Adam Kinnaird1,2, Wayne Brisbane1, Lorna Kwan1, Alan Priester3, Ryan Chuang1, Danielle E Barsa1, Merdie Delfin1, Anthony Sisk4, Daniel Margolis5, Ely Felker6, Jim Hu7, Leonard S Marks1. 1. Department of Urology, David Geffen School of Medicine, UCLA, Los Angeles, CA, United States. 2. Division of Urology, Department of Surgery, University of Alberta, Edmonton, AB, Canada. 3. Department of Bioengineering, UCLA, Los Angeles, CA, United States. 4. Department of Pathology & Laboratory Medicine, UCLA, Los Angeles, CA, United States. 5. Department of Radiology, Weill Cornell Medical College, New York, NY, United States. 6. Department of Radiological Sciences, UCLA, Los Angeles, CA, United States. 7. Department of Urology, Weill Cornell Medical College, New York, NY, United States.
Abstract
INTRODUCTION: A functional tool to optimize patient selection for magnetic resonance imaging (MRI)-guided prostate biopsy (MRGB) is an unmet clinical need. We sought to develop a prostate cancer risk calculator (PCRC-MRI) that combines MRI and clinical characteristics to aid decision-making for MRGB in North American men. METHODS: Two prospective registries containing 2354 consecutive men undergoing MRGB (September 2009 to April 2019) were analyzed. Patients were randomized into five groups, with one group randomly assigned to be the validation cohort against the other four groups as the discovery cohort. The primary outcome was detection of clinically significant prostate cancer (csPCa) defined as Gleason grade group ≥2. Variables included age, ethnicity, digital rectal exam (DRE), prior biopsy, prostate-specific antigen (PSA), prostate volume, PSA density, and MRI score. Odds ratios (OR) were calculated from multivariate logistic regression comparing two models: one with clinical variables only (clinical) against a second combining clinical variables with MRI data (clinical+MRI). RESULTS: csPCa was present in 942 (40%) of the 2354 men available for study. The positive and negative predictive values for csPCa in the clinical+MRI model were 57% and 89%, respectively. The area under the curve of the clinical+MRI model was superior to the clinical model in discovery (0.843 vs. 0.707, p<0.0001) and validation (0.888 vs. 0.757, p<0.0001) cohorts. Use of PCRC-MRI would have avoided approximately 16 unnecessary biopsies in every 100 men. Of all variables examined, Asian ethnicity was the most protective factor (OR 0.46, 0.29-0.75) while MRI score 5 indicated greatest risk (OR15.8, 10.5-23.9). CONCLUSIONS: A risk calculator (PCRC-MRI), based on a large North American cohort, is shown to improve patient selection for MRGB, especially in preventing unnecessary biopsies. This tool is available at https://www.uclahealth.org/urology/prostate-cancer-riskcalculator and may help rationalize biopsy decision-making.
INTRODUCTION: A functional tool to optimize patient selection for magnetic resonance imaging (MRI)-guided prostate biopsy (MRGB) is an unmet clinical need. We sought to develop a prostate cancer risk calculator (PCRC-MRI) that combines MRI and clinical characteristics to aid decision-making for MRGB in North American men. METHODS: Two prospective registries containing 2354 consecutive men undergoing MRGB (September 2009 to April 2019) were analyzed. Patients were randomized into five groups, with one group randomly assigned to be the validation cohort against the other four groups as the discovery cohort. The primary outcome was detection of clinically significant prostate cancer (csPCa) defined as Gleason grade group ≥2. Variables included age, ethnicity, digital rectal exam (DRE), prior biopsy, prostate-specific antigen (PSA), prostate volume, PSA density, and MRI score. Odds ratios (OR) were calculated from multivariate logistic regression comparing two models: one with clinical variables only (clinical) against a second combining clinical variables with MRI data (clinical+MRI). RESULTS: csPCa was present in 942 (40%) of the 2354 men available for study. The positive and negative predictive values for csPCa in the clinical+MRI model were 57% and 89%, respectively. The area under the curve of the clinical+MRI model was superior to the clinical model in discovery (0.843 vs. 0.707, p<0.0001) and validation (0.888 vs. 0.757, p<0.0001) cohorts. Use of PCRC-MRI would have avoided approximately 16 unnecessary biopsies in every 100 men. Of all variables examined, Asian ethnicity was the most protective factor (OR 0.46, 0.29-0.75) while MRI score 5 indicated greatest risk (OR15.8, 10.5-23.9). CONCLUSIONS: A risk calculator (PCRC-MRI), based on a large North American cohort, is shown to improve patient selection for MRGB, especially in preventing unnecessary biopsies. This tool is available at https://www.uclahealth.org/urology/prostate-cancer-riskcalculator and may help rationalize biopsy decision-making.
