MP54-13 EVALUATING THE VALUE OF MRI IN PREDICTING RADICAL PROSTATECTOMY PATHOLOGIC OUTCOMES

You have accessJournal of UrologyProstate Cancer: Localized: Surgical Therapy III (MP54)1 Apr 2019MP54-13 EVALUATING THE VALUE OF MRI IN PREDICTING RADICAL PROSTATECTOMY PATHOLOGIC OUTCOMES Adharsh Murali, Sean R. Meyer, Arvin George, Nnenaya Agochukwu, Ji Qi, Tae Kim, Prasad Shankar, Matthew Davenport, Karandeep Singh*, and for the Michigan Urological Surgery Improvement Collaborative Adharsh MuraliAdharsh Murali More articles by this author , Sean R. MeyerSean R. Meyer More articles by this author , Arvin GeorgeArvin George More articles by this author , Nnenaya AgochukwuNnenaya Agochukwu More articles by this author , Ji QiJi Qi More articles by this author , Tae KimTae Kim More articles by this author , Prasad ShankarPrasad Shankar More articles by this author , Matthew DavenportMatthew Davenport More articles by this author , Karandeep Singh*Karandeep Singh* More articles by this author , and for the Michigan Urological Surgery Improvement Collaborative More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000556682.24083.d2AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Prediction of pathologic outcomes of radical prostatectomy (RP) is a potentially important source of information to assist counseling, decision-making, and potentially guide operative planning. While predictions based on clinical, laboratory, and biopsy information have proved useful, we sought to evaluate whether multiparametric prostate magnetic resonance imaging (mpMRI) has a role in the prediction of pathologic outcomes. METHODS: The Michigan Urological Surgery Improvement Collaborative (MUSIC) is a consortium of 44 diverse urology practices that maintains a prospective registry of men with prostate cancer (CaP) with high-quality, validated data abstraction. Using information at a single center (University of Michigan) matched to data from the MUSIC registry, we developed random forest models to predict pathologic outcomes--non-organ confined disease (NOCD), extraprostatic extension (EPE), seminal vesicle invasion (SVI), or lymph node involvement (LNI)--at the time of surgery. We compared models developed using traditional predictors (clinical T-stage, PSA, biopsy Gleason score, the number of positive and total cores on biopsy), mpMRI predictors (highest PI-RADS score, number of lesions, and appearance of lymph nodes), and both. We used 10-fold cross-validated (CV) area-under-the-curve (AUC) to assess model discrimination. RESULTS: We identified 392 men who underwent RP as primary treatment for CaP between 2015 and 2018 preceded by mpMRI. Traditional predictors exhibited excellent discriminative ability for predicting pathologic outcomes (Table 1). The addition of mpMRI information to traditional predictors minimally improved the prediction of EPE, SVI, and NOCD and slightly worsened prediction of LNI. CONCLUSIONS: The addition of mpMRI information provides minimal additional value in predicting RP pathologic outcomes beyond traditional predictors. Source of Funding: Blue Cross and Blue Shield of Michigan and grant K12 DK111011 from the National Institute of Diabetes and Digestive and Kidney Disease Ann Arbor, MI© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e789-e789 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Adharsh Murali More articles by this author Sean R. Meyer More articles by this author Arvin George More articles by this author Nnenaya Agochukwu More articles by this author Ji Qi More articles by this author Tae Kim More articles by this author Prasad Shankar More articles by this author Matthew Davenport More articles by this author Karandeep Singh* More articles by this author for the Michigan Urological Surgery Improvement Collaborative More articles by this author Expand All Advertisement PDF downloadLoading ...