Advanced ovarian cancer. Neural network analysis to predict treatment outcome.
BACKGROUND
Quantitative methods for the analysis of prognostic information are important in order to use this knowledge optimally. The neural network is a new quantitative method where the fundamental building blocks are units which can be likened to neurons, and weighted connections which can be likened to synapses. The more the hidden units, the more complex the patterns that can be learnt.


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 size, 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 tested to predict survival on an independent data set a comparison with the results obtained in the neural network test set could not be performed.


CONCLUSIONS
Neural networks perform at least as well as Cox's method 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.
Kappen
H J
HJ
University of Nijmegen, Laboratorium for Medical and Biophysics, The Netherlands.
Neijt
J P
JP
D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use; D005260:Female; D006801:Humans; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D010051:Ovarian Neoplasms / Q000188:drug therapy* / Q000401:mortality*; D011237:Predictive Value of Tests; D011379:Prognosis; D013534:Survival; D016896:Treatment Outcome