MP65-16 AI DRIVEN ASSESSMENT OF BODY COMPOSITION PARAMETERS IN RADICAL CYSTECTOMY PATIENTS: PREDICTORS OF 90-DAY COMPLICATIONS

Abstract

You have accessJournal of UrologyCME1 Apr 2023MP65-16 AI DRIVEN ASSESSMENT OF BODY COMPOSITION PARAMETERS IN RADICAL CYSTECTOMY PATIENTS: PREDICTORS OF 90-DAY COMPLICATIONS Anthony Fadel, Vidit Sharma, Matthew K. Tollefson, Daniel J. Blezek, Robert F. Tarrell, Prabin Thapa, Lyndsay D. Viers, Aaron M. Potretzke, Stephen A. Boorjian, Igor Frank, Robert P. Hartman, and Boyd R. Viers Anthony FadelAnthony Fadel More articles by this author , Vidit SharmaVidit Sharma More articles by this author , Matthew K. TollefsonMatthew K. Tollefson More articles by this author , Daniel J. BlezekDaniel J. Blezek More articles by this author , Robert F. TarrellRobert F. Tarrell More articles by this author , Prabin ThapaPrabin Thapa More articles by this author , Lyndsay D. ViersLyndsay D. Viers More articles by this author , Aaron M. PotretzkeAaron M. Potretzke More articles by this author , Stephen A. BoorjianStephen A. Boorjian More articles by this author , Igor FrankIgor Frank More articles by this author , Robert P. HartmanRobert P. Hartman More articles by this author , and Boyd R. ViersBoyd R. Viers More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003323.16AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Sarcopenia is associated with increased mortality after radical cystectomy (RCx). Traditional imaging techniques used to assess sarcopenia are time consuming and labor intensive. Herein we demonstrate the utility of an AI algorithm with deep learning to analyze CT scans and produce body composition parameters in a time-efficient manner. This allows for outcome prediction and correlation of body measures to post-RCx complications. METHODS: Perioperative CT images for 843 RCx patients from 2009-2017 were collected from our institution. An AI algorithm was developed to extract muscle and adipose tissue parameters from 2D axial images at the L3 level. The following areas were segmented: skeletal muscle (SM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). Skeletal muscle index (SMI) and fat mass index (FMI) were then calculated. All measures were correlated with post-RCx complications using multivariable logistic regression analysis. RESULTS: There was significant variation in pre-operative body composition (Figure 1). An FMI>208 was associated with significantly more wound complications (40% vs 19%, p<.001) while an FMI>260 was associated with more infectious complications (38% vs 21%, p=.003). After adjusting for patient characteristics, these associations of FMI were maintained on multivariable analysis for more infectious (Odds ratio (OR) 1.004, p=.002) and wound (OR 1.006, p<.001) complications. When examining the components of FMI, SAT was independently associated with more wound complications (OR 1.003, p=.006) whereas VAT was independently associated with increased odds of 90-day infectious complications (OR 1.002, p=.011). Similarly, an SMI<42 was associated with major complications (28% vs 17%, p=.002), and on multivariable analysis higher pre-operative SMI was associated with lower odds of major complications (OR 0.972, p=.008). CONCLUSIONS: An AI algorithm was successfully able to segment body composition areas of adipose and skeletal muscle tissues. Sarcopenia assessment using this AI technology is now clinically feasible. Changes in body parameters corresponded with changes in body indices and were predictive of wound, infectious, and major complications. Source of Funding: None. © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e897 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Anthony Fadel More articles by this author Vidit Sharma More articles by this author Matthew K. Tollefson More articles by this author Daniel J. Blezek More articles by this author Robert F. Tarrell More articles by this author Prabin Thapa More articles by this author Lyndsay D. Viers More articles by this author Aaron M. Potretzke More articles by this author Stephen A. Boorjian More articles by this author Igor Frank More articles by this author Robert P. Hartman More articles by this author Boyd R. Viers More articles by this author Expand All Advertisement PDF downloadLoading ...