PSA-based machine learning model improves prostate cancer risk stratification in a screening population.

Journal: World journal of urology

Published Date: Aug 3, 2020

Abstract

CONTEXT: The majority of prostate cancer diagnoses are facilitated by testing serum Prostate Specific Antigen (PSA) levels. Despite this, there are limitations to the diagnostic accuracy of PSA. Consideration of patient demographic factors and biochemical adjuncts to PSA may improve prostate cancer risk stratification. We aimed to develop a contemporary, accurate and cost-effective model based on objective measures to improve the accuracy of prostate cancer risk stratification.

Authors

Marlon Perera

Faculty of Medicine, The University of Queensland, Brisbane, Australia.
Rohan Mirchandani

Maxwell Plus, Brisbane, QLD, Australia.
Nathan Papa

Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.
Geoff Breemer

Maxwell Plus, Brisbane, QLD, Australia.
Anna Effeindzourou

Maxwell Plus, Brisbane, QLD, Australia.
Lewis Smith

Maxwell Plus, Brisbane, QLD, Australia.
Peter Swindle

Department of Urology, Mater Hospital, Brisbane, QLD, Australia.
Elliot Smith

Maxwell Plus, Brisbane, QLD, Australia.

Keywords

Aged Early Detection of Cancer Humans Machine Learning Male Middle Aged Models, Theoretical Prostate-Specific Antigen Prostatic Neoplasms Retrospective Studies Risk Assessment

External Resources

View on PubMed Access via DOI PubMed (32747980)

PSA-based machine learning model improves prostate cancer risk stratification in a screening population.

Abstract

Authors

Keywords

External Resources

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