Artificial neural networks predict survival from pancreatic cancer after radical surgery

American Journal of Surgery

Volumn 205, Issue 1, 2013, Pages 1-7

  Ansari, Daniel ^a   Nilsson, Johan ^a   Andersson, Roland ^a   Regnér, Sara ^a   Tingstedt, Bobby ^a   Andersson, Bodil ^a  

^a LUND UNIVERSITY HOSPITAL   (Sweden)

Author keywords

Artificial neural network; Pancreatic cancer; Prognosis; Surgery; Survival

Indexed keywords

CAPECITABINE; FLUOROURACIL; GEMCITABINE;

ADULT; AGE; AGED; ANASTOMOSIS LEAKAGE; ARTICLE; ARTIFICIAL NEURAL NETWORK; BODY MASS; CANCER SURVIVAL; COMPARATIVE STUDY; FEMALE; HEART INFARCTION; HEMATOMA; HISTOPATHOLOGY; HUMAN; LEUKOCYTE COUNT; LONG TERM SURVIVAL; LYMPH NODE METASTASIS; MAJOR CLINICAL STUDY; MALE; PANCREAS ADENOCARCINOMA; PANCREAS FISTULA; PANCREAS RESECTION; PANCREATICODUODENECTOMY; PREDICTIVE VALUE; PRIORITY JOURNAL; PROPORTIONAL HAZARDS MODEL; SEROMA; SURVIVAL PREDICTION; TUMOR DIFFERENTIATION; TUMOR LOCALIZATION; WOUND DEHISCENCE;

AGED; CARCINOMA, PANCREATIC DUCTAL; CHEMOTHERAPY, ADJUVANT; FEMALE; HUMANS; KAPLAN-MEIER ESTIMATE; LEUKOCYTE COUNT; LYMPHATIC METASTASIS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); PANCREATECTOMY; PANCREATIC NEOPLASMS; PANCREATICODUODENECTOMY; PROGNOSIS; REGRESSION ANALYSIS;

EID: 84871195183     PISSN: 00029610     EISSN: 18791883     Source Type: Journal    
DOI: 10.1016/j.amjsurg.2012.05.032     Document Type: Article

References (40)

