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Published on: April 9, 2019
Validation of a Urine-Based Proteomics Test to Predict Clinically Significant Prostate Cancer: Complementing mpMRI
Maria Frantzi1, Ana C Morillo2, Guillermo Lendinez3
1Department of Biomarker Research, Mosaiques Diagnostics GmbH, Hannover, Germany.
Introduction:
Prostate cancer (PCa) is the most frequently diagnosed cancer among men. A major clinical need is to accurately predict clinically significant PCa (csPCa). A proteomics-based 19-biomarker model (19-BM) was previously developed using capillary electrophoresis-mass spectrometry (CE-MS) and validated in close to 1,000 patients at risk for PCa. This study aimed to validate 19-BM in a multicenter prospective cohort of 101 biopsy-naive patients using current diagnostic pathways.
Methods:
Urine samples from 101 patients with suspicious of PCa were analyzed using CE-MS. All patients underwent multiparametric or magnetic resonance imaging (mpMRI) using a 3-T system. The 19-BM score was estimated using support vector machine-based software (MosaCluster v1.7.0), employing the previously published cut-off criterion of -0.07. Diagnostic nomograms were investigated along with mpMRI.
Results:
Independent validation of 19-BM yielded a sensitivity of 77% and a specificity of 85% (AUC:0.81). This performance surpassed those of prostate-specific antigen (PSA; AUC:0.56) and PSA density (AUC:0.69). For PI-RADS≤ 3 patients, 19-BM showed a sensitivity of 86% and a specificity of 88%. Integrating 19-BM with mpMRI resulted in significantly better accuracy (AUC:0.90) compared to individual investigations alone (AUC19BM = 0.81; p = 0.004 and AUCmpMRI: 0.79; p = 0.001). Examining the decision curve analysis, 19-BM with mpMRI surpassed other approaches for the prevailing risk interval from a 30% cut-off.
Conclusions:
19-BM exhibited favorable reproducibility for the prediction of csPCa. In patients with PI-RADS ≤3, 19-BM correctly classified 88% of the patients with insignificant PCa at the cost of 1 missed csPCa patient. Utilizing the 19-BM test could prove valuable in complementing mpMRI and reducing the need for unnecessary biopsies.
