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An examination of the dynamic changes in prostate‐specific antigen occurring in a population‐based cohort of men over time
Author(s) -
Inman Brant A.,
Zhang Jingyu,
Shah Nilay D.,
Denton Brian T.
Publication year - 2012
Publication title -
bju international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.773
H-Index - 148
eISSN - 1464-410X
pISSN - 1464-4096
DOI - 10.1111/j.1464-410x.2011.10925.x
Subject(s) - prostate cancer , medicine , prostate specific antigen , cohort , receiver operating characteristic , population , oncology , prostate , logistic regression , cancer , gynecology , urology , environmental health
Study Type – Diagnosis (exploratory cohort) Level of Evidence 2b What's known on the subject? and What does the study add? A single serum PSA measurement is commonly used as a screening test to identify men with prostate cancer. A rise in PSA over time may identify men at increased risk of prostate cancer. Dynamic measures of PSA change (ex: PSA velocity, PSA doubling time) are frequently used to justify prostate biopsy in men. We demonstrate that the current serum PSA is the best predictor of future prostate cancer risk among commonly available clinical variables. We show that dynamic measures of PSA change do not improve upon PSA's ability to predict future prostate cancer. Our study suggests that dynamic measures of PSA change may not be useful in screening for prostate cancer. OBJECTIVE• To determine whether prostate‐specific antigen velocity (PSA‐V), PSA doubling time (PSA‐DT), or PSA percentage change (PSA‐PC) add incremental information to PSA alone for community‐based men undergoing prostate cancer (PCa) screening.PARTICIPANTS AND METHODS• A population‐based cohort of 11 872 men from Olmsted County, MN undergoing PSA screening for PCa from 1993 to 2005 was analysed for PSA, PSA‐DT, PSA‐PC and PSA‐V and subsequent PCa. • Receiver‐operating characteristics curves and logistic regression were used to calculate the area under the curve (AUC) and Aikaike's information criterion. • Reclassification analysis was performed and the net reclassification improvement and integrated discrimination improvement were measured. • The method of Begg and Greenes was used to adjust for verification bias.RESULTS• The single best predictor of future PCa was PSA (AUC = 0.773) with PSA‐V (AUC = 0.729) and PSA‐DT/PSA‐PC (AUC = 0.689) performing worse. • After age adjustment, combining PSA with PSA‐V (AUC = 0.773) or PSA‐DT/PSA‐PC (AUC = 0.773) resulted in no better predictions than PSA alone. • Reclassification analysis showed that adding PSA‐V or PSA‐DT/PSA‐PC to PSA did not result in a meaningful amount of reclassification.CONCLUSIONS• PSA is a better predictor of future PCa than PSA‐V, PSA‐DT, or PSA‐PC. • Adding PSA‐V, PSA‐DT, or PSA‐PC to PSA does not result in clinically relevant improvements in the ability to predict future PCa.