Neural networks significantly improve cancer staging accuracy

Harry Burke; Philip Goodman; David Rosen

Neural networks significantly improve cancer staging accuracy

Harry Burke^*, Philip Goodman, David Rosen

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

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 language	English
Journal	Proceedings of the IEEE Symposium on Computer-Based Medical Systems
State	Published - 1994
Event	Proceedings of the 1994 IEEE 7th Symposium on Computer-Based Medical Systems - Winston-Salem, NC, USA Duration: 11 Jun 1994 → 12 Jun 1994

Cite this

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title = "Neural networks significantly improve cancer staging accuracy",

abstract = "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.",

author = "Harry Burke and Philip Goodman and David Rosen",

year = "1994",

language = "English",

journal = "Proceedings of the IEEE Symposium on Computer-Based Medical Systems",

issn = "1063-7125",

note = "null ; Conference date: 11-06-1994 Through 12-06-1994",

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T1 - Neural networks significantly improve cancer staging accuracy

AU - Burke, Harry

AU - Goodman, Philip

AU - Rosen, David

PY - 1994

Y1 - 1994

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0028591550&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0028591550

SN - 1063-7125

JO - Proceedings of the IEEE Symposium on Computer-Based Medical Systems

JF - Proceedings of the IEEE Symposium on Computer-Based Medical Systems

Y2 - 11 June 1994 through 12 June 1994

ER -

Neural networks significantly improve cancer staging accuracy

Abstract

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