You have accessJournal of UrologyCME1 May 2022MP47-01 DEVELOPMENT OF INDIVIDUAL LEVEL INFERENCE AVAILABLE EXPLAINABLE ARTIFICIAL INTELLIGENCE MODEL FOR CANCER-SPECIFIC SURVIVAL AFTER NEPHRECTOMY IN RENAL CELL CARCINOMA PATIENTS Jungyo Suh, Dongwon Kim, Bumjin Lim, Cheryn Song, Dalsan You, In Gab Jeong, Jun Hyuk Hong, Bumsik Hong, Hanjong Ahn, and Choung-Soo Kim Jungyo SuhJungyo Suh More articles by this author , Dongwon KimDongwon Kim More articles by this author , Bumjin LimBumjin Lim More articles by this author , Cheryn SongCheryn Song More articles by this author , Dalsan YouDalsan You More articles by this author , In Gab JeongIn Gab Jeong More articles by this author , Jun Hyuk HongJun Hyuk Hong More articles by this author , Bumsik HongBumsik Hong More articles by this author , Hanjong AhnHanjong Ahn More articles by this author , and Choung-Soo KimChoung-Soo Kim More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002618.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: This study aim to development individual level inference available explainable artificial intelligence (XAI) model for the cancer specific survival (CSS) prediction for renal cell carcinoma patient who underwent nephrectomy. METHODS: A single centre, nephrectomy registry data from 1990 to 2016 were retrospectively reviewed for this analysis. Clinical parameters (age, sex, body mass index), surgical parameter (operation methods) and pathologic feature (histologic type, T stage, N stage, M stage, tumor thrombus, Fuhrman Grade) were included for analysis. Train/valid and test set was split with 8:1:1 ratio. Accelerated failure time (AFT) model for CSS was developed by decision tree-based ensemble machine learning method. Hyperparameters of the model were optimized by Bayesian method. Concordance index(c-index) was evaluated for accuracy of the estimated survival time of the developed model. Global and individual level inference were simulated and displayed by shapely additive value RESULTS: From 4969 nephrectomy patients, 4688 were eligible for analysis. Average age was 54.8±12.2 year-old and average survival time was 47.7±38.8 months. After hyperparameter optimization, the c-index of the final developed model was 0.932, 0.849 and 0.832 for each of train, validation, and test dataset. CONCLUSIONS: We successfully developed XAI model with acceptable level performance for the cancer specific survival prediction for renal cell carcinoma patient. Source of Funding: None. © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e809 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Jungyo Suh More articles by this author Dongwon Kim More articles by this author Bumjin Lim More articles by this author Cheryn Song More articles by this author Dalsan You More articles by this author In Gab Jeong More articles by this author Jun Hyuk Hong More articles by this author Bumsik Hong More articles by this author Hanjong Ahn More articles by this author Choung-Soo Kim More articles by this author Expand All Advertisement PDF DownloadLoading ...