1. A. Jemal, R. Siegel, and J. Xu Cancer statistics, 2010 CA Cancer J Clin 60 2010 277 300
2. M.H. Katz, R. Hwang, and J.B. Fleming Tumor-node-metastasis staging of pancreatic adenocarcinoma CA Cancer J Clin 58 2008 111 125 (Pubitemid 351521866)
3. G. Garcea, A.R. Dennison, and C.J. Pattenden Survival following curative resection for pancreatic ductal adenocarcinoma. A systematic review of the literature JOP 9 2008 99 132 (Pubitemid 351611725)
4. R.W. Parks, V. Bettschart, and S. Frame Benefits of specialisation in the management of pancreatic cancer: results of a Scottish population-based study Br J Cancer 91 2004 459 465 (Pubitemid 39093563)
5. S.S. Cross, R.F. Harrison, and R.L. Kennedy Introduction to neural networks Lancet 346 1995 1075 1079
6. A. Bartosch-Harlid, B. Andersson, and U. Aho Artificial neural networks in pancreatic disease Br J Surg 95 2008 817 826
7. M. Lundin, J. Lundin, and H.B. Burke Artificial neural networks applied to survival prediction in breast cancer Oncology 57 1999 281 286
8. S.G. Dolgobrodov, P. Moore, and R. Marshall Artificial neural network: predicted vs observed survival in patients with colonic cancer Dis Colon Rectum 50 2007 184 191 (Pubitemid 46227214)
9. F. Sato, Y. Shimada, and F.M. Selaru Prediction of survival in patients with esophageal carcinoma using artificial neural networks Cancer 103 2005 1596 1605 (Pubitemid 40490020)
10. E. Biganzoli, P. Boracchi, and L. Mariani Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach Stat Med 17 1998 1169 1186 (Pubitemid 28221032)
11. D. Dindo, N. Demartines, and P.A. Clavien Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey Ann Surg 240 2004 205 213 (Pubitemid 38963009)
12. M.H. Jamal, S.A. Doi, and E. Simoneau Unresectable pancreatic adenocarcinoma: do we know who survives? HPB (Oxford) 12 2010 561 566
13. N. Yokoyama, Y. Shirai, and T. Wakai Jaundice at presentation heralds advanced disease and poor prognosis in patients with ampullary carcinoma World J Surg 29 2005 519 523 (Pubitemid 41125380)
14. J.A. Soto, O. Alvarez, and J.E. Lopera Biliary obstruction: findings at MR cholangiography and cross-sectional MR imaging Radiographics 20 2000 353 366
15. J.J. Yeh, M. Gonen, and J.S. Tomlinson Effect of blood transfusion on outcome after pancreaticoduodenectomy for exocrine tumour of the pancreas Br J Surg 94 2007 466 472 (Pubitemid 46624725)
16. T.J. Howard, J.E. Krug, and J. Yu A margin-negative R0 resection accomplished with minimal postoperative complications is the surgeon's contribution to long-term survival in pancreatic cancer J Gastrointest Surg 10 2006 1338 1345 discussion 45-6
17. M. Wagner, C. Redaelli, and M. Lietz Curative resection is the single most important factor determining outcome in patients with pancreatic adenocarcinoma Br J Surg 91 2004 586 594 (Pubitemid 38624667)
18. M.J. Pencina, and R.B. D'Agostino Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation Stat Med 23 2004 2109 2123 (Pubitemid 38968032)
19. B. Andersson, R. Andersson, and M. Ohlsson Prediction of severe acute pancreatitis at admission to hospital using artificial neural networks Pancreatology 11 2011 328 335
20. R.P. Lippmann, and D.M. Shahian Coronary artery bypass risk prediction using neural networks Ann Thorac Surg 63 1997 1635 1643 (Pubitemid 27321527)
21. M. Schemper, and T.L. Smith Efficient evaluation of treatment effects in the presence of missing covariate values Stat Med 9 1990 777 784 (Pubitemid 20343986)
22. C. Bassi, C. Dervenis, and G. Butturini Postoperative pancreatic fistula: an international study group (ISGPF) definition Surgery 138 2005 8 13 (Pubitemid 40933766)
23. F.E. Harrell Jr., K.L. Lee, and D.B. Mark Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors Stat Med 15 1996 361 387 (Pubitemid 26072233)
24. B. Damato, A. Eleuteri, and A.C. Fisher Artificial neural networks estimating survival probability after treatment of choroidal melanoma Ophthalmology 115 2008 1598 1607
25. A. Eleuteri, M.S. Aung, and A.F. Taktak Continuous and discrete time survival analysis: neural network approaches Conf Proc IEEE Eng Med Biol Soc 2007 2007 5420 5423
26. D. Faraggi, and R. Simon A neural network model for survival data Stat Med 14 1995 73 82
27. Hoang T, Trinh QA, Asselain B. Construction and validation of a prognostic model for metastatic breast cancer using bayesian neural network and regression tree. Intelligent Data Analysis in Medicine and Pharmacology. The European Conference on Artificial Intelligence, Lyon, France: ECAI; 23rd July, 2002. Work shop notes, pages 37-43.
28. A.F. Taktak, A. Eleuteri, and M.S. Aung Survival analysis in cancer using a partial logistic neural network model with Bayesian regularisation framework: a validation study Int J Knowledge Engineering Soft Data Paradigms 1 2009 277 295
29. M.F. Brennan, M.W. Kattan, and D. Klimstra Prognostic nomogram for patients undergoing resection for adenocarcinoma of the pancreas Ann Surg 240 2004 293 298 (Pubitemid 38963021)
30. T. Botsis, V.K. Anagnostou, and G. Hartvigsen Modeling prognostic factors in resectable pancreatic adenocarcinomas Cancer Inform 7 2010 281 291
31. R.N. Naguib, M.C. Robinson, and D.E. Neal Neural network analysis of combined conventional and experimental prognostic markers in prostate cancer: a pilot study Br J Cancer 78 1998 246 250 (Pubitemid 28302264)
32. S. Tsai, M.A. Choti, and L. Assumpcao Impact of obesity on perioperative outcomes and survival following pancreaticoduodenectomy for pancreatic cancer: a large single-institution study J Gastrointest Surg 14 2010 1143 1150
33. A.K. Mathur, A.A. Ghaferi, and K. Sell Influence of body mass index on complications and oncologic outcomes following hepatectomy for malignancy J Gastrointest Surg 14 2010 849 857
34. C.C. Benz Impact of aging on the biology of breast cancer Crit Rev Oncol Hematol 66 2008 65 74 (Pubitemid 351324129)
35. C.J. Etzel, M. Lu, and K. Merriman An epidemiologic study of early onset lung cancer Lung Cancer 52 2006 129 134
36. N. Li, S.I. Grivennikov, and M. Karin The unholy trinity: inflammation, cytokines, and STAT3 shape the cancer microenvironment Cancer Cell 19 2011 429 431
37. L. De Monte, M. Reni, and E. Tassi Intratumor T helper type 2 cell infiltrate correlates with cancer-associated fibroblast thymic stromal lymphopoietin production and reduced survival in pancreatic cancer J Exp Med 208 2011 469 478
38. D. Ansari, A. Rosendahl, and J. Elebro Systematic review of immunohistochemical biomarkers to identify prognostic subgroups of patients with pancreatic cancer Br J Surg 98 2011 1041 1055
39. A.J. Russ, S.M. Weber, and R.J. Rettammel Impact of selection bias on the utilization of adjuvant therapy for pancreas adenocarcinoma Ann Surg Oncol 17 2010 371 376
40. G.J. van der Heijden, A.R. Donders, and T. Stijnen Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: a clinical example J Clin Epidemiol 59 2006 1102 1109 (Pubitemid 44374315)