Pim J van Leeuwen1,2, Andrew Hayen3, James E Thompson1,2,3, Daniel Moses4, Ron Shnier4, Maret Böhm2, Magdaline Abuodha2, Anne-Maree Haynes2, Francis Ting1,2,3, Jelle Barentsz5, Monique Roobol6, Justin Vass7, Krishan Rasiah7, Warick Delprado8, Phillip D Stricker1,2,3. 1. St. Vincent's Prostate Cancer Centre, Darlinghurst, New South Wales, Australia. 2. Garvan Institute of Medical Research/The Kinghorn Cancer Centre, Darlinghurst, New South Wales, Australia. 3. School of Public Health and Community Medicine, Kensington, New South Wales, Australia. 4. School of Medicine, University of New South Wales, Kensington, New South Wales, Australia. 5. Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, the Netherlands. 6. Department of Urology, Erasmus University Medical Center, Rotterdam, the Netherlands. 7. Department of Urology, Royal North Shore Private Hospital, St Leonards, New South Wales, Australia. 8. Douglass Hanly Moir Pathology and University of Notre Dame, Darlinghurst, New South Wales, Australia.
Abstract
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.
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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