Literature DB >> 11309767

Neural network and regression predictions of 5-year survival after colon carcinoma treatment.

P B Snow1, D J Kerr, J M Brandt, D M Rodvold.   

Abstract

BACKGROUND: The Commission on Cancer data from the National Cancer Data Base (NCDB) for patients with colon carcinoma was used to develop several artificial neural network and regression-based models. These models were designed to predict the likelihood of 5-year survival after primary treatment for colon carcinoma.
METHODS: Two modeling methods were used in the study. Artificial neural networks were used to select the more important variables from the NCDB database and model 5-year survival. A standard parametric logistic regression also was used to model survival and the two methods compared on a prospective set of patients not used in model development.
RESULTS: The neural network yielded a receiver operating characteristic (ROC) area of 87.6%. At a sensitivity to mortality of 95% the specificity was 41%. The logistic regression yielded a ROC area of 82% and at a sensitivity to mortality of 95% gave a specificity of 27%.
CONCLUSIONS: The neural network found a strong pattern in the database predictive of 5-year survival status. The logistic regression produced somewhat less accurate, but good, results. Copyright 2001 American Cancer Society.

Entities:  

Mesh:

Year:  2001        PMID: 11309767     DOI: 10.1002/1097-0142(20010415)91:8+<1673::aid-cncr1182>3.0.co;2-t

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.860


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