Nomograms and instruments for the initial prostate evaluation: the ability to estimate the likelihood of identifying prostate cancer.

Sidney Kimmel Center for Prostate and Urologic Cancers, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA.
Seminars in Urologic Oncology 06/2002; 20(2):116-22. DOI: 10.1053/suro.2002.32520
Source: PubMed

ABSTRACT As a result of prostate cancer screening programs, approximately 10% of otherwise healthy men will be found to have an elevated prostate-specific antigen (PSA) level and therefore be at risk for harboring prostate cancer. Patients with an elevated PSA level have a wide variation in the risk for having prostate cancer diagnosed by transrectal ultrasound (TRUS)-guided prostate biopsy. To adequately counsel these patients, some form of individualized risk assessment must be given. There are several tables, artificial neural network (ANN) models, and nomograms that are available to stratify an individual patients risk for having prostate cancer diagnosed by a TRUS biopsy, either initially or on subsequent biopsies after a previous negative biopsy. Presently, nomograms are also being developed to predict the risk not only for having prostate cancer but also for clinically significant prostate cancer. The difficulty in calculating this risk for an individual patient is that the multiple competing clinical and pathologic factors have varying degrees of effect on the overall risk. This problem of competing risk factors can be overcome by the use of nomograms or ANNs. This article reviews the available instruments that are available to the urologist to enable prediction of the risk for having prostate cancer diagnosed by TRUS-guided prostate biopsy.

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    ABSTRACT: Artificial neural networks (ANN) represent an interesting alternative methodological approach to predict prostate cancer related outcomes on an individual basis. Constructed by flexible, nonlinear regression models, they have the potential to offer enhanced goodness-of-fit and predictive ability over traditional linear models. However, this potential comes at the price of increased complexity in modeling building and validation. Therefore, despite their promotion in the field of prostate cancer since the early 1990 s, due to less transparency and the need for computational infrastructure, ANNs have become less popular. Their utility hinges on appropriate implementation and validation, which unfortunately is only recently being recognized and addressed. In this chapter, the reader is provided with a descriptive and an analytic tabulation of decision aid criteria for ANNs for prostate cancer detection.