Table of Contents
Advances in Artificial Neural Systems
Volume 2010, Article ID 309841, 11 pages
http://dx.doi.org/10.1155/2010/309841
Research Article

Comparison of Artificial Neural Network with Logistic Regression as Classification Models for Variable Selection for Prediction of Breast Cancer Patient Outcomes

1THEMIS, 60 avenue Rockefeller, 69 008 LYON, France
2ICTA-PM, 11 rue du Bocage, 21 121 Fontaine les Dijon, France
3Laboratoire ERIC, Ecole Polytechnique Universitaire de Lyon 1, 69622 Villeurbanne, France
4School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
5Oncology Department, Pfizer France, Avenue Dr. Lannelongue, 75014 Paris, France
6Centre Léon Bérard, 28 rue Laennec, 69373 Lyon Cedex 08, France

Received 4 February 2010; Accepted 5 June 2010

Academic Editor: Tomasz G. Smolinski

Copyright © 2010 Valérie Bourdès et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The aim of this study was to compare multilayer perceptron neural networks (NNs) with standard logistic regression (LR) to identify key covariates impacting on mortality from cancer causes, disease-free survival (DFS), and disease recurrence using Area Under Receiver-Operating Characteristics (AUROC) in breast cancer patients. From 1996 to 2004, 2,535 patients diagnosed with primary breast cancer entered into the study at a single French centre, where they received standard treatment. For specific mortality as well as DFS analysis, the ROC curves were greater with the NN models compared to LR model with better sensitivity and specificity. Four predictive factors were retained by both approaches for mortality: clinical size stage, Scarff Bloom Richardson grade, number of invaded nodes, and progesterone receptor. The results enhanced the relevance of the use of NN models in predictive analysis in oncology, which appeared to be more accurate in prediction in this French breast cancer cohort.