Predicting survival of individual patients with esophageal cancer by adaptive neuro-fuzzy inference system approach

Applied Soft Computing Journal

Volumn 35, Issue , 2015, Pages 583-590

  Wang, Chang Yu ^a   Tsai, Jinn Tsong ^b   Fang, Chun Hsiung ^c   Lee, Tsair Fwu ^c   Chou, Jyh Horng ^c,d  

^a YUAN'S GENERAL HOSPITAL   (Taiwan)

^b NATIONAL PINGTUNG UNIVERSITY   (Taiwan)

^c NATIONAL KAOHSIUNG UNIVERSITY OF APPLIED SCIENCES   (Taiwan)

^d KAOHSIUNG MEDICAL UNIVERSITY   (Taiwan)

Author keywords

Adaptive fuzzy inference system (ANFIS); Esophageal cancer; Radiotherapy; Survival analysis

Indexed keywords

DIAGNOSIS; DISEASES; FORECASTING; FUZZY LOGIC; FUZZY SYSTEMS; METADATA; PATIENT TREATMENT; RADIOTHERAPY; RISK ASSESSMENT; TRACKING (POSITION);

ADAPTIVE FUZZY; ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; DIFFERENT TREATMENTS; ESOPHAGEAL CANCER; MULTI-DISCIPLINARY TEAMS; SURVIVAL ANALYSIS; SURVIVAL PREDICTION; SURVIVAL PROBABILITIES;

FUZZY INFERENCE;

EID: 84937558592     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2015.05.045     Document Type: Article

References (32)

