Prostate Health Index (Phi) and Prostate Cancer Antigen 3 (PCA3) significantly improve prostate cancer detection at initial biopsy in a total PSA range of 2-10 ng/ml

Abstract

Many efforts to reduce prostate specific antigen (PSA) overdiagnosis and overtreatment have been made. To this aim, Prostate Health Index (Phi) and Prostate Cancer Antigen 3 (PCA3) have been proposed as new more specific biomarkers. We evaluated the ability of phi and PCA3 to identify prostate cancer (PCa) at initial prostate biopsy in men with total PSA range of 2-10 ng/ml. The performance of phi and PCA3 were evaluated in 300 patients undergoing first prostate biopsy. ROC curve analyses tested the accuracy (AUC) of phi and PCA3 in predicting PCa. Decision curve analyses (DCA) were used to compare the clinical benefit of the two biomarkers. We found that the AUC value of phi (0.77) was comparable to those of %p2PSA (0.76) and PCA3 (0.73) with no significant differences in pairwise comparison (%p2PSA vs phi p = 0.673, %p2PSA vs. PCA3 p = 0.417 and phi vs. PCA3 p = 0.247). These three biomarkers significantly outperformed fPSA (AUC = 0.60), % fPSA (AUC = 0.62) and p2PSA (AUC = 0.63). At DCA, phi and PCA3 exhibited a very close net benefit profile until the threshold probability of 25%, then phi index showed higher net benefit than PCA3. Multivariable analysis showed that the addition of phi and PCA3 to the base multivariable model (age, PSA, %fPSA, DRE, prostate volume) increased predictive accuracy, whereas no model improved single biomarker performance. Finally we showed that subjects with active surveillance (AS) compatible cancer had significantly lower phi and PCA3 values (p<0.001 and p = 0.01, respectively). In conclusion, both phi and PCA3 comparably increase the accuracy in predicting the presence of PCa in total PSA range 2-10 ng/ml at initial biopsy, outperforming currently used %fPSA.

Figure 1. Receiver operating characteristic (ROC) curve for comparing all the analyzed markers as predictor of PCa in first biopsy.

Figure 2. Decision curve analysis.

The net benefit (calculated by subtracting the proportions of false positive from the proportion of true positive, the former being weighted by the relative harms of false positive and false negative results) of both phi and PCA3 is plotted against the threshold probability (the probability of PCa at which the benefits of opting for biopsy or no biopsy are considered equal). Solid lines represent the net benefit associated to the benchmarking strategies of biopsying all or no men irrespective of any diagnostic tool.

Figure 3. PCA3 and phi ability in discriminating PCa according to PRIAS criteria.

Box plot shows the distribution of PCA3 values (upper panel) and phi values (lower panel) in patients with biopsy proven PCa classified according to the PRIAS criteria for active surveillance. Data are shown as median (horizontal line in the box) and Q1 and Q3 (borders of the box). Whiskers represent the lowest and the highest values that are not outliers (i.e., data points below Q1–1.5x IQR or above Q3+1.5x IQR) or extreme values (i.e., data points below Q1–3xIQR or above Q3+3xIQR). Dots represent outlier values and asterisks represent extreme values. Q1 = 25th percentile; Q3 = 75th percentile; IQR (interquartile range) = Q3–Q1.