Comparison of genetic algorithm neural network with logistic regression for predicting outcome after surgery for patients with nonsmall cell lung carcinoma

Cancer

Volumn 79, Issue 7, 1997, Pages 1338-1342

  Jefferson, Miles F ^a   Pendleton, Neil ^a   Lucas, Sam B ^b   Horan, Michael A ^a,b  

^a HOPE HOSPITAL   (United Kingdom)

^b UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

genetic algorithms; logistic regression; lung carcinoma; neural networks; outcome

Indexed keywords

ACCURACY; ADULT; AGED; ALGORITHM; ARTICLE; CANCER SURGERY; FEMALE; HUMAN; INTERMETHOD COMPARISON; LUNG NON SMALL CELL CANCER; MAJOR CLINICAL STUDY; MALE; MATHEMATICAL MODEL; PRIORITY JOURNAL; REGRESSION ANALYSIS; TREATMENT OUTCOME; TUMOR VOLUME;

AGED; CARCINOMA, NON-SMALL-CELL LUNG; FEMALE; HUMANS; LOGISTIC MODELS; LUNG NEOPLASMS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); TREATMENT OUTCOME;

EID: 0030951433     PISSN: 0008543X     EISSN: None     Source Type: Journal    
DOI: 10.1002/(SICI)1097-0142(19970401)79:7<1338::AID-CNCR10>3.0.CO;2-0     Document Type: Article

References (21)

1. Cross SS, Harrison RF, Kennedy RL. Introduction to neural networks. Lancet 1995;346:1075-9.
2. Baxt WG. Application of neural networks to clinical medicine. Lancet 1995;346:1135-8.
3. Goldberg DE. Genetic algorithms in search, optimization and machine learning. 1st edition. Reading, MA: Addison-Welsey Publishing Co., 1989.
4. Narayanan MN, Lucas SB. A genetic algorithm to improve a neural network to predict a patient's response to warfarin. Methods inf Med 1993;32:55-8.
5. Jefferson MF, Narayanan MN, Lucas SB. A neural network computer method to model the INR response of individual patients anticoagulated with warfarin. Br J Haematol 1995;89(1):29.
6. Macartney FJ. Diagnostic logic. BMJ 1987;295:1325-31.
7. Pendleton N, Jefferson MF, Dixon GR, Myskow MW, Horan MA. Correlates of tumour size and metastasis of resected non small cell lung cancer (NSCLC) with age. J Gerontol 1996;51A:B50-3.
8. Wyatt JC, Altman DG. Commentary. Prognostic models: clinically useful or quickly forgotten? BMJ 1995;311:1539-41.
9. Altman DG, De Stavola BL, Love SB, Stepniewska KA. Review of survival analyses published in cancer journals. Br J Cancer 1995;72:511-8.
10. Hart A, Wyatt J. Evaluating black-boxes as medical decision aids: issues arising from a study of neural networks. Med Inf (London) 1990;15:229-36.
11. Jefferson MF, Pendleton N, Lucas SB. Neural networks. Lancet 1995;346:1712.
12. Mountain CF. Lung cancer stage in classification. Clin Chest Med 1993;14:43-53.
13. UICC (International Union Against Cancer). TNM classification of malignant tumours. 4th edition Hermanek P, Sobin LH, editors. Berlin: Springer-Verlag, 1987.
14. World Health Organization. The World Health Organization histological typing of lung tumours. Am J Clin Pathol 1982;77:123-36.
15. Splinter TAW. Therapy for small cell and non-small cell lung cancer. Curr Opin Oncol 1992;4:315-22.
16. Martini N. Surgical treatment of lung cancer. Semin Oncol 1990;17:9-10.
17. Sheilds TW. Surgical therapy for carcinoma of the lung. Clin Chest Med 1993;14:121-47.
18. Mountain CF, Greenberg SD, Fraire AE. Tumour stage in non-small cell carcinoma of the lung. Chest 1991;99:1258-60.
19. Greenberg SD, Fraire AE, Kinner DM, Johnson EH. Tumour cell type versus staging in the prognosis of carcinoma of the lung. Pathol Annu 1987;22:387-405.
20. Lederle FA, Neiwoehner DE. Lung cancer surgery, a critical review of the evidence. Arch Intern Med 1995;154:2397-400.
21. Burke HB, Goodman PH, Rosen DB, Henson DE, Weinstein JN, Harrell FE, et al. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer 1997;79:857-62.