GRNN ensemble classifier for lung cancer prognosis using only demographic and TNM features

Predicting the recurrence of non-small cell lung cancer remains a clinical challenge. The current best practice employs heuristic decisions based on the TNM classification scheme that many believe can be improved upon. Much research has recently been devoted to searching for gene signatures derived from gene expression microarrays for this challenge, but a consensus signature is still elusive. We present an approach to first create a benchmark for recurrence prediction based only upon gender, age and TNM features that uses several learning classifier induction methods and combines them into an ensemble using a recent extension of the general regression neural network. Using this approach on a pooled sample of 422 patients from two previously published studies (Shedden and Raponi), we demonstrate error rates in the low 20% for both false positives and negatives. Future work will focus on discovering if gene signatures can be discovered that can improve this performance.