PD22-07 A MULTICENTER STUDY PRESENTING AN EXPLAINABLE AI APPROACH TO AUTOMATIC SEGMENTATION OF RENAL TUMORS ON COMPUTED TOMOGRAPHY

Abstract

You have accessJournal of UrologyCME1 Apr 2023PD22-07 A MULTICENTER STUDY PRESENTING AN EXPLAINABLE AI APPROACH TO AUTOMATIC SEGMENTATION OF RENAL TUMORS ON COMPUTED TOMOGRAPHY Annemarie Uhlig, Sophie Bachanek, Paul Romario Würzberg, Manuel Nietert, Lutz Trojan, Joachim Lotz, and Johannes Uhlig Annemarie UhligAnnemarie Uhlig More articles by this author , Sophie BachanekSophie Bachanek More articles by this author , Paul Romario WürzbergPaul Romario Würzberg More articles by this author , Manuel NietertManuel Nietert More articles by this author , Lutz TrojanLutz Trojan More articles by this author , Joachim LotzJoachim Lotz More articles by this author , and Johannes UhligJohannes Uhlig More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003295.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Automatic segmentation of renal tumors is a cornerstone for Artificial Intelligence (AI) based tumor diagnostics. This study presents an automatic segmentation algorithm and visualization method using CT-studies from renal tumor patients recruited in a multicenter setting. METHODS: Renal tumor patients diagnosed between 2018 and 2021 were retrospectively assessed for this study. Inclusion criteria were CT-imaging of renal tumors in corticomedullary (CM) or nephrogenic (NG) contrast media phase. Patients aged <18years, and those with cystic or infiltrative renal tumors (i.e. lymphomas) were excluded. Manual segmentation of the renal tumors was performed by a GU-radiologist on all axial CT slices. A convolutional neural network (UNET) was trained based on the radiologists´ manual segmentations. In an independent validation dataset, the accuracy of the UNETs predictions of renal tumor contours was compared to the references standard of manual segmentations and quantified using the DICE score. RESULTS: A total of n=394/ n=350 patients with renal tumors imaged in CM /NG phase, respectively, were included (median age 66y; 35% female; median tumor diameter 5.4cm). CT-studies from >20 radiological imaging centers were included with different imaging protocols and slice thickness. The UNET was trained on n=316 CM and n=294 NG contrast phase patients (n=7019 / n=6859 separate CT images). In the independent validation dataset (n=78 / n=56 patients with 1713 / 1298 CT images), the UNET achieved a DICE score of 0.88 and 0.90 for the corticomedullary and nephrogenic CM phase, respectively. The UNET predictions were visualized using a tile-based approach with color-coding and contour-lines that could be overlaid on CT-images to depict varying levels of prediction confidence. CONCLUSIONS: A UNET AI-approach yields a robust automatic delineation of renal tumors on CT-images acquired in clinical routine, irrespective of the contrast media phase. Using color-coding and contour-lines that could be overlaid on original CT-images provides an explainable approach to the UNETs predictions and might improve AI-acceptance in clinical practice. Source of Funding: This study received funding by the 2022 ESR Research Seed Grant. The 2022 ESR Research Seed Grants were kindly supported by an unrestricted, non-exclusive grant from GE Healthcare © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e667 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Annemarie Uhlig More articles by this author Sophie Bachanek More articles by this author Paul Romario Würzberg More articles by this author Manuel Nietert More articles by this author Lutz Trojan More articles by this author Joachim Lotz More articles by this author Johannes Uhlig More articles by this author Expand All Advertisement PDF downloadLoading ...