Computer-assisted diagnostics: application to prostate cancer.

Abstract

Artificial neural networks (ANNs) have only recently been applied to solve problems in the diagnosis, staging, and prediction of treatment outcome in prostate cancer. A literature search provided information on 10 published journal articles that were selected for review and analysis. In all but one of the studies that compared the ANN output with logistic regression modeling, the ANN performed better. Specific training issues for neural networks are discussed and examples provided. We conclude that the continued development and refinement of computer-assisted diagnostic methodology are warranted to enhance conventional statistical approaches to the classification and pattern recognition found in data sets from men either suspected of having or known to have prostate cancer.