1. J. Anandpushparaj ANFIS based tumor detection in thoracic images Res. J. Recent Sci. 3 2014 45 49
2. E. Biganzoli, P. Boracchi, L. Mariani, and E. Marubini Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach Stat. Med. 17 1998 1169 1186
3. G.J. Chang, C.Y. Hu, C. Eng, J.M. Skibber, and M.A. Rodriguez-Bigas Practical application of a calculator for conditional survival in colon cancer J. Clin. Oncol. 27 2009 5938 5943
4. S.W. Chang, A.K. Sameem, R. Mohd Zain, and A.F.M.A. Merican A Bootstrap-ANFIS framework for oral cancer prognosis based on clinical and genomic markers IEEE International Conference on Computer Science and Information Technology Chengdu, China, 9-11 July 2010 2010 487 491
5. A.B. Crumley, D.C. McMillan, M. McKernan, A.C. McDonald, and R.C. Stuart Evaluation of an inflammation-based prognostic score in patients with inoperable gastro-oesophageal cancer Br. J. Cancer 94 2006 637 641
6. D.N. Devesh, and D.K. Rajendra Implementation of ANFIS for breast cancer detection using TMS320C6713 DSP International Conference and Workshop on Emerging Trends in Technology, vol. 13 2011 8 11
7. S.B. Edge, and D.R. Byrd AJCC Cancer Staging Manual 7th ed. 2010 Springer
8. P.C. Enzinger, and R.J. Mayer Esophageal cancer N. Engl. J. Med. 349 2003 2241 2252
9. L.M. Forrest, D.C. McMillan, C.S. McArdle, W.J. Angerson, and D.J. Dunlop Evaluation of cumulative prognostic scores based on the systemic inflammatory response in patients with inoperable non-small-cell lung cancer Br. J. Cancer 89 2003 1028 1030
10. P. Glen, N.B. Jamieson, D.C. McMillan, R. Carter, C.W. Imrie, and C.J. McKay Evaluation of an inflammation-based prognostic score in patients with inoperable pancreatic cancer Pancreatology 6 2006 450 453
11. H. Hamdan, and J.M. Garibaldi Adaptive neuro-fuzzy inference system (ANFIS) in modelling breast cancer survival 2010 IEEE International Conference on Fuzzy Systems 2010 1 8
12. H. Hamdan, and J.M. Garibaldi An investigation of the effect of input representation in ANFIS modelling of breast cancer survival Presented at the International Joint Conference on Computational Intelligence Valencia, Spain, October 24-26 2010
13. H. Hamdan, and J.M. Garibaldi A framework for automatic modelling of survival using fuzzy inference 2012 IEEE International Conference on Fuzzy Systems Brisbane, QLD 2012 1 8
14. J.S.R. Jang ANFIS: adaptive-network-based fuzzy inference system IEEE Trans. Syst. Man Cybern. 23 1993 665 685
15. J.S.R. Jang, C.T. Sun, and E. Mizutani Neuro-Fuzzy and Soft Computing 2004 Pearson Education Taipei
16. A.Z. Lorestani, D.A.M. Bidgoli, and D.M.A. Dezfoli Improve breast cancer detection in mammography images using neuro-fuzzy (ANFIS) Int. J. Adv. Res. Comput. Sci. 4 2013 157 160
17. D.C. McMillan An inflammation-based prognostic score and its role in the nutrition-based management of patients with cancer Proc. Nutr. Soc. 67 2008 257 262
18. D.C. McMillan, J.E. Crozier, K. Canna, W.J. Angerson, and C.S. McArdle Evaluation of an inflammation-based prognostic score (GPS) in patients undergoing resection for colon and rectal cancer Int. J. Colorectal Dis. 22 2007 881 886
19. D.C. McMillan, M.M. Elahi, N. Sattar, W.J. Angerson, J. Johnstone, and C.S. McArdle Measurement of the systemic inflammatory response predicts cancer-specific and non-cancer survival in patients with cancer Nutr. Cancer 41 2001 64 69
20. A.M. Murri, J.M. Bartlett, P.A. Canney, J.C. Doughty, C. Wilson, and D.C. McMillan Evaluation of an inflammation-based prognostic score (GPS) in patients with metastatic breast cancer Br. J. Cancer 94 2006 227 230
21. M. Negnevitsky Artificial Intelligence: A Guide to Intelligent Systems 2nd ed. 2005 Addison-Wesley Longman Publishing Co., Inc.
22. S. Ramsey, G.W. Lamb, M. Aitchison, J. Graham, and D.C. McMillan Evaluation of an inflammation-based prognostic score in patients with metastatic renal cancer Cancer 109 2007 205 212
23. S. Salas, J.L. Deville, R. Giorgi, T. Pignon, D. Bagarry, K. Barrau, M. Zanaret, A. Giovanni, A. Bourgeois, R. Favre, and F. Duffaud Nutritional factors as predictors of response to radio-chemotherapy and survival in unresectable squamous head and neck carcinoma Radiother. Oncol. 87 2008 195 200
24. M. Salmeri, A. Mencattini, and M. Kakar Respiratory motion prediction by fuzzy logic approaches: ANFIS vs interval type-2 non singleton fuzzy system Computer Assisted Radiology and Surgery Pisa, Italy, June 2012 66 67
25. B.J. Schneider, and S.G. Urba Preoperative chemoradiation for the treatment of locoregional esophageal cancer: the standard of care? Semin. Radiat. Oncol. 17 2007 45 52
26. M. Stahl, M. Stuschke, N. Lehmann, H.J. Meyer, M.K. Walz, S. Seeber, B. Klump, W. Budach, R. Teichmann, M. Schmitt, G. Schmitt, C. Franke, and H. Wilke Chemoradiation with and without surgery in patients with locally advanced squamous cell carcinoma of the esophagus J. Clin. Oncol. 23 2005 2310 2317
27. M. Suntharalingam Definitive chemoradiation in the management of locally advanced esophageal cancer Semin. Radiat. Oncol. 17 2007 22 28
28. C.Y. Wang, M.J. Hsieh, Y.C. Chiu, S.H. Li, H.W. Huang, F.M. Fang, and Y.J. Huang Higher serum C-reactive protein concentration and hypoalbuminemia are poor prognostic indicators in patients with esophageal cancer undergoing radiotherapy Radiother. Oncol. 92 2009 270 275
29. C.Y. Wang, T.F. Lee, C.H. Fang, and J.H. Chou Fuzzy logic-based prognostic score for outcome prediction in esophageal cancer IEEE Trans. Inf. Technol. Biomed. 16 2012 1224 1230
30. Q.R. Xu, X.P. Zhuge, H.L. Zhang, Y.M. Ping, and L.Q. Chen The N-classification for esophageal cancer staging: should it be based on number, distance, or extent of the lymph node metastasis? World J. Surg. 35 2011 1303 1310
31. K. Yuvaraj, and U.S. Ragupathy Computer aided segmentation and classification of mass in mammographic images using ANFIS Eur. J. Biomed. Inf. 9 2013 37 41
32. L.A. Zadeh Fuzzy sets Inf. Control 8 1965 338 353