Prostate cancer (PCa) screening has been substantially influenced by the clinical implementation of serum prostate-specific antigen (PSA).
In this context, improvement of early PCa detection and stage migration as well as reduced PCa mortality were achieved, and up-to-date PSA represents the gold standard biomarker of PCa diagnosis together with clinical findings. Nonetheless, PSA shows weakness in discriminating between malign and benign prostatic disease or indolent and aggressive cancers. As a result, the expansion of PSA screening is extensively debated with regard to overdetection and ultimately overtreatment, keeping in mind that PCa is still the third leading cause of cancer-specific mortality in the Western male population. Consequently, today's task is to increase the accuracy of PCa detection and furthermore to allow stratification for indolent PCa that might permit active surveillance and to filter out aggressive cancers necessitating treatment. Thus, novel biomarkers, especially in combination with approved clinical risk factors (e.g., age or family history of PCa), within multivariable prediction models carry the potential to improve many aspects of PCa diagnosis and to enable risk classification in clinical practice. Multivariable models lead to superior accuracy for PCa prediction instead of the use of a single risk factor. The aim of this article was to present an overview of known risk factors for PCa together with new promising blood- and urine-based biomarkers and their application within risk models that may allow risk stratification for PCa prior to prostate biopsy. Risk models may optimize PCa detection and classification with regard to improved PCa risk assessment and avoidance of unnecessary prostate biopsies.
Written by:
Schmid M, Trinh QD, Graefen M, Fisch M, Chun FK, Hansen J. Are you the author?
Department of Urology, University Hospital Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
Reference: World J Urol. 2014 May 14. Epub ahead of print.
doi: 10.1007/s00345-014-1317-2
PubMed Abstract
PMID: 24825472
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