Integrating inflammatory serum biomarkers into a risk calculator for prostate cancer detection

Abstract Improved prostate cancer detection methods would avoid over-diagnosis of clinically indolent disease informing appropriate treatment decisions. The aims of this study were to investigate the role of a panel of Inflammation biomarkers to inform the need for a biopsy to diagnose prostate canc...

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Autores principales:	Amirhossein Jalali, Michael Kitching, Kenneth Martin, Ciaran Richardson, Thomas Brendan Murphy, Stephen Peter FitzGerald, Ronald William Watson, Antoinette Sabrina Perry
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6c13b9d6b64942d2abb4f6e7fe734e43
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spelling	oai:doaj.org-article:6c13b9d6b64942d2abb4f6e7fe734e432021-12-02T10:48:03ZIntegrating inflammatory serum biomarkers into a risk calculator for prostate cancer detection10.1038/s41598-021-81965-32045-2322https://doaj.org/article/6c13b9d6b64942d2abb4f6e7fe734e432021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81965-3https://doaj.org/toc/2045-2322Abstract Improved prostate cancer detection methods would avoid over-diagnosis of clinically indolent disease informing appropriate treatment decisions. The aims of this study were to investigate the role of a panel of Inflammation biomarkers to inform the need for a biopsy to diagnose prostate cancer. Peripheral blood serum obtained from 436 men undergoing transrectal ultrasound guided biopsy were assessed for a panel of 18 inflammatory serum biomarkers in addition to Total and Free Prostate Specific Antigen (PSA). This panel was integrated into a previously developed Irish clinical risk calculator (IPRC) for the detection of prostate cancer and high-grade prostate cancer (Gleason Score ≥ 7). Using logistic regression and multinomial regression methods, two models (Logst-RC and Multi-RC) were developed considering linear and nonlinear effects of the panel in conjunction with clinical and demographic parameters for determination of the two endpoints. Both models significantly improved the predictive ability of the clinical model for detection of prostate cancer (from 0.656 to 0.731 for Logst-RC and 0.713 for Multi-RC) and high-grade prostate cancer (from 0.716 to 0.785 for Logst-RC and 0.767 for Multi-RC) and demonstrated higher clinical net benefit. This improved discriminatory power and clinical utility may allow for individualised risk stratification improving clinical decision making.Amirhossein JalaliMichael KitchingKenneth MartinCiaran RichardsonThomas Brendan MurphyStephen Peter FitzGeraldRonald William WatsonAntoinette Sabrina PerryNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Amirhossein Jalali Michael Kitching Kenneth Martin Ciaran Richardson Thomas Brendan Murphy Stephen Peter FitzGerald Ronald William Watson Antoinette Sabrina Perry Integrating inflammatory serum biomarkers into a risk calculator for prostate cancer detection
description	Abstract Improved prostate cancer detection methods would avoid over-diagnosis of clinically indolent disease informing appropriate treatment decisions. The aims of this study were to investigate the role of a panel of Inflammation biomarkers to inform the need for a biopsy to diagnose prostate cancer. Peripheral blood serum obtained from 436 men undergoing transrectal ultrasound guided biopsy were assessed for a panel of 18 inflammatory serum biomarkers in addition to Total and Free Prostate Specific Antigen (PSA). This panel was integrated into a previously developed Irish clinical risk calculator (IPRC) for the detection of prostate cancer and high-grade prostate cancer (Gleason Score ≥ 7). Using logistic regression and multinomial regression methods, two models (Logst-RC and Multi-RC) were developed considering linear and nonlinear effects of the panel in conjunction with clinical and demographic parameters for determination of the two endpoints. Both models significantly improved the predictive ability of the clinical model for detection of prostate cancer (from 0.656 to 0.731 for Logst-RC and 0.713 for Multi-RC) and high-grade prostate cancer (from 0.716 to 0.785 for Logst-RC and 0.767 for Multi-RC) and demonstrated higher clinical net benefit. This improved discriminatory power and clinical utility may allow for individualised risk stratification improving clinical decision making.
format	article
author	Amirhossein Jalali Michael Kitching Kenneth Martin Ciaran Richardson Thomas Brendan Murphy Stephen Peter FitzGerald Ronald William Watson Antoinette Sabrina Perry
author_facet	Amirhossein Jalali Michael Kitching Kenneth Martin Ciaran Richardson Thomas Brendan Murphy Stephen Peter FitzGerald Ronald William Watson Antoinette Sabrina Perry
author_sort	Amirhossein Jalali
title	Integrating inflammatory serum biomarkers into a risk calculator for prostate cancer detection
title_short	Integrating inflammatory serum biomarkers into a risk calculator for prostate cancer detection
title_full	Integrating inflammatory serum biomarkers into a risk calculator for prostate cancer detection
title_fullStr	Integrating inflammatory serum biomarkers into a risk calculator for prostate cancer detection
title_full_unstemmed	Integrating inflammatory serum biomarkers into a risk calculator for prostate cancer detection
title_sort	integrating inflammatory serum biomarkers into a risk calculator for prostate cancer detection
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6c13b9d6b64942d2abb4f6e7fe734e43
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Integrating inflammatory serum biomarkers into a risk calculator for prostate cancer detection

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