%[-2]proPSA and "prostate health index" (PHI) improve the diagnostic accuracy for clinically relevant prostate cancer at initial and repeat biopsy compared to t-PSA and %f-PSA in men ≤ 65 years old - Abstract

AIM: %[-2]proPSA and "prostate health index" (PHI) improve the diagnostic accuracy for clinically relevant prostate cancer at initial and repeat biopsy compared to t-PSA and %f-PSA in men ≤ 65 years old.

OBJECTIVES: To prospectively test the diagnostic accuracy of %[-2]proPSA and PHI and to determine its role for discrimination between significant and insignificant prostate cancer (PCa) at initial and repeat prostate biopsy in men ≤ 65 years.

PATIENTS AND METHODS: The diagnostic performance of %[-2]proPSA and PHI were evaluated in a multicenter study. A total of 769 men ≤ 65 years old scheduled for initial or repeat prostate biopsy were recruited in four sites based on t-PSA level 1.6-8.0 ng/ml WHO-calibrated (2-10 ng/ml Hybritech-calibrated). Serum samples were measured for the concentration of t-PSA, f-PSA and [-2]proPSA with Beckman Coulter immunoassays on Access-2- or DxI800-instruments. PHI was calculated as ([-2]proPSA/f-PSA) x √t-PSA). Uni- and multivariable logistic regression models and an artificial neural network (ANN) were complemented by decision curve analysis (DCA).

RESULTS: In univariate analysis %[-2]proPSA and PHI were best predictors of PCa detection in all patients (AUC: 0.72 and 0.73), at initial (AUC: 0.67 and 0.69) and repeat biopsy (AUC: 0.74 and 0.74). t-PSA and %f-PSA performed less accurate for all patients (AUC: 0.54 and 0.62). For detection of significant PCa (based on PRIAS-criteria) %[-2]proPSA and PHI equally demonstrated best performance (AUC: 0.70 and 0.73) compared with t-PSA and %f-PSA (AUC: 0.54 and 0.59). In multivariate analysis PHI added to a base model of age, prostate volume, DRE, t-PSA and %f-PSA. PHI was strongest in predicting PCa in all patients, at initial and repeat biopsy and for significant PCa (AUC: 0.73, 0.68, 0.78 and 0.72, respectively). In DCA for all patients the artificial neural network (ANN) showed the broadest threshold probability and best net benefit. PHI as single parameter and the base model + PHI were equivalent with threshold probability and net benefit nearing those of the ANN. For significant cancers the ANN was the strongest parameter in DCA.

CONCLUSION: This multicenter study showed that %[-2]proPSA and PHI have a superior diagnostic performance for detecting PCa in PSA range of 1.6-8.0 ng/ml compared with t-PSA and %f-PSA at initial and repeat biopsy and for predicting significant PCa in men ≤ 65 years old. They are equally superior for counseling patients prior to biopsy.

Written by:
Boegemann M, Stephan C, Cammann H, Vincendeau S, Houlgatte A, Jung K, Blanchet JS, Semjonow A.   Are you the author?
Department of Urology, Prostate Center, University Clinic Muenster, Germany.

Reference: BJU Int. 2015 Mar 28. Epub ahead of print.
doi: 10.1111/bju.13139


PubMed Abstract
PMID: 25818705

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