MP33-08 DEEP LEARNING RENAL VOLUME ANALYSIS TO PREDICT LONG-TERM RENAL FUNCTION AFTER PARTIAL AND RADICAL NEPHRECTOMY

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You have accessJournal of UrologyCME1 May 2022MP33-08 DEEP LEARNING RENAL VOLUME ANALYSIS TO PREDICT LONG-TERM RENAL FUNCTION AFTER PARTIAL AND RADICAL NEPHRECTOMY Abhinav Khanna, Vidit Sharma, Adriana Gregory, Christine Lohse, Harrison C. Gottlich, Theodora Potretzke, R. Houston Thompson, Stephen A. Boorjian, Bradley Leibovich, Timothy Kline, and Aaron Potretzke Abhinav KhannaAbhinav Khanna More articles by this author , Vidit SharmaVidit Sharma More articles by this author , Adriana GregoryAdriana Gregory More articles by this author , Christine LohseChristine Lohse More articles by this author , Harrison C. GottlichHarrison C. Gottlich More articles by this author , Theodora PotretzkeTheodora Potretzke More articles by this author , R. Houston ThompsonR. Houston Thompson More articles by this author , Stephen A. BoorjianStephen A. Boorjian More articles by this author , Bradley LeibovichBradley Leibovich More articles by this author , Timothy KlineTimothy Kline More articles by this author , and Aaron PotretzkeAaron Potretzke More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002587.08AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Post-operative renal function (PORF) following extirpative renal surgery is largely dependent upon pre-operative renal function and the amount of renal parenchyma spared. The latter is often difficult to quantify. Some authors have suggested that renal volume on cross-sectional imaging may correlate with PORF. However, the calculation of renal volume is resource-intensive and does not translate readily into clinical practice. We aim to develop a deep learning algorithm capable of automatically calculating renal volume based on pre-operative MRI images. METHODS: We identified patients undergoing partial nephrectomy (PN) or radical nephrectomy (RN) at our tertiary referral center with accessible pre-operative MRI images. We developed a novel deep learning algorithm using U-Net architecture to identify kidneys on T2-weighted MRI and quantify non-neoplastic renal parenchymal volume (RV). The cohort was divided into a 74/13/13% split of training/validation/test subsets. Model development was carried out using a 5-fold cross validation technique. An ensemble of the three best performing models on the training and validation subsets was implemented to generate a more robust prediction segmentation. The associations between height-normalized pre-operative RV and 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: MRI images from from 330 patients, including 208 PN and 122 RN were used to develop a deep learning algorithm with a final Dice coefficient of 0.93 and Jaccard index of 0.87 compared to manual segmentations (Figure 1). On unadjusted analyses, RV was associated with PORF following PN and RN (p <0.001 and p=0.008, respectively). When added to existing multivariable models to predict PORF, the associations between RV and PORF remained statistically significant (p <0.001 and p=0.05, respectively). CONCLUSIONS: Pre-operative non-neoplastic renal volume is associated with long-term renal function following PN and RN, even after adjusting for a previously validated clinical prediction model. We developed a deep learning tool to facilitate automated RV assessment, which may promote integration of RV measurement into clinical practice. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e572 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Abhinav Khanna More articles by this author Vidit Sharma More articles by this author Adriana Gregory More articles by this author Christine Lohse More articles by this author Harrison C. Gottlich More articles by this author Theodora Potretzke More articles by this author R. Houston Thompson More articles by this author Stephen A. Boorjian More articles by this author Bradley Leibovich More articles by this author Timothy Kline More articles by this author Aaron Potretzke More articles by this author Expand All Advertisement PDF DownloadLoading ...