatocellular carcinoma: implications for resection beyond the BCLC guidelines. Ann Surg Oncol
2020;27:866-74. DOI PubMed
52. Tsilimigras DI, Mehta R, Moris D, et al. A machine-based approach to preoperatively identify patients with the most and least benefit
associated with resection for intrahepatic cholangiocarcinoma: an international multi-institutional analysis of 1146 patients. Ann Surg
Oncol 2020;27:1110-9. DOI PubMed
53. Bagante F, Spolverato G, Merath K, et al. Intrahepatic cholangiocarcinoma tumor burden: a classification and regression tree model to
define prognostic groups after resection. Surgery 2019;166:983-90. DOI PubMed
54. Tsilimigras DI, Hyer JM, Paredes AZ, et al. A novel classification of intrahepatic cholangiocarcinoma phenotypes using machine
learning techniques: an international multi-institutional analysis. Ann Surg Oncol 2020;27:5224-32. DOI PubMed
55. Moro A, Mehta R, Tsilimigras DI, et al. Prognostic factors differ according to KRAS mutational status: a classification and regression
tree model to define prognostic groups after hepatectomy for colorectal liver metastasis. Surgery 2020;168:497-503. DOI PubMed
56. Gholipour C, Fakhree MB, Shalchi RA, Abbasi M. Prediction of conversion of laparoscopic cholecystectomy to open surgery with
artificial neural networks. BMC Surg 2009;9:13. DOI PubMed PMC
57. Eldar S, Siegelmann HT, Buzaglo D, et al. Conversion of laparoscopic cholecystectomy to open cholecystectomy in acute
cholecystitis: artificial neural networks improve the prediction of conversion. World J Surg 2002;26:79-85. DOI PubMed
58. Bouarfa L, Schneider A, Feussner H, et al. Prediction of intraoperative complexity from preoperative patient data for laparoscopic
cholecystectomy. Artif Intell Med 2011;52:169-76. DOI PubMed
59. Liew PL, Lee YC, Lin YC, et al. Comparison of artificial neural networks with logistic regression in prediction of gallbladder disease
among obese patients. Dig Liver Dis 2007;39:356-62. DOI PubMed
60. Vukicevic AM, Stojadinovic M, Radovic M, et al. Automated development of artificial neural networks for clinical purposes:
Application for predicting the outcome of choledocholithiasis surgery. Comput Biol Med 2016;75:80-9. DOI PubMed
61. Shi HY, Lee HH, Tsai JT, et al. Comparisons of prediction models of quality of life after laparoscopic cholecystectomy: a longitudinal
prospective study. PLoS One 2012;7:e51285. DOI PubMed PMC
62. Velez-Serrano JF, Velez-Serrano D, Hernandez-Barrera V, et al. Prediction of in-hospital mortality after pancreatic resection in
pancreatic cancer patients: a boosting approach via a population-based study using health administrative data. PLoS One
2017;12:e0178757. DOI PubMed PMC
63. Mofidi R, Duff MD, Madhavan KK, Garden OJ, Parks RW. Identification of severe acute pancreatitis using an artificial neural
network. Surgery 2007;141:59-66. DOI PubMed
64. Ansari D, Nilsson J, Andersson R, Regnér S, Tingstedt B, Andersson B. Artificial neural networks predict survival from pancreatic
cancer after radical surgery. Am J Surg 2013;205:1-7. DOI PubMed
65. Walczak S, Velanovich V. An evaluation of artificial neural networks in predicting pancreatic cancer survival. J Gastrointest Surg
2017;21:1606-12. DOI PubMed
66. Sala Elarre P, Oyaga-Iriarte E, Yu KH, et al. Use of machine-learning algorithms in intensified preoperative therapy of pancreatic
cancer to predict individual risk of relapse. Cancers (Basel) 2019;11:606. DOI PubMed PMC
67. Mu W, Liu C, Gao F, et al. Prediction of clinically relevant pancreatico-enteric anastomotic fistulas after pancreatoduodenectomy
using deep learning of preoperative computed tomography. Theranostics 2020;10:9779-88. DOI PubMed PMC
68. Lin Z, Tang B, Cai J, et al. Preoperative prediction of clinically relevant postoperative pancreatic fistula after