You have accessJournal of UrologyCME1 Apr 2023MP09-14 RADIOMIC ANALYSIS TO DISTINGUISH BETWEEN UPPER TRACT UROTHELIAL CARCINOMA AND RENAL CELL CARCINOMA: CAN WE IMPROVE IMAGING USING MACHINE LEARNING? Severin Rodler, Mona Rzany, Alexander Buchner, Christian Stief, Matthias Fabritius, Michael Winkelmann, Philipp Kazmierczak, and Julian Marcon Severin RodlerSeverin Rodler More articles by this author , Mona RzanyMona Rzany More articles by this author , Alexander BuchnerAlexander Buchner More articles by this author , Christian StiefChristian Stief More articles by this author , Matthias FabritiusMatthias Fabritius More articles by this author , Michael WinkelmannMichael Winkelmann More articles by this author , Philipp KazmierczakPhilipp Kazmierczak More articles by this author , and Julian MarconJulian Marcon More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003224.14AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Imaging to reliably differentiate between upper tract urothelial carcinoma (UTUC) and renal cell carcinoma (RCC) is limited in its accuracy. Radiomics uses high-throughput extraction of data from imaging series, while machine learning can be applied to identify radiomic features predictive of clinical endpoints. We aim to assess whether Radiomics and machine learning approaches might improve preoperative diagnostics to distinguish between UTUC and RCC. METHODS: After IRB approval, we queried our internal database. Inclusion criteria were the diagnosis of a pathologically confirmed RCC or UTUC and available CT scans in the venous phase. Manual tumor segmentation in the axial plane was carried out by a board-certified radiologist with more than 7 years of experience following a standardized approach using imaging software mint LesionTM (Mint Medical GmbH, Heidelberg, Germany). To test for inter-reader variability (IRV), we randomly selected 30% of imaging series for which segmentation was repeated by a second board-certified radiologist. Intraclass correlation coefficient was performed to calculate IRV. Lasso regression and cross validation analyses were performed to identify predictive features to differentiate between UTUC and RCC. ROC analysis was then carried out as a goodness-of-fit test regarding predictive ability of the radiomic score. RESULTS: 236 patients were included in this study of which 119 (50.4%) presented with RCC and 116 (49.6%) with UTUC. All patients were treated at our academic center between 2005 and 2021. 72 patients were female (30.5%) and 164 (69.5%) were male. The median age of the study cohort was 70.5 (IQR: 59.5-77) years. An IRV > 80% was determined for a total of 24 radiomic features. Using the radiomic score, a differentiation between UTUC and RCC was possible with a sensitivity of 88.4% and a specificity of 81% for the training cohort. For the test cohort, a sensitivity of 81% and a specificity of 80% was observed (Figure 1). CONCLUSIONS: Using radiomic analysis and a machine learning approach, we could reliably distinguish between RCC and UTUC. Further studies are required to determine the role of radiomic analysis and machine learning in routine imaging of unclear renal masses. Source of Funding: none © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e110 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Severin Rodler More articles by this author Mona Rzany More articles by this author Alexander Buchner More articles by this author Christian Stief More articles by this author Matthias Fabritius More articles by this author Michael Winkelmann More articles by this author Philipp Kazmierczak More articles by this author Julian Marcon More articles by this author Expand All Advertisement PDF downloadLoading ...