Artificial neural networks applied to survival prediction in breast cancer

Oncology

Volumn 57, Issue 4, 1999, Pages 281-286

  Lundin, M ^c   Lundin, Johan ^b   Burke, H B ^a   Toikkanen, S ^c   Pylkkanen L ^c   Joensuu, H ^c  

^a NEW YORK MEDICAL COLLEGE   (United States)

^b HELSINKI UNIVERSITY CENTRAL HOSPITAL   (Finland)

^c NONE

Author keywords

Breast cancer; Neural networks; Survival prediction

Indexed keywords

ADULT; AGED; AREA UNDER THE CURVE; ARTICLE; ARTIFICIAL NEURAL NETWORK; AXILLARY LYMPH NODE; BREAST CANCER; CANCER GRADING; CANCER SURVIVAL; CELL NUCLEUS; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC ERROR; FEMALE; HISTOLOGY; HUMAN; HUMAN TISSUE; MAJOR CLINICAL STUDY; MITOSIS; PREDICTION AND FORECASTING; PRIORITY JOURNAL; REGRESSION ANALYSIS; TUMOR NECROSIS;

EID: 0032730109     PISSN: 00302414     EISSN: None     Source Type: Journal    
DOI: 10.1159/000012061     Document Type: Article

References (33)

1. Clark GM, Hilsenbeck SG, Ravdin PM, De Laurentiis M, Osborne CK: Prognostic factors: Rationale and methods of analysis and integration. Breast Cancer Res Treat 1994;32:105-112.
2. Burke HB, Henson DE: The American Joint Committee on Cancer. Criteria for prognostic factors and for an enhanced prognostic system. Cancer 1993;72:3131-3135.
3. Fielding LP, Henson DE: Multiple prognostic factors and outcome analysis in patients with cancer. Communication from the American Joint Committee on Cancer. Cancer 1993;71: 2426-2429.
4. Baxt WG: Application of artificial neural networks to clinical medicine. Lancet 1995;346: 1135-1138.
5. Baxt WG, Skora J: Prospective validation of artificial neural network trained to identify acute myocardial infarction. Lancet 1996;347: 12-15.
6. Dybowski R, Weller P, Chang R, Gant V: Prediction of outcome in critically ill patients using artificial neural network synthesised by genetic algorithm. Lancet 1996;347:1146-1150.
7. Baxt WG: Complexity, chaos and human physiology: The justification for non-linear neural computational analysis. Cancer Lett 1994;77: 85-93.
8. Burke HB: Statistical analysis of complex systems in biomedicine; in Fisher D, Lenz HJ (eds): Learning from Data: AI and Statistics V. Berlin, Springer, 1996, vol 112, pp 251-258.
9. Bottaci L, Drew PJ, Hartley JE, Hadfield MB, Farouk R, Lee PW, Macintyre IM, Duthie GS, Monson JR: Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions. Lancet 1997; 350:469-472.
10. Burke HB, Goodman PH, Rosen DB, Henson DE, Weinstein JN, Harrell FE, Marks JR, Winchester DP, Bostwick DG: Artificial neural networks improve the accuracy of cancer survival prediction. Cancer 1997;79:857-862.
11. Jefferson MF, Pendleton N, Lucas SB, Horan MA: Comparison of a genetic algorithm neural network with logistic regression for predicting outcome after surgery for patients with non-small cell lung carcinoma. Cancer 1997;79: 1338-1342.
12. Cady B, Stone MD, Schuler JG, Thakur R, Wanner MA, Lavin PT: The new era in breast cancer. Invasion, size, and nodal involvement dramatically decreasing as a result of mammographic screening. Arch Surg 1996;131:301-308.
13. Barth A, Craig PH, Silverstein MJ: Predictors of axillary lymph node metastases in patients with T1 breast carcinoma. Cancer 1997;79: 1918-1922.
14. De Laurentiis M, Gallo C, De Placido S, Perrone F, Pettinato G, Petrella G, Carlomagno C, Panico L, Delrio P, Bianco AR: A predictive index of axillary nodal involvement in operable breast cancer. Br J Cancer 1996;73:1241-1247.
15. Ravdin PM, De Laurentiis M, Vendely T, Clark GM: Prediction of axillary lymph node status in breast cancer patients by use of prognostic indicators. J Natl Cancer Inst 1994;86: 1771-1775.
16. Fisher B, Redmond C, Fisher ER, Bauer M, Wolmark N, Wickerham DL, Deutsch M, Montague E, Margolese R, Foster R: Ten-year results of a randomized clinical trial comparing radical mastectomy and total mastectomy with or without radiation. N Engl J Med 1985;312: 674-681.
17. Menard S, Bufalino R, Rilke F, Cascinelli N, Veronesi U, Colnaghi MI: Prognosis based on primary breast carcinoma instead of pathological nodal status. Br J Cancer 1994;70:709-712.
18. World Health Organization: Histological Typing of Breast Tumours, ed 2. Geneva, World Health Organization, 1981.
19. Fahlman SE, Lebiere C: The cascade-correlation learning architecture; in Touretzky DS (ed): Advances in Neural Information Processing Systems. San Mateo, Morgan Kaufmann, 1990, vol 2, pp 524-532.
20. Brown M, An PE, Harris CJ, Wang H: How biased is your multi-layer perceptron? World Congress on Neural Networks. Portland, INNS, 1993, pp 507-511.
21. Metz CE: Basic principles of ROC analysis. Semin Nucl Med 1978;8:283-298.
22. Hanley JA, McNeil BJ: A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983;148:839-843.
23. Doyle HR, Dvorchik I, Mitchell S, Marino IR, Ebert FH, McMichael J, Fung JJ: Predicting outcomes after liver transplantation. A connectionist approach. Ann Surg 1994;219:408-415.
24. Ebell MH: Artificial neural networks for predicting failure to survive following in-hospital cardiopulmonary resuscitation. J Fam Pract 1993;36:297-303.
25. Bryce TJ, Dewhirst MW, Floyd CE Jr, Hars V, Brizel DM: Artificial neural network model of survival in patients treated with irradiation with and without concurrent chemotherapy for advanced carcinoma of the head and neck. Int J Radiat Oncol Biol Phys 1998;41:339-345.
26. Ravdin PM, Clark GM, Hilsenbeck SG, Owens MA, Vendely P, Pandian MR, McGuire WL: A demonstration that breast cancer recurrence can be predicted by neural network analysis. Breast Cancer Res Treat 1992;21:47-53.
27. Tu JV: Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 1996;49:1225-1231.
28. White H: Learning in artificial neural networks. A statistical perspective. Neural Comput 1989;1:425-464.
29. De Laurentiis M, Ravdin PM: A technique for using neural network analysis to perform survival analysis of censored data. Cancer Lett 1994;77:127-138.
30. Faraggi D, Simon R: A neural network model for survival data. Stat Med 1995;14:73-82.
31. Liestol K, Andersen PK, Andersen U: Survival analysis and neural nets. Stat Med 1994;13: 1189-1200.
32. Ohno-Machado L, Walker MG, Musen MA: Hierarchical neural networks for survival analysis. Medinfo 1995;8:828-832.
33. Veronesi U, Paganelli G, Galimberti V, Viale G, Zurrida S, Bedoni M, Costa A, de Cicco C, Geraghty JG, Luini A, Sacchini V, Veronesi P: Sentinel-node biopsy to avoid axillary dissection in breast cancer with clinically negative lymph-nodes. Lancet 1997;349:1864-1867.