Machine learning framework-based prognostic classifier for predicting recurrence-free survival in patients undergoing radical cystectomy for urothelial bladder cancer

You have accessJournal of UrologyCME1 Apr 2023MP56-19 MACHINE LEARNING FRAMEWORK-BASED PROGNOSTIC CLASSIFIER FOR PREDICTING RECURRENCE-FREE SURVIVAL IN PATIENTS UNDERGOING RADICAL CYSTECTOMY FOR UROTHELIAL BLADDER CANCER Giovanni Cacciamani, Yifan Xue, Udu Durairaj, Sidney Roberts, Dhruv Patel, Ragheb Raad, Gus Miranda, Sarmad Sadeghi, Andrew Hung, Inderbir Gill, Mihir Desai, Peter Kuhn, Jeremy Mason, and Assad Oberai Giovanni CacciamaniGiovanni Cacciamani More articles by this author , Yifan XueYifan Xue More articles by this author , Udu DurairajUdu Durairaj More articles by this author , Sidney RobertsSidney Roberts More articles by this author , Dhruv PatelDhruv Patel More articles by this author , Ragheb RaadRagheb Raad More articles by this author , Gus MirandaGus Miranda More articles by this author , Sarmad SadeghiSarmad Sadeghi More articles by this author , Andrew HungAndrew Hung More articles by this author , Inderbir GillInderbir Gill More articles by this author , Mihir DesaiMihir Desai More articles by this author , Peter KuhnPeter Kuhn More articles by this author , Jeremy MasonJeremy Mason More articles by this author , and Assad OberaiAssad Oberai More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003309.19AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: To assess the potential value of using machine learning (ML) approaches to derive risk prediction models for urothelial bladder cancer (BCa) recurrence at 1, 3, and 5 years after radical cystectomy (RC). METHODS: We used our established, IRB-approved (HS-01B014), longitudinally maintained, RC database of consecutive BCa primary surgical cases containing prospectively collected, detailed clinical, radiologic, and pathologic elements (years 1975-2016) to select patients with urothelial BCa. We included on only those with urothelial carcinoma histology treated with intent to cure. We excluded non-BCa primary patients undergoing RC for other pelvic malignancies. We further sub-divided the data into three groups with known recurrence-free survival (RFS) status at the 1-, 3-, and 5-year marks from the time of RC. The data was split into training (60%), validation (20%) and testing sets (20%). Separate classifiers for predicting 1-, 3-, and 5-year RFS were constructed using ML methods that included support vector machines, multilayer perceptrons, random forests (RFC), gradient boosting (GBC), extra trees (ExTC), and AdaBoost. RESULTS: Our analysis included 2152 patients with uBCa in our dataset, of which we have a minimum of 1 year of continuous data. The performance of the three top models in predicting 1-, 3-, and 5-year RFS is 0.882, 0.830, and 0.876 for RFC, 0.884, 0.849, and 0.874 for ExtC, and 0878, 0.828, and 0.872 for GBC, respectively. The AUC for the set of the top 12 features showed an accuracy between 0.827(95% CI 0.826-0.827) to 0.879 (95% CI 0.877-0.880). CONCLUSIONS: We report a ML-based framework, which incorporates disease and patient factors to predict 1, 3, and 5 years of RFS in patients undergoing RC for BCa with higher accuracy than the leading nomograms. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e782 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Giovanni Cacciamani More articles by this author Yifan Xue More articles by this author Udu Durairaj More articles by this author Sidney Roberts More articles by this author Dhruv Patel More articles by this author Ragheb Raad More articles by this author Gus Miranda More articles by this author Sarmad Sadeghi More articles by this author Andrew Hung More articles by this author Inderbir Gill More articles by this author Mihir Desai More articles by this author Peter Kuhn More articles by this author Jeremy Mason More articles by this author Assad Oberai More articles by this author Expand All Advertisement PDF downloadLoading ...