BACKGROUND: Urinary prostate cancer gene 3 (PCA3) represents a promising novel marker of prostate cancer detection. OBJECTIVE: To test whether urinary PCA3 assay improves prostate cancer (PCa) risk assessment and to construct a decision-making aid in a multi-institutional cohort with pre-prostate biopsy data. DESIGN, SETTING, AND PARTICIPANTS: PCA3 assay cut-off threshold analyses were followed by logistic regression models which used established predictors to assess PCa-risk at biopsy in a large multi-institutional data set of 809 men at risk of harboring PCa. MEASUREMENTS: Regression coefficients were used to construct four sets of nomograms. Predictive accuracy (PA) estimates of biopsy outcome predictions were quantified using the area under the curve of the receiver operator characteristic analysis in models with and without PCA3. Bootstrap resamples were used for internal validation and to reduce overfit bias. The extent of overestimation or underestimation of the observed PCa rate at biopsy was explored graphically using nonparametric loss-calibration plots. Differences in PA were tested using the Mantel-Haenszel test. Finally, nomogram-derived probability cut-offs were tested to assess the ability to identify patients with or without PCa. RESULTS AND LIMITATIONS: PCA3 was identified as a statistically independent risk factor of PCa at biopsy. Addition of a PCA3 assay improved bootstrap-corrected multivariate PA of the base model between 2% and 5%. The highest increment in PA resulted from a PCA3 assay cut-off threshold of 17, where a 5% gain in PA (from 0.68 to 0.73, p=0.04) was recorded. Nomogram probability-derived risk cut-off analyses further corroborate the superiority of the PCA3 nomogram over the base model. CONCLUSIONS: PCA3 fulfills the criteria for a novel marker capable of increasing PA of multivariate biopsy models. This novel PCA3-based nomogram better identifies men at risk of harboring PCa and assists in deciding whether further evaluation is necessary.
BACKGROUND: Urinary prostate cancer gene 3 (PCA3) represents a promising novel marker of prostate cancer detection. OBJECTIVE: To test whether urinary PCA3 assay improves prostate cancer (PCa) risk assessment and to construct a decision-making aid in a multi-institutional cohort with pre-prostate biopsy data. DESIGN, SETTING, AND PARTICIPANTS: PCA3 assay cut-off threshold analyses were followed by logistic regression models which used established predictors to assess PCa-risk at biopsy in a large multi-institutional data set of 809 men at risk of harboring PCa. MEASUREMENTS: Regression coefficients were used to construct four sets of nomograms. Predictive accuracy (PA) estimates of biopsy outcome predictions were quantified using the area under the curve of the receiver operator characteristic analysis in models with and without PCA3. Bootstrap resamples were used for internal validation and to reduce overfit bias. The extent of overestimation or underestimation of the observed PCa rate at biopsy was explored graphically using nonparametric loss-calibration plots. Differences in PA were tested using the Mantel-Haenszel test. Finally, nomogram-derived probability cut-offs were tested to assess the ability to identify patients with or without PCa. RESULTS AND LIMITATIONS: PCA3 was identified as a statistically independent risk factor of PCa at biopsy. Addition of a PCA3 assay improved bootstrap-corrected multivariate PA of the base model between 2% and 5%. The highest increment in PA resulted from a PCA3 assay cut-off threshold of 17, where a 5% gain in PA (from 0.68 to 0.73, p=0.04) was recorded. Nomogram probability-derived risk cut-off analyses further corroborate the superiority of the PCA3 nomogram over the base model. CONCLUSIONS:PCA3 fulfills the criteria for a novel marker capable of increasing PA of multivariate biopsy models. This novel PCA3-based nomogram better identifies men at risk of harboring PCa and assists in deciding whether further evaluation is necessary.
Authors: Yuanyuan Liang; Jamie C Messer; Christopher Louden; Miguel A Jimenez-Rios; Ian M Thompson; Hector R Camarena-Reynoso Journal: Urol Oncol Date: 2012-02-03 Impact factor: 3.498
Authors: Yuanyuan Liang; Donna P Ankerst; Ziding Feng; Rong Fu; Janet L Stanford; Ian M Thompson Journal: Urol Oncol Date: 2012-05-01 Impact factor: 3.498