Background: Quantitative methods for the analysis of prognostic inform
ation are important in order to use this knowledge optimally. The neur
al network is a new quantitative method where the fundamental building
blocks are units which can be likened to neurons, and weighted connec
tions which can be likened to synapses. The more the hidden units, the
more complex the patterns that can be learnt. Materials and methods:
Data from two Dutch studies in ovarian cancer were used to compare the
previously reported survival rates predicted by the Cox's prognostic
index with the prediction obtained by a neural network. Results: Both
the Cox's analysis and the neural network agreed on residual tumour si
ze, stage, and performance status as being important for survival. The
neural network identified additional predictive factors such as place
of diagnosis and age. As the Cox's prognostic index has not been test
ed to predict survival on an independent data set a comparison with th
e results obtained in the neural network test set could not be perform
ed. Conclusions: Neural networks perform at least as well as Cox's met
hod for the prediction of survival, and prognostic factors can easily
be identified. The analysis not only revealed the predictive power of
some characteristics, but also the non-predictive power of the others.