ReviewWorld journal of urology2014
The role of biomarkers in the assessment of prostate cancer risk prior to prostate biopsy: which markers matter and how should they be used?
Review in World journal of urology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Blood and urine-based biomarkers in prostate cancer: Current advances, clinical applications, and future directions.The journal of liquid biopsy · 2025Review
- Proteolyzed Variant of IgG with Free C-Terminal Lysine as a Biomarker of Prostate Cancer.Biology · 2021Article
- A critical appraisal of biomarkers in prostate cancer.World journal of urology · 2020Review
- Assessment of men's risk thresholds to proceed with prostate biopsy for the early detection of prostate cancer.International urology and nephrology · 2019Article
- Comparative Study of Blood-Based Biomarkers, α2,3-Sialic Acid PSA and PHI, for High-Risk Prostate Cancer Detection.International journal of molecular sciences · 2017Article
- Prostate tumor attenuation in the nu/nu murine model due to anti-sarcosine antibodies in folate-targeted liposomes.Scientific reports · 2016Article
- Sniffing out prostate cancer: a new clinical opportunity.Central European journal of urology · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
24825472What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.