Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks

Author: Jerez J.M.   Franco L.   Alba E.   Llombart-Cussac A.   Lluch A.   Ribelles N.   Munárriz B.   Martín M.  

Publisher: Springer Publishing Company

ISSN: 0167-6806

Source: Breast Cancer Research and Treatment, Vol.94, Iss.3, 2005-12, pp. : 265-272

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Abstract

The objective of this study is to compare the predictive accuracy of a neural network (NN) model versus the standard Cox proportional hazard model. Data about the 3811 patients included in this study were collected within the ‘El Álamo’ Project, the largest dataset on breast cancer (BC) in Spain. The best prognostic model generated by the NN contains as covariates age, tumour size, lymph node status, tumour grade and type of treatment. These same variables were considered as having prognostic significance within the Cox model analysis. Nevertheless, the predictions made by the NN were statistically significant more accurate than those from the Cox model (p</i><0.0001). seven="" different="" time="" intervals="" were="" also="" analyzed="" to="" find="" that="" the="" nn="" predictions="" were="" much="" more="" accurate="" than="" those="" from="" the="" cox="" model="" in="" particular="" in="" the="" early="" intervals="" between="" 1–10="" and="" 11–20 months,="" and="" in="" the="" later="" one="" considered="" from="" 61 months="" to="" maximum="" follow-up="" time="" (mft).="" interestingly,="" these="" intervals="" contain="" regions="" of="" high="" relapse="" risk="" that="" have="" been="" observed="" in="" different="" studies="" and="" that="" are="" also="" present="" in="" the="" analyzed="" dataset.=""></0.0001).>