Prediction of individual patient outcome in cancer: Comparison of artificial neural networks and Kaplan-Meier methods

BACKGROUND. There is a great need for accurate treatment and outcome prediction an cancer. Two methods for prediction, artificial neural networks and Kaplan-Meier plots, have not, to the authors knowledge, been compared previously. METHODS. This review compares the advantages and disadvantages of the use of artificial neural networks and Kaplan-Meier curves for treatment and outcome prediction in cancer. RESULTS. Artificial neural networks are useful for prediction of outcome for individual patients with cancer because they are as accurate as the best traditional statistical methods, are able to capture complex phenomena without a priori knowledge, and can be reduced to a simpler model if the phenomena are not complex. Kaplan-Meier plots are of limited accuracy for prediction because they require partitioning of variables, require cutting continuous variables into discrete pieces, and can only handle one or two variables effectively. CONCLUSIONS. Artificial neural networks are an efficient statistical method for outcome prediction in cancer that utilizes all available powerful prognostic factors and maximizes predictive accuracy. Use of Kaplan-Meier plots for predictions is discouraged because of serious technical limitations and low accuracy.