Risk of prostate cancer for men with prior negative biopsies undergoing magnetic resonance imaging compared with biopsy‐naive men: A prospective evaluation of the PLUM cohort
Abstract
Background: Men with prior negative prostate biopsies have a lower risk of being diagnosed with prostate cancer in comparison with biopsy‐naive men. However, the relative clinical utility of identified lesions on multiparametric magnetic resonance imaging (mpMRI) is uncertain between the 2 settings.Methods: Patients from the Prospective Loyola University mpMRI (PLUM) Prostate Biopsy Cohort (January 2015 to June 2020) were examined. The detection of any prostate cancer and clinically significant prostate cancer (Gleason score ≥ 3 + 4) was stratified by Prostate Imaging–Reporting and Data System (PI‐RADS) scores in the prior negative and biopsy‐naive settings. Multivariable logistic regression models (PLUM models) assessed predictors, and decision curve analyses were used to estimate the clinical utility of PI‐RADS cutoffs relative to the models.Results: Nine hundred men (420 prior negative patients and 480 biopsy‐naive patients) were included. Prior negative patients had lower risks of any prostate cancer (27.9% vs 54.4%) and clinically significant prostate cancer (20.0% vs 38.3%) in comparison with biopsy‐naive patients, and this persisted when they were stratified by PI‐RADS (eg, PI‐RADS 3: 13.6% vs 27.4% [any prostate cancer] and 5.2% vs 15.4% [clinically significant prostate cancer]). The rate of detection of clinically significant prostate cancer was 5.3% among men with prior negative biopsy and PI‐RADS ≤ 3. Family history and Asian ancestry were significant predictors among biopsy‐naive patients. PLUM models demonstrated a greater net benefit and reduction in biopsies (45.8%) without missing clinically significant cancer in comparison with PI‐RADS cutoffs (PI‐RADS 4: 34.0%).Conclusions: Patients with prior negative biopsies had lower prostate cancer detection by PI‐RADS score category in comparison with biopsy‐naive men. Decision curve analyses suggested that many biopsies could be avoided by the use of the PLUM models or a PI‐RADS 4 cutoff without any clinically significant cancer being missed.Lay Summary: Men with a prior negative prostate biopsy had a lower risk of harboring prostate cancer in comparison with those who never had a biopsy. This was true even when patients in each group had similar multiparametric magnetic resonance imaging (mpMRI) findings in terms of Prostate Imaging–Reporting and Data System (PI‐RADS)–graded lesions. Decision curve analyses showed that many biopsies could be avoided by the use of the Prospective Loyola University mpMRI prediction models or a PI‐RADS 4 cutoff for patients with prior negative biopsies.Keywords
Funding Information
- Siemens Foundation
This publication has 16 references indexed in Scilit:
- Contemporary Trends in Magnetic Resonance Imaging at the Time of Prostate Biopsy: Results from a Large Private Insurance DatabaseEuropean Urology Focus, 2021
- The REDCap consortium: Building an international community of software platform partnersJournal of Biomedical Informatics, 2019
- A Contemporary Prostate Biopsy Risk Calculator Based on Multiple Heterogeneous CohortsEuropean Urology, 2018
- A Magnetic Resonance Imaging–Based Prediction Model for Prostate Biopsy Risk StratificationJAMA Oncology, 2018
- MRI-Targeted or Standard Biopsy for Prostate-Cancer DiagnosisNew England Journal of Medicine, 2018
- Prediction of Prostate Cancer Risk Among Men Undergoing Combined MRI-targeted and Systematic Biopsy Using Novel Pre-biopsy Nomograms That Incorporate MRI FindingsUrology, 2018
- Prostate Magnetic Resonance Imaging and Magnetic Resonance Imaging Targeted Biopsy in Patients with a Prior Negative Biopsy: A Consensus Statement by AUA and SARJournal of Urology, 2016
- The influence of prostate‐specific antigen density on positive and negative predictive values of multiparametric magnetic resonance imaging to detect Gleason score 7–10 prostate cancer in a repeat biopsy settingBJU International, 2016
- PI-RADS Prostate Imaging – Reporting and Data System: 2015, Version 2European Urology, 2016
- Decision Curve Analysis: A Novel Method for Evaluating Prediction ModelsMedical Decision Making, 2006