MP56-09 A RISK CALCULATOR INCORPORATING MRI-GUIDED BIOPSY DATA TO PREDICT CLINICALLY SIGNIFICANT PROSTATE CANCER

Abstract

You have accessJournal of UrologyProstate Cancer: Detection & Screening IV (MP56)1 Apr 2020MP56-09 A RISK CALCULATOR INCORPORATING MRI-GUIDED BIOPSY DATA TO PREDICT CLINICALLY SIGNIFICANT PROSTATE CANCER Adam Kinnaird*, Alan Priester, Ryan Chuang, Danielle Barsa, Merdie Delfin, Anthony Sisk, Ely Felker, Lorna Kwan, and Leonard Marks Adam Kinnaird*Adam Kinnaird* More articles by this author , Alan PriesterAlan Priester More articles by this author , Ryan ChuangRyan Chuang More articles by this author , Danielle BarsaDanielle Barsa More articles by this author , Merdie DelfinMerdie Delfin More articles by this author , Anthony SiskAnthony Sisk More articles by this author , Ely FelkerEly Felker More articles by this author , Lorna KwanLorna Kwan More articles by this author , and Leonard MarksLeonard Marks More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000925.09AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: MRI guidance provides increased accuracy of prostate biopsy, and use of the PIRADS score helps determine cancer likelihood. However, when used alone the PIRADS score, especially in the mid-ranges, is only moderately predictive of clinically-significant prostate cancer (csPCa). Thus, we aimed to create a predictive index which combines MRI and clinical characteristics, potentially allowing a patient to rationally answer the question, “Do I Need a Biopsy?” METHODS: Subjects were all men undergoing MRI-guided biopsy to rule out csPCa at UCLA (9/09-4/19), excluding only men with previously diagnosed PCa. Mean (SD) age was 64.8 (7.7). 66% were Caucasian. Biopsy strategy combined targeted and systematic sampling (JAMA Surg online 6-12-19). csPCa was defined as Grade Group ≥2. A prediction tool was created from a logistic regression model that included age, race, family history, abnormality on rectal exam, PSA, prostate volume, PSA density, previous negative biopsy, MRI score, and longest diameter of region of interest (ROI) on MRI. Clinical characteristics were chosen a priori based on previous research. ROC curves were generated to display the relative ability of each variable to predict csPCa. RESULTS: Of the 2643 men undergoing biopsy, 1605 were eligible for the study. csPCa was detected in 683 (43%). Odds ratios of clinical and radiological variables included in the creation of the risk calculator are listed in Table 1. The overall AUC of the prediction tool (Figure 1) was superior to AUC of MRI score alone (0.874 vs. 0.792; p<0.0001). Inclusion of the MRI-visible lesion’s longest diameter into the model, regardless of MRI score, improved prediction of csPCa at MRI-guided biopsy. CONCLUSIONS: A risk calculator has been created for prediction of csPCa (AUC = 0.874), based on MRI and clinical data and confirmed by MRI-guided biopsy. The tool can help rationalize biopsy decisions, beyond other inputs currently available. Multi-site prospective testing of the tool is in progress. Source of Funding: R01CA218547, R01CA195505 © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e850-e851 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Adam Kinnaird* More articles by this author Alan Priester More articles by this author Ryan Chuang More articles by this author Danielle Barsa More articles by this author Merdie Delfin More articles by this author Anthony Sisk More articles by this author Ely Felker More articles by this author Lorna Kwan More articles by this author Leonard Marks More articles by this author Expand All Advertisement PDF downloadLoading ...