Original Article
Head-to-head comparison of prostate cancer risk calculators predicting biopsy outcome
Abstract
Background: Multivariable risk calculators (RCs) predicting prostate cancer (PCa) aim to reduce unnecessary workup (e.g., MRI and biopsy) by selectively identifying those men at risk for PCa or clinically significant PCa (csPCa) (Gleason ≥7). The lack of an adequate comparison makes choosing between RCs difficult for patients, clinicians and guideline developers. We aim to perform a head-to-head comparison of seven well known RCs predicting biopsy outcome.
Methods: Our study comprised 7,199 men from ten independent contemporary cohorts in Europe and Australia, who underwent prostate biopsy between 2007 and 2015. We evaluated the performance of the ERSPC RPCRC, Finne, Chun, ProstataClass, Karakiewicz, Sunnybrook, and PCPT 2.0 (HG) RCs in predicting the presence of any PCa and csPCa. Performance was assessed by discrimination, calibration and net benefit analyses.
Results: A total of 3,458 (48%) PCa were detected; 1,784 (25%) men had csPCa. No particular RC stood out predicting any PCa: pooled area under the ROC-curve (AUC) ranged between 0.64 and 0.72. The ERSPC RPCRC had the highest pooled AUC 0.77 (95% CI: 0.73–0.80) when predicting csPCa. Decision curve analysis (DCA) showed limited net benefit in the detection of csPCa, but that can be improved by a simple calibration step. The main limitation is the retrospective design of the study.
Conclusions: No particular RC stands out when predicting biopsy outcome on the presence of any PCa. The ERSPC RPCRC is superior in identifying those men at risk for csPCa. Net benefit analyses show that a multivariate approach before further workup is advisable.
Methods: Our study comprised 7,199 men from ten independent contemporary cohorts in Europe and Australia, who underwent prostate biopsy between 2007 and 2015. We evaluated the performance of the ERSPC RPCRC, Finne, Chun, ProstataClass, Karakiewicz, Sunnybrook, and PCPT 2.0 (HG) RCs in predicting the presence of any PCa and csPCa. Performance was assessed by discrimination, calibration and net benefit analyses.
Results: A total of 3,458 (48%) PCa were detected; 1,784 (25%) men had csPCa. No particular RC stood out predicting any PCa: pooled area under the ROC-curve (AUC) ranged between 0.64 and 0.72. The ERSPC RPCRC had the highest pooled AUC 0.77 (95% CI: 0.73–0.80) when predicting csPCa. Decision curve analysis (DCA) showed limited net benefit in the detection of csPCa, but that can be improved by a simple calibration step. The main limitation is the retrospective design of the study.
Conclusions: No particular RC stands out when predicting biopsy outcome on the presence of any PCa. The ERSPC RPCRC is superior in identifying those men at risk for csPCa. Net benefit analyses show that a multivariate approach before further workup is advisable.
