The Prostate Cancer Prevention Trial and European Randomized Study of Screening for Prostate Cancer risk calculators indicating a positive prostate biopsy: a comparison
- PMID: 18715246
- DOI: 10.1111/j.1464-410X.2008.07940.x
The Prostate Cancer Prevention Trial and European Randomized Study of Screening for Prostate Cancer risk calculators indicating a positive prostate biopsy: a comparison
Abstract
Objective: To assess the potential problem that different tools for predicting a positive outcome of prostate biopsy can produce divergent outcomes in the same man, by comparing the risk calculators based on the Prostate Cancer Prevention Trial (PCPT) and the European Randomized Study of Screening for Prostate Cancer (ERSPC).
Materials and methods: In the prostate-specific antigen (PSA) range of 0.2-30.0 ng/mL, the prediction curves of 'virtual' standard study participants were evaluated using both prediction tools. The effects of prostate volume, digital rectal examination, transrectal ultrasonography (TRUS), previous negative biopsy, family history, race, and age were also assessed.
Results: Important differences in underlying study design and populations between the PCPT and ERSPC cause an essential discrepancy between the risk calculators. In the PCPT there were few biopsies in the higher PSA ranges, and in the ERSPC in the lower PSA ranges. Both risk indicators have incorporated some variables that are not used in the other, because they were insignificant in multivariate analysis. TRUS and especially prostate volume (not available in the PCPT) have a considerably larger effect on predictions in comparable PSA ranges than race, age, family history of prostate cancer, and previous negative biopsy (indicators that were excluded in ERSPC).
Conclusions: Before using risk calculators users must consider the properties of the underlying populations and what are the included or unavailable risk factors, and compare these to the patient. When these prerequisites are disregarded, dissimilarities will result in grossly inaccurate predictions for individual patients.
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