Prostate cancer (PCa) diagnosis faces the dual problem of overdiagnosis leading to unnecessary biopsies and underdetection of clinically meaningful disease. The study prospectively evaluated a urine-based 19-biomarker model (19-BM) measured by capillary electrophoresis–mass spectrometry (CE‑MS) to predict clinically significant prostate cancer (csPCa) in patients who had not previously undergone prostate biopsy (biopsy‑naïve) but were considered at risk.
The primary aim was to validate the previously developed urinary proteomic signature in a prospective cohort and to compare its diagnostic accuracy with established tools including prostate-specific antigen (PSA) testing, the ERSPC risk calculator, PSA density (PSAD), and multiparametric magnetic resonance imaging (mpMRI). The study also examined the performance of an existing nomogram that integrates 19-BM and mpMRI in this prospective setting.
One hundred sixty-one biopsy‑naïve patients deemed at risk for csPCa were prospectively enrolled at two major hospitals. Each participant underwent pre‑biopsy urine sampling for CE‑MS analysis to generate the 19-BM score, mpMRI, and subsequent prostate biopsy.
Performance metrics reported in the source included Area Under the Receiver Operating Characteristic Curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). The study compared the 19-BM against PSA, ERSPC, PSAD, and mpMRI. Additionally, 100 patients who proceeded to radical prostatectomy were evaluated to assess correlations between preoperative 19-BM scores and definitive prostatectomy pathology.
Details on assay processing, exact thresholds, biopsy protocols, mpMRI scoring methodology, and statistical methods are described in the full article. The preview reports key comparative performance metrics and p values for statistical comparisons between diagnostic tools.
The urine‑based 19-BM achieved an AUC of 0.79 for predicting clinically significant prostate cancer in this prospective cohort. Reported comparative AUCs and statistical comparisons in the source are:
These comparisons indicate that the 19-BM outperformed each of these commonly used diagnostic tools in terms of AUC within the reported dataset.
In the subgroup of patients with low Prostate Imaging Reporting and Data System (PI-RADS) scores, the 19-BM demonstrated a sensitivity of 75% and a specificity of 90%, suggesting potential value for risk stratification when mpMRI yields low suspicion findings.
A previously published nomogram combining 19-BM with mpMRI was prospectively validated in this cohort and returned an AUC of 0.82, with reported specificity of 90% and sensitivity of 64% in this validation set. The prospectively validated nomogram therefore showed slightly improved AUC compared with 19-BM alone in this sample.
The authors state that the 19-BM demonstrates robust performance after prospective validation and may serve as a complementary test to current diagnostic pathways, potentially reducing unnecessary invasive procedures. The preview notes that further methodological and subgroup detail are contained in the full text; where the source preview is incomplete, those details were not reported in the available excerpt.
Limitations described in brief in the preview include that the full procedural and demographic data require access to the full article. The preview does not provide granular breakdowns of patient demographics, biopsy results by grade group, or long‑term outcomes, and such details were not reported in the provided source excerpt.
In this prospective validation, the urine‑based CE‑MS 19-BM showed strong discriminatory performance for detecting clinically significant prostate cancer in biopsy‑naïve patients and outperformed PSA, ERSPC, PSAD, and mpMRI on reported AUC comparisons. The 19-BM showed particularly favorable specificity in low PI-RADS cases and, when combined with mpMRI in a previously published nomogram, achieved an AUC of 0.82 in this prospective cohort.
The authors conclude that the 19-BM can act as a useful complement to current diagnostic tools, with potential to reduce unnecessary biopsies and improve detection of csPCa. For full operational details, thresholds, and implementation considerations, readers should consult the complete article.
The study datasets generated have been deposited in the Zenodo repository and are publicly accessible at the DOI provided in the source: https://doi.org/10.5281/zenodo.18922158.
The preview lists selected references supporting the study context, including guideline and methodological citations such as the 2024 EAU‑EANM‑ESTRO‑ESUR‑ISUP‑SIOG guideline update on prostate cancer, prior validations of CE‑MS urinary biomarkers, and literature on MRI performance and variability. Full reference details are included in the source article preview.