Artificial neural networks applied to survival prediction in breast cancer

M. Lundin*, Johan Lundin, H. B. Burke, S. Toikkanen, L. Pylkkänen, H. Joensuu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

116 Scopus citations


In this study, we evaluated the accuracy of a neural network in predicting 5-, 10- and 15-year breast-cancer-specific survival. A series of 951 breast cancer patients was divided into a training set of 651 and a validation set of 300 patients. Eight variables were entered as input to the network: tumor size, axillary nodal status, histological type, mitotic count, nuclear pleomorphism, tubule formation, tumor necrosis and age. The area under the ROC curve (AUC) was used as a measure of accuracy of the prediction models in generating survival estimates for the patients in the independent validation set. The AUC values of the neural network models for 5-, 10- and 15-year breast-cancer-specific survival were 0.909, 0.886 and 0.883, respectively. The corresponding AUC values for logistic regression were 0.897, 0.862 and 0.858. Axillary lymph node status (NO vs. N+) predicted 5-year survival with a specificity of 71% and a sensitivity of 77%. The sensitivity of the neural network model was 91% at this specificity level. The rate of false predictions at 5 years was 82/300 for nodal status and 40/300 for the neural network. When nodal status was excluded from the neural network model, the rate of false predictions increased only to 49/300 (AUC 0.877). An artificial neural network is very accurate in the 5-, 10- and 15-year breast-cancer-specific survival prediction. The consistently high accuracy over time and the good predictive performance of a network trained without information on nodal status demonstrate that neural networks can be important tools for cancer survival prediction.

Original languageEnglish
Pages (from-to)281-286
Number of pages6
JournalOncology (Switzerland)
Issue number4
StatePublished - Nov 1999
Externally publishedYes


  • Breast cancer
  • Neural networks
  • Survival prediction


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