Authors: Florian A Distler; Jan P Radtke; David Bonekamp; Claudia Kesch; Heinz-Peter Schlemmer; Kathrin Wieczorek; Marietta Kirchner; Sascha Pahernik; Markus Hohenfellner; Boris A Hadaschik Journal: J Urol Date: 2017-03-31 Impact factor: 7.450
Authors: Arnout R Alberts; Monique J Roobol; Jan F M Verbeek; Ivo G Schoots; Peter K Chiu; Daniël F Osses; Jasper D Tijsterman; Harrie P Beerlage; Christophe K Mannaerts; Lars Schimmöller; Peter Albers; Christian Arsov Journal: Eur Urol Date: 2018-08-03 Impact factor: 20.096
Authors: Michael D Gross; Leonard S Marks; Geoffrey A Sonn; David A Green; Gerald J Wang; Jonathan E Shoag; Elizabeth Cabezon; Daniel J Margolis; Brian D Robinson; Jim C Hu Journal: J Urol Date: 2019-09-10 Impact factor: 7.450
Authors: Hashim U Ahmed; Ahmed El-Shater Bosaily; Louise C Brown; Rhian Gabe; Richard Kaplan; Mahesh K Parmar; Yolanda Collaco-Moraes; Katie Ward; Richard G Hindley; Alex Freeman; Alex P Kirkham; Robert Oldroyd; Chris Parker; Mark Emberton Journal: Lancet Date: 2017-01-20 Impact factor: 79.321
Authors: Donna P Ankerst; Andreas Boeck; Stephen J Freedland; Ian M Thompson; Angel M Cronin; Monique J Roobol; Jonas Hugosson; J Stephen Jones; Michael W Kattan; Eric A Klein; Freddie Hamdy; David Neal; Jenny Donovan; Dipen J Parekh; Helmut Klocker; Wolfgang Horninger; Amine Benchikh; Gilles Salama; Arnauld Villers; Daniel M Moreira; Fritz H Schröder; Hans Lilja; Andrew J Vickers Journal: World J Urol Date: 2011-12-31 Impact factor: 4.226
Authors: Veeru Kasivisvanathan; Antti S Rannikko; Marcelo Borghi; Valeria Panebianco; Lance A Mynderse; Markku H Vaarala; Alberto Briganti; Lars Budäus; Giles Hellawell; Richard G Hindley; Monique J Roobol; Scott Eggener; Maneesh Ghei; Arnauld Villers; Franck Bladou; Geert M Villeirs; Jaspal Virdi; Silvan Boxler; Grégoire Robert; Paras B Singh; Wulphert Venderink; Boris A Hadaschik; Alain Ruffion; Jim C Hu; Daniel Margolis; Sébastien Crouzet; Laurence Klotz; Samir S Taneja; Peter Pinto; Inderbir Gill; Clare Allen; Francesco Giganti; Alex Freeman; Stephen Morris; Shonit Punwani; Norman R Williams; Chris Brew-Graves; Jonathan Deeks; Yemisi Takwoingi; Mark Emberton; Caroline M Moore Journal: N Engl J Med Date: 2018-03-18 Impact factor: 176.079