A Multiparametric Magnetic Resonance Imaging Based Risk Model to Determine the Risk of Significant Prostate Cancer prior to biopsy

To develop and externally validate a predictive model for detection of significant prostate cancer (PC).

Development of the model was based on prospective cohort including 393 men who underwent mpMRI prior to biopsy. External validity of the model was then examined retrospectively in 198 men from a separate institution whom underwent a mpMRI followed by biopsy for abnormal PSA/DRE. A model was developed with age, PSA, DRE, prostate volume, previous biopsy and PIRADS score as predictors for significant PC (Gleason 7 with >5% grade 4, ≥ 20% cores positive or ≥ 7mm of PC in any core). Probability was studied via logistic regression. Discriminatory performance was quantified by concordance statistics and internally validated with bootstrap resampling.

393 men had complete data. A total of 149 patients (37.9%) had significant PC. While the variable model had good accuracy in predicting significant PC (AUC of 0.80), the advanced model (incorporating mpMRI) had significant higher AUC of 0.88 (p<0.001). The model was well calibrated in internal and external validation. Decision analysis demonstrating 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 PC.

Individualized risk assessment of significant PC using a predictive model that incorporates mpMRI PIRADS score and clinical data may allow a considerable reduction in unnecessary biopsies and reduction of the risk of over-detection of insignificant PC at the cost of a very small increase in the number of significant cancers missed. This article is protected by copyright. All rights reserved.

BJU international. 2017 Feb 16 [Epub ahead of print]

Pim J van Leeuwen, Andrew Hayen, James E Thompson, Daniel Moses, Ron Shnier, Maret Böhm, Magdaline Abuodha, Anne-Maree Haynes, Francis Ting, Jelle Barentsz, Monique Roobol, Justin Vass, Krishan Rasiah, Warick Delprado, Phillip D Stricker

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

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