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.
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.
Authors: Stacy Loeb; H Ballentine Carter; Edward M Schaeffer; Anna Kettermann; Luigi Ferrucci; E Jeffrey Metter Journal: Urology Date: 2011-01 Impact factor: 2.649
Authors: David Connolly; Amanda Black; Liam J Murray; Thiagarajan Nambirajan; Patrick F Keane; Anna Gavin Journal: BJU Int Date: 2008-03-13 Impact factor: 5.588
Authors: Ruth D Etzioni; Donna P Ankerst; Noel S Weiss; Lurdes Y T Inoue; Ian M Thompson Journal: J Natl Cancer Inst Date: 2007-10-09 Impact factor: 13.506
Authors: Andrew J Vickers; Tineke Wolters; Caroline J Savage; Angel M Cronin; M Frank O'Brien; Kim Pettersson; Monique J Roobol; Gunnar Aus; Peter T Scardino; Jonas Hugosson; Fritz H Schröder; Hans Lilja Journal: Eur Urol Date: 2009-08-07 Impact factor: 20.096