A multiparametric magnetic resonance imaging-based risk model to determine the risk of significant prostate cancer prior to biopsy

van Leeuwen, PJ; Hayen, A; Thompson, JE; Moses, D; Shnier, R; Böhm, M; Abuodha, M; Haynes, AM; Ting, F; Barentsz, J; Roobol, M; Vass, J; Rasiah, K; Delprado, W; Stricker, PD

A multiparametric magnetic resonance imaging-based risk model to determine the risk of significant prostate cancer prior to biopsy

van Leeuwen, PJ Hayen, A

Thompson, JE Moses, D Shnier, R Böhm, M Abuodha, M Haynes, AM Ting, F Barentsz, J Roobol, M Vass, J Rasiah, K Delprado, W Stricker, PD

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Publication Type:: Journal Article
Citation:: BJU International, 2017, 120 (6), pp. 774 - 781
Issue Date:: 2017-12-01

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Field	Value	Language
dc.contributor.author	van Leeuwen, PJ	en_US
dc.contributor.author	Hayen, A https://orcid.org/0000-0003-4046-8030	en_US
dc.contributor.author	Thompson, JE	en_US
dc.contributor.author	Moses, D	en_US
dc.contributor.author	Shnier, R	en_US
dc.contributor.author	Böhm, M	en_US
dc.contributor.author	Abuodha, M	en_US
dc.contributor.author	Haynes, AM	en_US
dc.contributor.author	Ting, F	en_US
dc.contributor.author	Barentsz, J	en_US
dc.contributor.author	Roobol, M	en_US
dc.contributor.author	Vass, J	en_US
dc.contributor.author	Rasiah, K	en_US
dc.contributor.author	Delprado, W	en_US
dc.contributor.author	Stricker, PD	en_US
dc.date.issued	2017-12-01	en_US
dc.identifier.citation	BJU International, 2017, 120 (6), pp. 774 - 781	en_US
dc.identifier.issn	1464-4096	en_US
dc.identifier.uri	http://hdl.handle.net/10453/110534
dc.description.abstract	© 2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd Objective: To develop and externally validate a predictive model for detection of significant prostate cancer. Patients and Methods: Development of the model was based on a prospective cohort including 393 men who underwent multiparametric magnetic resonance imaging (mpMRI) before biopsy. External validity of the model was then examined retrospectively in 198 men from a separate institution whom underwent mpMRI followed by biopsy for abnormal prostate-specific antigen (PSA) level or digital rectal examination (DRE). A model was developed with age, PSA level, DRE, prostate volume, previous biopsy, and Prostate Imaging Reporting and Data System (PIRADS) score, as predictors for significant prostate cancer (Gleason 7 with >5% grade 4, ≥20% cores positive or ≥7 mm of cancer in any core). Probability was studied via logistic regression. Discriminatory performance was quantified by concordance statistics and internally validated with bootstrap resampling. Results: In all, 393 men had complete data and 149 (37.9%) had significant prostate cancer. While the variable model had good accuracy in predicting significant prostate cancer, area under the curve (AUC) of 0.80, the advanced model (incorporating mpMRI) had a significantly higher AUC of 0.88 (P < 0.001). The model was well calibrated in internal and external validation. Decision analysis showed that use of the advanced model in practice would improve biopsy outcome predictions. Clinical application of the model would reduce 28% of biopsies, whilst missing 2.6% significant prostate cancer. Conclusions: Individualised risk assessment of significant prostate cancer using a predictive model that incorporates mpMRI PIRADS score and clinical data allows a considerable reduction in unnecessary biopsies and reduction of the risk of over-detection of insignificant prostate cancer at the cost of a very small increase in the number of significant cancers missed.	en_US
dc.relation.ispartof	BJU International	en_US
dc.relation.isbasedon	10.1111/bju.13814	en_US
dc.subject.classification	Urology & Nephrology	en_US
dc.subject.mesh	Prostate	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Prostatic Neoplasms	en_US
dc.subject.mesh	Magnetic Resonance Imaging	en_US
dc.subject.mesh	Biopsy	en_US
dc.subject.mesh	Nomograms	en_US
dc.subject.mesh	Risk Assessment	en_US
dc.subject.mesh	Prospective Studies	en_US
dc.subject.mesh	Aged	en_US
dc.subject.mesh	Middle Aged	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Early Detection of Cancer	en_US
dc.title	A multiparametric magnetic resonance imaging-based risk model to determine the risk of significant prostate cancer prior to biopsy	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	120	en_US
utslib.for	1103 Clinical Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
pubs.organisational-group	/University of Technology Sydney/Faculty of Health/Public Health
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	120	en_US

Abstract:

© 2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd Objective: To develop and externally validate a predictive model for detection of significant prostate cancer. Patients and Methods: Development of the model was based on a prospective cohort including 393 men who underwent multiparametric magnetic resonance imaging (mpMRI) before biopsy. External validity of the model was then examined retrospectively in 198 men from a separate institution whom underwent mpMRI followed by biopsy for abnormal prostate-specific antigen (PSA) level or digital rectal examination (DRE). A model was developed with age, PSA level, DRE, prostate volume, previous biopsy, and Prostate Imaging Reporting and Data System (PIRADS) score, as predictors for significant prostate cancer (Gleason 7 with >5% grade 4, ≥20% cores positive or ≥7 mm of cancer in any core). Probability was studied via logistic regression. Discriminatory performance was quantified by concordance statistics and internally validated with bootstrap resampling. Results: In all, 393 men had complete data and 149 (37.9%) had significant prostate cancer. While the variable model had good accuracy in predicting significant prostate cancer, area under the curve (AUC) of 0.80, the advanced model (incorporating mpMRI) had a significantly higher AUC of 0.88 (P < 0.001). The model was well calibrated in internal and external validation. Decision analysis showed that use of the advanced model in practice would improve biopsy outcome predictions. Clinical application of the model would reduce 28% of biopsies, whilst missing 2.6% significant prostate cancer. Conclusions: Individualised risk assessment of significant prostate cancer using a predictive model that incorporates mpMRI PIRADS score and clinical data allows a considerable reduction in unnecessary biopsies and reduction of the risk of over-detection of insignificant prostate cancer at the cost of a very small increase in the number of significant cancers missed.

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http://hdl.handle.net/10453/110534