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This paper assesses the value of using artificial 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.


Journal article


Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Publication Date





1003 - 1006