Although radical prostatectomy has been the mainstay of treatment for men with clinically organ-confined prostate cancer, a certain proportion of men undergoing radical prostatectomy fail to achieve a complete cure of this disease; that is, postoperative biochemical recurrence develops in approximately 30% of men, some of whom will ultimately die of disease progression.
A number of studies, therefore, have been carried out to identify factors reflecting prognostic outcomes following radical prostatectomy, which would be potentially helpful for properly counseling individual patients undergoing this surgery. Furthermore, various types of model systems using multiple clinicopathological parameters, such as the nomogram, look-up table and artificial neural network, have been shown to have better performance in postoperative prognostic prediction than the opinions of expert clinicians. However, there have not been any standard models uniformly applied to postoperative prognostic prediction, which could be explained, at least in part, by the use of conventional clinicopathological parameters alone, suggesting the need for the additional evaluation of molecular markers simultaneously considering the unique biological features of prostate cancer. In this review, a search of the literature was carried out focusing on the significance of prognostic models following radical prostatectomy, and it is suggested that these models could be promising tools to provide accurate information on the postoperative clinical course of prostate cancer patients. To widely introduce such models into clinical practice, it is necessary to further improve currently available models and develop more reliable, flexible, simple and easily accessible tools by incorporating conventional clinicopathological factors as well as molecular biomarkers.
Written by:
Miyake H, Fujisawa M. Are you the author?
Division of Urology, Kobe University Graduate School of Medicine, Kobe, Japan.
Reference: Int J Urol. 2012 Sep 30. Epub ahead of print.
doi: 10.1111/j.1442-2042.2012.03175.x
PubMed Abstract
PMID: 23020893
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