Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

PURPOSE OF REVIEW: To delineate how recent findings on prostate-specific antigen (PSA) can improve prediction of risk, detection, and prediction of clinical endpoints of prostate cancer (PCa). RECENT FINDINGS: The widely used PSA cut-point of 4.0 ng/ml increasingly appears arbitrary, but no cut-point achieves both high sensitivity and high specificity. The accuracy of detecting PCa can be increased by additional predictive factors and a combinations of markers. Evidence implies that a panel of kallikrein markers improves the specificity and reduces costs by eliminating unnecessary biopsies. Large, population-based studies have provided evidence that PSA can be used to predict PCa risk many years in advance, improve treatment selection and patient care, and predict the risk of complications and disease recurrence. However, definitive evidence is currently lacking as to whether PSA screening lowers PCa -specific mortality. SUMMARY: PSA is still the main tool for early detection, risk stratification, and monitoring of PCa. However, PSA values are affected by many technical and biological factors. Instead of using a fixed PSA cut-point, using statistical prediction models and considering the integration additional markers may be able to improve and individualize PCa diagnostics. A single PSA measurement at early middle age can predict risk of advanced PCa decades in advance and stratify patients for intensity of subsequent screening.

Original publication

DOI

10.1097/MOU.0b013e32832a2d10

Type

Journal article

Journal

Curr Opin Urol

Publication Date

05/2009

Volume

19

Pages

221 - 226

Keywords

Humans, Male, Predictive Value of Tests, Prognosis, Prostate-Specific Antigen, Prostatic Neoplasms, Risk Factors, Sensitivity and Specificity