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Published on: January 5, 2017
Prospective validation study of a combined urine and plasma test for predicting high-grade prostate cancer in biopsy
Torben Brøchner Pedersen1, Mads Hvid Poulsen2, Martin Lund3
1Department of Urology, Odense University Hospital, Odense, Denmark; Open Patient Data Explorative Network (OPEN), Odense University Hospital, Odense, Denmark. torben.b.pedersen@rsyd.dk.
Objective:
Early and accurate diagnosis of prostate cancer (PC) is crucial for effective treatment. Diagnosing clinically insignificant cancers can lead to overdiagnosis and overtreatment, highlighting the importance of accurately selecting patients for further evaluation based on improved risk prediction tools. Novel biomarkers offer promise for enhancing this diagnostic process. In this study, we aimed to externally validate a previously developed urine and plasma biomarker test in a biopsy-naïve population.
Materials And Methods:
Urine and blood samples were prospectively collected from 362 biopsy-naïve men with suspected PC before they underwent transrectal prostate biopsies. The expression levels of a 10-gene mRNA panel were quantified using reverse transcription/quantitative polymerase chain reaction of both urine and plasma. These gene expression levels, combined with clinical features and plasma prostate-specific antigen (PSA) levels, were used to predict the presence of International Society of Urological Pathology grade group ≥ 2 PC.
Results:
Complete data were available for 314 patients. The sensitivity and specificity of the biomarker test were 87% (95% CI: 79-93%) and 42% (95% CI: 36-49%), respectively. The area under the curve was 0.76 (95% CI: 0.7-0.82) for the biomarker test probability and 0.65 (95% CI: 0.59-0.72) for PSA (p = 0.02). The test's negative predictive value was 89% (CI: 81-94%).
Conclusion:
This study did not replicate the previously reported high accuracy of the biomarker test, highlighting the need for further refinement and robust external validation to ensure reliable performance across diverse patient populations.
