Artificial neural networks for diagnosis and prognosis in prostate cancer.

Abstract

The application of artificial neural networks (ANNs), especially feed-forward neural networks (FFNNs), has become very popular for diagnosis and prognosis in clinical medicine, often accompanied by exaggerated statements of their potential. The excitement stems mainly from the fact that ANNs were developed as attempts to model the decision process of the human brain. Traditionally, logistic regression models and proportional hazard regression models have been used in these applications. In this article, FFNNs are introduced as flexible, nonlinear regression models and necessary precautions for their use are discussed. Furthermore, the results of a literature survey of applications of ANNs in prostate cancer published between 1999 and 2001 are described; most applications suffer from methodologic deficiencies. It is concluded that there is so far no evidence that the application of ANNs provide real progress in the field of diagnosis and prognosis in prostate cancer.