Purpose: Prostate cancer (PCa) is the most frequently diagnosed cancer in men. One 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 1000 patients at risk for PCa. Here, our objective was to validate 19-BM in a multicentre prospective cohort of 101 biopsy-naive patients using current diagnostic pathways. Materials and Methods: Urine samples from 101 PCa patients were analysed through CE-MS. All patients underwent MRI using a 3-T system. The 19-BM score was estimated via a support vector machine-based software (MosaCluster; v1.7.0), employing previously established cut-off criterion of -0.07. Previously developed diagnostic nomograms were calculated along with MRI. Results: Independent validation of the 19-BM yielded a sensitivity of 77% and specificity of 85% (AUC:0.81). This performance surpasses that of PSA (AUC:0.56), and PSA density (AUC:0.69). For PI-RADS≤ 3 patients, the 19-BM showed a sensitivity of 86% and specificity of 88%. Integrating the 19-BM with MRI resulted in significantly better accuracy (AUC:0.90) compared to the individual investigations alone (AUC19BM=0.81; p=0.004 and AUCMRI:0.79; p=0.001). Examining the decision curve analysis, the 19-BM with MRI surpassed other approaches for the prevailing risk interval from 30% cut-off. Conclusions: 19-BM exhibited favourable reproducibility for prediction of csPCa. In PI-RADS≤3 patients the 19-BM correctly classified 88% of the patients with insignificant PCa at the cost of one csPCa patient that was missed. Utilising 19-BM test could prove valuable complementing MRI and reducing the need for unnecessary biopsies.

Prof. Harald Mischak holds ownership interest in Mosaiques Diagnostics GmbH. Dr. Maria Frantzi and Mr. Emmanouil Mavrogeorgis are employed by Mosaiques Diagnostics GmbH. No potential conflicts of interest were disclosed by the other authors concerning the 19-BM diagnostic test

This work was funded by PI22/01769 [FIS (Science and Innovation Ministry, ISC III, FEDER)] and ProSTRAT AI (01DS23014; Federal Ministry of Education and Research/ BMBF/ EUREKA network). Marina Mora-Ortiz has been awarded funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 847468.

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Ethics committee/IRB of the University Hospital of Reina Sofia (Cordoba, Spain) granted ethical approval (number PI22/01769; 31/08/2022) for this work, in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants for the project.

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All data produced in the present study are available upon reasonable request to the authors