You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging I1 Apr 2016MP73-03 COMPARISON OF ACCURACY OF RISK-STRATIFICATION OF SMALL RENAL MASSES (SRMS) WITH ALGORITHM BASED ON PERCUTANEOUS RENAL MASS BIOPSY (RMB) AND MASS SIZE VERSUS NOMOGRAMS BASED ON R.E.N.A.L. NEPHROMETRY SCORE (RNS) Takahiro Osawa, Khaled S. Hafez, David C. Miller, Jeffrey S. Montgomery, Todd M. Morgan, Ganesh S. Palapattu, Alon Z. Weizer, Elaine M. Caoili, James H. Ellis, Lakshmi P. Kunju, and J. Stuart Wolf Takahiro OsawaTakahiro Osawa More articles by this author , Khaled S. HafezKhaled S. Hafez More articles by this author , David C. MillerDavid C. Miller More articles by this author , Jeffrey S. MontgomeryJeffrey S. Montgomery More articles by this author , Todd M. MorganTodd M. Morgan More articles by this author , Ganesh S. PalapattuGanesh S. Palapattu More articles by this author , Alon Z. WeizerAlon Z. Weizer More articles by this author , Elaine M. CaoiliElaine M. Caoili More articles by this author , James H. EllisJames H. Ellis More articles by this author , Lakshmi P. KunjuLakshmi P. Kunju More articles by this author , and J. Stuart WolfJ. Stuart Wolf More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.1658AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Introduction: A previously-published risk-stratification algorithm based on RMB and radiographic mass size was useful in designating surveillance versus the need for immediate treatment of SRMs. Nonetheless, there were some incorrect assignments, most notably when RMB indicated low-risk malignancy but final pathology revealed high-risk malignancy. METHODS For 202 clinically localized SRMs in 200 patients with RNS, preoperative RMB, and final pathology, we assessed the accuracy of management assignment (surveillance versus treatment) by the RMB-based algorithm and the RNS system (RNS, RNS components, and nomogram based on RNS). Prognostic ability of assignment was compared between these two methods. In addition, Logistic regression was used to determine if adding RNS system to the RMB-based algorithm could improve assignment. RESULTS Of the 202 SRMs, 53 (26%) were assigned to surveillance and 149 (74%) were assigned to treatment by the RMB-based algorithm. Of the 53 assigned to surveillance, 25 (47%) had benign/favorable RMB histology and 28 (53%) had intermediate RMB histology with mass size < 2 cm. Of these 53 masses, 9 (17%) were incorrectly assigned to surveillance, in that final pathology indicated need for treatment. Of the 9 masses incorrectly assigned to surveillance, 3 were < 2 cm with intermediate-risk histology on RMB and 6 were 2-4 cm with benign- or favorable-risk histology on RMB. Of the former, all 3 had unfavorable final pathology, and of the latter 2 had intermediate final pathology and 4 had unfavorable final pathology (Table). Final pathology confirmed correct assignment in all 149 masses assigned to treatment. RNS system didn't improve overall assignment with statistical significance. In addition, the AUC of the RMB-based algorithm and the RNS nomograms and for predicting the accurate assignment were 0.65 and 0.97, respectively. CONCLUSIONS The RMB-based algorithm is not only superior to RNS system for risk-stratification of SRMs, but RNS system do not improve the performance of RMB-based algorithm. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e961 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Takahiro Osawa More articles by this author Khaled S. Hafez More articles by this author David C. Miller More articles by this author Jeffrey S. Montgomery More articles by this author Todd M. Morgan More articles by this author Ganesh S. Palapattu More articles by this author Alon Z. Weizer More articles by this author Elaine M. Caoili More articles by this author James H. Ellis More articles by this author Lakshmi P. Kunju More articles by this author J. Stuart Wolf More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...