Mp30-07 Automated Renal Volume Measurement Using Artificial Intelligence: Correlation to Post-Operative Renal Function After Radical and Partial Nephrectomy

You have accessJournal of UrologyImaging/Uroradiology II (MP30)1 May 2024MP30-07 AUTOMATED RENAL VOLUME MEASUREMENT USING ARTIFICIAL INTELLIGENCE: CORRELATION TO POST-OPERATIVE RENAL FUNCTION AFTER RADICAL AND PARTIAL NEPHRECTOMY Abhinav Khanna, Vidit Sharma, Ekamjit S. Deol, Adriana Gregory, Harrison C. Gottlich, Cole Cook, Jason Klug, Christine Lohse, Theodora Potretzke, Aaron Potretzke, Stephen A. Boorjian, R. Houston Thompson, Andrew Rule, Naoki Takahashi, Alexander Denic, Bradley Erickson, Timothy Kline, and Bradley Leibovich Abhinav KhannaAbhinav Khanna , Vidit SharmaVidit Sharma , Ekamjit S. DeolEkamjit S. Deol , Adriana GregoryAdriana Gregory , Harrison C. GottlichHarrison C. Gottlich , Cole CookCole Cook , Jason KlugJason Klug , Christine LohseChristine Lohse , Theodora PotretzkeTheodora Potretzke , Aaron PotretzkeAaron Potretzke , Stephen A. BoorjianStephen A. Boorjian , R. Houston ThompsonR. Houston Thompson , Andrew RuleAndrew Rule , Naoki TakahashiNaoki Takahashi , Alexander DenicAlexander Denic , Bradley EricksonBradley Erickson , Timothy KlineTimothy Kline , and Bradley LeibovichBradley Leibovich View All Author Informationhttps://doi.org/10.1097/01.JU.0001009416.90901.7b.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Post-operative renal function (PORF) after radical nephrectomy (RN) and partial nephrectomy (PN) is correlated to the volume of parenchyma spared during surgery. However, the calculation of non-neoplastic renal volume on imaging is resource-intensive and does not translate readily into clinical practice. We examine the utility of a deep learning algorithm for automated renal volume calculation in predicting PORF following PN and RN. METHODS: We identified patients undergoing RN or PN at our tertiary referral center with accessible pre-operative CT images. We developed a novel deep learning algorithm using nnU-Net architecture to automatically measure ipsilateral renal volume (RV), contralateral RV, and kidney tumor volume on contrast-enhanced CT (Figure 1). The associations between algorithm-generated pre-operative non-neoplastic RV and observed PORF were assessed using generalized linear mixed effect models, adjusted for known clinical factors associated with PORF (age, diabetes, preoperative eGFR, proteinuria, tumor size, time from surgery). RESULTS: CT images from 1,077 patients, including 300 RN and 777 PN, were included. Mean (SD) contralateral RV as a split percentage was 53% (7) for RN and 50% (3) for PN. Mean (SD) contralateral RV as an absolute volume was 197 mL (52) for RN and 198 mL (51) for PN. Mean duration of follow-up from surgery was 45 (35) months for 3,073 postoperative eGFR assessments following RN and 50 (38) months for 7,478 postoperative eGFR assessments following PN. The mean (SD) number of postoperative eGFR measurements was 10 (9) per patient. When added to previously validated multivariable clinical models to predict PORF, higher AI-derived contralateral RV was independently associated with better PORF in RN (p<0.001) and PN (p<0.001). Each 10% increase in split contralateral RV was associated with a 6.5 and a 2.6 mL/min/1.73 m2 increase in PORF following RN and PN, respectively. CONCLUSIONS: Higher pre-operative non-neoplastic RV is associated with improved long-term renal function following RN and PN, even after adjusting for a previously validated comprehensive clinical prediction model. We developed an AI tool to automate measurement of non-neoplastic RV, which may facilitate integration of RV measurement into clinical practice. Download PPT Source of Funding: None © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e493 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Abhinav Khanna More articles by this author Vidit Sharma More articles by this author Ekamjit S. Deol More articles by this author Adriana Gregory More articles by this author Harrison C. Gottlich More articles by this author Cole Cook More articles by this author Jason Klug More articles by this author Christine Lohse More articles by this author Theodora Potretzke More articles by this author Aaron Potretzke More articles by this author Stephen A. Boorjian More articles by this author R. Houston Thompson More articles by this author Andrew Rule More articles by this author Naoki Takahashi More articles by this author Alexander Denic More articles by this author Bradley Erickson More articles by this author Timothy Kline More articles by this author Bradley Leibovich More articles by this author Expand All Advertisement PDF downloadLoading ...