PMID: 11309764Apr 20, 2001Paper

Impact of different variables on the outcome of patients with clinically confined prostate carcinoma: prediction of pathologic stage and biochemical failure using an artificial neural network

Cancer
A M ZiadaE D Crawford

Abstract

The advent of advanced computing techniques has provided the opportunity to analyze clinical data using artificial intelligence techniques. This study was designed to determine whether a neural network could be developed using preoperative prognostic indicators to predict the pathologic stage and time of biochemical failure for patients who undergo radical prostatectomy. The preoperative information included TNM stage, prostate size, prostate specific antigen (PSA) level, biopsy results (Gleason score and percentage of positive biopsy), as well as patient age. All 309 patients underwent radical prostatectomy at the University of Colorado Health Sciences Center. The data from all patients were used to train a multilayer perceptron artificial neural network. The failure rate was defined as a rise in the PSA level > 0.2 ng/mL. The biochemical failure rate in the data base used was 14.2%. Univariate and multivariate analyses were performed to validate the results. The neural network statistics for the validation set showed a sensitivity and specificity of 79% and 81%, respectively, for the prediction of pathologic stage with an overall accuracy of 80% compared with an overall accuracy of 67% using the multivariate regression analys...Continue Reading

References

Oct 8, 1987·The New England Journal of Medicine·T A StameyE Redwine
Oct 1, 1993·British Journal of Urology·G R Fournier, P Narayan
Jan 1, 1993·Scandinavian Journal of Urology and Nephrology·K V Pedersen, A Herder
Jan 1, 1993·Seminars in Roentgenology·P Ramchandani, M D Schnall

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