Neural networks in the prediction of survival in patients with colorectal cancer.

It is important to predict outcome for colorectal cancer patients following surgery, as almost 50% of patients undergoing a potentially curative resection will experience relapse. It is clear that present prognostic categories such as Dukes or TNM staging are too broad, and further refining is required to prognosticate for high-risk subgroups. One approach is to determine a phenotype associated with recurrence. We compared 2 methods of analyzing such data. Pathologic data from a large clinical trial was analyzed for 403 patients. The outcome modeled was disease recurrence. The results from logistic regression analysis and a neural network approach are compared with respect to receiver operator characteristic plots, which estimate the fit of the model. The best logistic regression model gives a result of 66%, and the neural network approach 78%. The conclusion from this study is that the neural network approach is superior to regression analysis. Further analyses are in progress using a larger patient sample size (n > 1000), improved statistical models, and a more refined neural network.