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Article of the Week: A mpMRI-based risk model to determine the risk of prostate cancer prior to biopsy

Every Week the Editor-in-Chief selects an Article of the Week from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.

In addition to the article itself, there is an accompanying editorial written by a prominent member of the urological community. This blog is intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation.

If you only have time to read one article this week, it should be this one.

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

Pim J. van Leeuwen*, Andrew Hayen, James E. Thompson*†‡, Daniel Moses§Ron Shnier§, Maret Bohm, Magdaline Abuodha, Anne-Maree HaynesFrancis Ting*†‡, Jelle Barentsz, Monique Roobol**, Justin Vass††, Krishan Rasiah††Warick Delprado‡‡ and Phillip D. Stricker*†‡


*St. Vincents Prostate Cancer Centre, Garvan Institute of Medical Research/The Kinghorn Cancer Centre, Darlinghurst, School of Public Health and Community Medicine, §School of Medicine, University of New South Wales, Kensington, New South Wales, Australia, Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, **Department of Urology, Erasmus University Medical Center, Rotterdam, the Netherlands, ††Department of Urology, Royal North Shore Private Hospital, St Leonards, and ‡‡Douglass Hanly Moir Pathology and University of Notre Dame, Darlinghurst, New South Wales, Australia




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 prosp   ctive 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.


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.


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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