PURPOSE: We performed a head-to-head comparison of the PHI (Prostate Health Index) and PCA3. MATERIALS AND METHODS: We evaluated PHI and PCA3 performance in 211 patients undergoing initial (116) or repeat (95) prostate biopsy. Multivariable logistic regression analysis was done using the AUC to test the accuracy of PHI and PCA3 for predicting prostate cancer in the overall population and in each setting. Decision curve analysis was used to compare the clinical benefit of different models. RESULTS: Overall, the AUC of the PHI (0.70) was significantly higher than the AUC of PCA3 (0.59), total prostate specific antigen (0.56) and free-to-total prostate specific antigen (0.60) (p = 0.043, 0.002 and 0.037, respectively). PHI was more accurate than PCA3 for predicting prostate cancer in the initial setting (AUC 0.69 vs 0.57) and in the repeat setting (AUC 0.72 vs 0.63), although no statistically significant difference was observed. Including PCA3 in the base multivariable model (prostate specific antigen plus free-to-total prostate specific antigen plus prostate volume) did not increase predictive accuracy in either setting (AUC 0.79 vs 0.80 and 0.75 vs 0.76, respectively). Conversely, including PHI in the base multivariable model improved predictive accuracy by 5% (AUC 0.79 to 0.84) and 6% (AUC 0.75 to 0.81) in the initial and repeat prostate biopsy settings, respectively. On decision curve analysis the highest net benefit was observed when PHI was added to the base multivariable model. CONCLUSIONS: PHI and PCA3 provide a significant increase in sensitivity and specificity compared to all other examined markers and they may help guide biopsy decisions. PCA3 does not increase the accuracy of predicting prostate cancer when PHI is assessed.
PURPOSE: We performed a head-to-head comparison of the PHI (Prostate Health Index) and PCA3. MATERIALS AND METHODS: We evaluated PHI and PCA3 performance in 211 patients undergoing initial (116) or repeat (95) prostate biopsy. Multivariable logistic regression analysis was done using the AUC to test the accuracy of PHI and PCA3 for predicting prostate cancer in the overall population and in each setting. Decision curve analysis was used to compare the clinical benefit of different models. RESULTS: Overall, the AUC of the PHI (0.70) was significantly higher than the AUC of PCA3 (0.59), total prostate specific antigen (0.56) and free-to-total prostate specific antigen (0.60) (p = 0.043, 0.002 and 0.037, respectively). PHI was more accurate than PCA3 for predicting prostate cancer in the initial setting (AUC 0.69 vs 0.57) and in the repeat setting (AUC 0.72 vs 0.63), although no statistically significant difference was observed. Including PCA3 in the base multivariable model (prostate specific antigen plus free-to-total prostate specific antigen plus prostate volume) did not increase predictive accuracy in either setting (AUC 0.79 vs 0.80 and 0.75 vs 0.76, respectively). Conversely, including PHI in the base multivariable model improved predictive accuracy by 5% (AUC 0.79 to 0.84) and 6% (AUC 0.75 to 0.81) in the initial and repeat prostate biopsy settings, respectively. On decision curve analysis the highest net benefit was observed when PHI was added to the base multivariable model. CONCLUSIONS: PHI and PCA3 provide a significant increase in sensitivity and specificity compared to all other examined markers and they may help guide biopsy decisions. PCA3 does not increase the accuracy of predicting prostate cancer when PHI is assessed.
Authors: L M Wong; D E Neal; A Finelli; S Davis; C Bonner; J Kapoor; J Trachtenberg; B Thomas; C M Hovens; A J Costello; N M Corcoran Journal: Prostate Cancer Prostatic Dis Date: 2015-02-10 Impact factor: 5.554
Authors: Marianne Schmid; Quoc-Dien Trinh; Markus Graefen; Margit Fisch; Felix K Chun; Jens Hansen Journal: World J Urol Date: 2014-05-14 Impact factor: 4.226