Mp53-11 a Pre-biopsy Nomogram for Prediction of the Risk of Gleason Score = 7 Prostate Cancer on Combined Mri-us Fusion Targeted and Systematic Prostate Biopsy Among Men With No Previous Biopsy
Year: 2016
Type: article
Study design: Observational Study
Abstract: You have accessJournal of UrologyProstate Cancer: Detection & Screening VII1 Apr 2016MP53-11 A PRE-BIOPSY NOMOGRAM FOR PREDICTION OF THE RISK OF GLEASON SCORE = 7 PROSTATE CANCER ON COMBINED MRI-US FUSION TARGETED AND SYSTEMATIC PROSTATE BIOPSY AMONG MEN WITH NO PREVIOUS BIOPSY Marc Bjurlin, James Wysock, Saradwata Sakar, Rajesh Venkataraman, Neil Mendhiratta, Xiaosong Meng, Andrew Rosenkrantz, William Huang, Herbert Lepor, and Samir Taneja Marc BjurlinMarc Bjurlin More articles by this author , James WysockJames Wysock More articles by this author , Saradwata SakarSaradwata Sakar More articles by this author , Rajesh VenkataramanRajesh Venkataraman More articles by this author , Neil MendhirattaNeil Mendhiratta More articles by this author , Xiaosong MengXiaosong Meng More articles by this author , Andrew RosenkrantzAndrew Rosenkrantz More articles by this author , William HuangWilliam Huang More articles by this author , Herbert LeporHerbert Lepor More articles by this author , and Samir TanejaSamir Taneja More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.508AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES MRI-targeted prostate biopsy (PB) is increasingly being utilized to aid cancer diagnosis in clinical practice. Our objective was to develop a nomogram to predict the probability of Gleason score ≤7 prostate cancer (CaP) on MRI targeted and systematic prostate biopsy in men with no previous biopsy. METHODS From June 2012 to June 2015, MR-US fusion targeted prostate biopsy was performed on approximately 1,140 men with suspicious regions identified on pre-biopsy 3T multiparametric-MRI along with systematic 12 core biopsy, utilizing the ProFuse|ArtemisTM system. Logistic regression modeling was used to evaluate predictors of Gleason score ≥7 CaP, and corresponding nomograms were generated. Models were created with a randomly selected training sample (n=232), tested (n=59), and then validated (n=98). Bias-corrected using bootstrap resampling, and Akaike information criterion was used to select best-fit models. RESULTS A total of 389 men with no previous biopsy and complete records were included for analysis (median age 66 years, PSA 4.8 ng/ml, prostate volume 46 cc, PSA density 0.09 ng/ml-cc). Statistically significant independent positive predictors of CaP on targeted and systematic PB were found to be PSA density, age, and MRI suspicion score (MRIss). A CaP probability nomogram (Figure 1) was generated using the predictors and model performance characteristics bias-corrected areas under the receiver-operating characteristic curves (AUC) were validated (Figure 2). CONCLUSIONS PSA density, age, and MRI suspicion score predict CaP on MRI-targeted and systematic biopsy. Our CaP probability nomogram may allow further individualization of the decision to perform PB in men with clinical suspicion of prostate cancer and no previous biopsy. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e701 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Marc Bjurlin More articles by this author James Wysock More articles by this author Saradwata Sakar More articles by this author Rajesh Venkataraman More articles by this author Neil Mendhiratta More articles by this author Xiaosong Meng More articles by this author Andrew Rosenkrantz More articles by this author William Huang More articles by this author Herbert Lepor More articles by this author Samir Taneja More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
Source: The Journal of Urology
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Funder: New York Academy of Medicine
OpenAlex ID: W2324858338