Summary
Neural Network Analysis, a form of artificial intelligence, was successfully used to predict the clinical outcome of node-positive breast cancer patients. A Neural Network was trained to predict clinical outcome using prognostic information from 1008 patients. During training, the network received as input information tumor hormone receptor status, DNA index and S-phase determination by flow cytometry, tumor size, number of axillary lymph nodes involved with tumor, and age of the patient, as well as lengtl of clinical followup, relapse status, and time of relapse. The ability of the trained Network to determine relapse probability was then validated in a separate set of 960 patients. The Neural Network was as powerful as Cox Regression Modeling inidentifying breast cancer patients at high and low risk for relapse.
References
McGuire WL, Tandon AT, Allred DC, Chamness GC, Clark GM: How to use prognostic factors in axillary node-negative breast cancer patients. J Natl Cancer Inst 82:1006–1015, 1990.
Cox DR: Regression models and life-tables. JR Stat Soc [B] 34:187–220, 1972.
Ciampi A, Lawless JF, McKinney SM, Singhal K: Regression and recursive partition strategies in the analysis of medical survival data. J Clin Epidemiol 41:737–748, 1988.
Ravdin PM, Clark GM, Tandon AK, McGuire WL: Predicting recurrence in axillary node-negative breast cancer patients using adaptive artificial intelligence. Breast Cancer Res Treat 16:190, 1990.
Qian N, Sejnowski TJ: Predicting the secondary structure of globular proteins using neural network models. J Mol Biol 202:865–884, 1988.
Bohr H, Bohr J, Brunak S, et al: Protein secondary structure and homology by neural network analysis. FEBS Lett 241:223–228, 1988.
Sejnowski TJ, Rosenberg CR: Net talk: A parallel network that learns to read aloud. Technical Report JHU/EECS-86-01, Johns Hopkins University, 1986.
Rumelhart DE, Hinton GE, Williams RJ: Learning representations by back propagating errors. Nature 323:533–536, 1986.
Clark GM, Dressler LG, Owens MA, et al: Prediction of relapse or survival in patients with node-negative breast cancer by DNA flow cytometry. N Engl J Med 320:627–633, 1989.
Cutler SJ, Ederer F: Maximum utilization of the life table method in analyzing survival. J Chronic Diseases 8:699–712, 1958.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Ravdin, P.M., Clark, G.M., Hilsenbeck, S.G. et al. A demonstration that breast cancer recurrence can be predicted by Neural Network analysis. Breast Cancer Res Tr 21, 47–53 (1992). https://doi.org/10.1007/BF01811963
Issue Date:
DOI: https://doi.org/10.1007/BF01811963