A practical application of neural network analysis for predicting outcome of individual breast cancer patients.

It has been previously shown that Neural Networks can be trained to recognize individual breast cancer patients at high and low risk for recurrent disease and death. This paper expands on the initial investigation and shows that by coding time as one of the prognostic variables, a Neural Network can use censored survival data to predict patient outcome over time. In this demonstration a Neural Network was trained, tested, and validated using censored survival data from a group of 1373 patients with node-positive breast cancer. The Neural Network method predicted patient outcome as accurately as Cox Regression modeling. The final Neural Network model can be presented with a patient's prognostic information and make a series of predictions about probability of relapse at different times of follow-up, allowing one to draw survival probability curves for individual patients.