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Multivariable logistic regression models were complemented by predictive accuracy analysis and decision-curve analysis. Of the 1026 patients included in the PROMEtheuS cohort, 158 (15.4%) had a first-degree relative with PCa. p2PSA, %p2PSA and PHI values were significantly higher (P http://www.selleck.cn/products/VX-770.html http://www.selleckchem.com/products/SB-431542.html and 70.1%; 95% confidence interval [CI]: 58.4�C80.7 and 59.4�C79.5 respectively). A PHI threshold of 40 was found to have the best balance between sensitivity and specificity (64.8 and 71.3%, respectively; 95% CI 52.5�C75.8 and 60.6�C80.5). At 90% sensitivity, the thresholds for %p2PSA and PHI were 1.20 and 25.5, with a specificity of 37.9 and 25.5%, respectively. At a %p2PSA threshold of 1.20, a total of 39 (24.8%) biopsies could have been avoided, but two cancers with a Gleason score (GS) of 7 would have been missed. At a PHI threshold of 25.5 a total of 27 (17.2%) biopsies could have been avoided and two (3.8%) cancers with a GS of 7 would have been missed. In multivariable logistic regression models, %p2PSA and PHI achieved independent predictor status and significantly increased the accuracy of multivariable models including PSA and prostate volume by 8.7 and 10%, respectively (P �� 0.001). p2PSA, %p2PSA and PHI were directly correlated with Gleason score (��: 0.247, P = 0.038; ��: 0.366, P = 0.002; ��: 0.464, P http://www.selleckchem.com/products/INCB18424.html SWOP-PRI and the North American PCPT are among the most popular. However, evidence on the relative predictive accuracy is lacking. A head-to-head comparison on the diagnostic accuracy of two previously validated prostate cancer risk predictors on biopsy confirmed the superiority of these tools over PSA alone. Moreover, in the studied population, the European SWOP-PRI proved to be more accurate than the North American PCPT-CRC. To compare the diagnostic accuracy of two previously validated prostate cancer risk predictors on biopsy.