PD13-13 A FULLY AUTOMATED ARTIFICIAL INTELLIGENCE SUPPORT SYSTEM FOR URINE CYTOLOGY: MULTI-CENTER EXTERNAL VALIDATION STUDY

Abstract

You have accessJournal of UrologyCME1 Apr 2023PD13-13 A FULLY AUTOMATED ARTIFICIAL INTELLIGENCE SUPPORT SYSTEM FOR URINE CYTOLOGY: MULTI-CENTER EXTERNAL VALIDATION STUDY Masatomo Kaneko, Keisuke Tsuji, Yuki Harada, Atsuko Fujihara, Kengo Ueno, Masaya Nakanishi, Eiichi Konishi, Tetsuro Takamatsu, Satoshi Teramukai, Toshiko Ito-Ihara, Andre Abreu, and Osamu Ukimura Masatomo KanekoMasatomo Kaneko More articles by this author , Keisuke TsujiKeisuke Tsuji More articles by this author , Yuki HaradaYuki Harada More articles by this author , Atsuko FujiharaAtsuko Fujihara More articles by this author , Kengo UenoKengo Ueno More articles by this author , Masaya NakanishiMasaya Nakanishi More articles by this author , Eiichi KonishiEiichi Konishi More articles by this author , Tetsuro TakamatsuTetsuro Takamatsu More articles by this author , Satoshi TeramukaiSatoshi Teramukai More articles by this author , Toshiko Ito-IharaToshiko Ito-Ihara More articles by this author , Andre AbreuAndre Abreu More articles by this author , and Osamu UkimuraOsamu Ukimura More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003260.13AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: To validate a fully automated artificial intelligence (AI) support system for urine cytology to detect histological high-grade urothelial carcinoma (HGUC). METHODS: A total of 660 urine cytology slides were collected from the urine of consecutive patients just before bladder biopsy or transurethral resection of the bladder at 3 institutions (center 1, Kyoto Prefectural University of Medicine Hospital; center 2, Japanese Red Cross Kyoto Second Hospital; center 3, North Medical Center Kyoto Prefectural University of Medicine) between 2016 to 2022 (IRB ERB-C 1339-5, 1673-1, and S2020-60). Board-certified cytotechnologists and pathologist independently labeled each cell and classified each slide according to the Paris system (TPS). After excluding an unsatisfactory slide, cytology slides were digitized for image analysis. AI was developed with a deep learning method using 181 slides obtained in center 1. Urinary cells on a slide were automatically detected by AI, each cell was classified into benign vs malignant, the results of cell-level classifications were integrated, and AI classified each slide into negative (benign or atypical urothelial cells) vs positive (suspicious HGUC or HGUC). The model showing the highest accuracy in the training dataset was selected, and the slide classification performance was tested with 315 slides obtained from center 1 for internal validation and 163 slides obtained from centers 2 and 3 for external validation. The diagnostic performance to detect histological HGUC assessed with TPS and slide classification with AI were compared. The receiver operating characteristic (ROC) analysis was performed for the binary classification. Statistical significance defined as p<0.05. RESULTS: For slide classification, the area under the ROC (AUC) of AI was 0.93 in internal validation and 0.84 in external validation. The accuracy was 85% in both internal and external datasets. For diagnostic performance to detect histological HGUC, AUC was 0.78 in internal validation and 0.73 in external validation. The accuracy/sensitivity/specificity with TPS was 68%/46%/89% in internal dataset and 66%/35%/91% in external dataset. When the sensitivity was matched with TPS, the accuracy of AI was 68% (p=1.0) and 66% (p=1.0) and specificity was 89% (p=0.6) and 91% (p=0.7) for internal and external validation, respectively. CONCLUSIONS: A fully automated AI system accurately classifies the digitized urine cytology slides and detects histological HGUC with comparative performance to TPS in both internal and external validation. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e410 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Masatomo Kaneko More articles by this author Keisuke Tsuji More articles by this author Yuki Harada More articles by this author Atsuko Fujihara More articles by this author Kengo Ueno More articles by this author Masaya Nakanishi More articles by this author Eiichi Konishi More articles by this author Tetsuro Takamatsu More articles by this author Satoshi Teramukai More articles by this author Toshiko Ito-Ihara More articles by this author Andre Abreu More articles by this author Osamu Ukimura More articles by this author Expand All Advertisement PDF downloadLoading ...