Neural networks significantly improve cancer staging accuracy

Harry Burke*, Philip Goodman, David Rosen

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


Cancer outcome prediction has been largely based on the pTNM staging system but this system presents two problems: (1) low accuracy rate and (2) no room for accuracy improvement because addition of predictive variables increases complexity. In this light, a comparison was made on the predictive accuracy of the current pTNM stage to a backpropagation neural network, for five-year breast cancer survival. This undertaking used the c-index as the measure of accuracy, which ranges from 0.5 (chance) to 1.0 (perfect prediction). Under the same variables, the pTNM stage system had a c-index of 0.69, while the backpropagation neural network scored 0.73, indicating better prediction accuracy.

Original languageEnglish
JournalProceedings of the IEEE Symposium on Computer-Based Medical Systems
StatePublished - 1994
EventProceedings of the 1994 IEEE 7th Symposium on Computer-Based Medical Systems - Winston-Salem, NC, USA
Duration: 11 Jun 199412 Jun 1994


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