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This paper assesses the value of general regression neural networks in the analysis of clinical and experimental prognostic factors and in the prediction of response to treatment and outcome in prostate cancer. 38 patients are considered in this study. The investigation includes a number of established and experimental factors with 3 clinical outcomes: (a) no response to initial treatment, (b) disease relapse and progression, and (c) sustained complete response to treatment. An overall classification rate of 89.5% is achieved together with equally high sensitivity and specificity rates. The results obtained by means of the neural approach offer a significant improvement over those derived through classical univariate and multivariate statistical analyses.

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145 - 150