Deep Learning-based Recalibration of the CUETO and EORTC Prediction Tools for Recurrence and Progression of Non-muscle-invasive Bladder Cancer.

Despite being standard tools for decision-making, the European Organisation for Research and Treatment of Cancer (EORTC), European Association of Urology (EAU), and Club Urologico Espanol de Tratamiento Oncologico (CUETO) risk groups provide moderate performance in predicting recurrence-free survival (RFS) and progression-free survival (PFS) in non-muscle-invasive bladder cancer (NMIBC). In this retrospective combined-cohort data-mining study, the training group consisted of 3570 patients with de novo diagnosed NMIBC. Predictors included gender, age, T stage, histopathological grading, tumor burden and diameter, EORTC and CUETO scores, and type of intravesical treatment. The models developed were externally validated using an independent cohort of 322 patients. Models were trained using Cox proportional-hazards deep neural networks (deep learning; DeepSurv) with a proprietary grid search of hyperparameters. For patients treated with surgery and bacillus Calmette-Guérin-treated patients, the models achieved a c index of 0.650 (95% confidence interval [CI] 0.649-0.650) for RFS and 0.878 (95% CI 0.873-0.874) for PFS in the training group. In the validation group, the c index was 0.651 (95% CI 0.648-0.654) for RFS and 0.881 (95% CI 0.878-0.885) for PFS. After inclusion of patients treated with mitomycin C, the c index for RFS models was 0.6415 (95% CI 0.6412-0.6417) for the training group and 0.660 (95% CI 0.657-0.664) for the validation group. Models for PFS achieved a c index of 0.885 (95% CI 0.885-0.885) for the training set and 0.876 (95% CI 0.873-0.880) for the validation set. Our tool outperformed standard-of-care risk stratification tools and showed no evidence of overfitting. The application is open source and available at https://biostat.umed.pl/deepNMIBC/. PATIENT SUMMARY: We created and validated a new tool to predict recurrence and progression of early-stage bladder cancer. The application uses advanced artificial intelligence to combine state-of-the-art scales, outperforms these scales for prediction, and is freely available online.

European urology oncology. 2021 Jun 03 [Epub ahead of print]

Mateusz Jobczyk, Konrad Stawiski, Marcin Kaszkowiak, Paweł Rajwa, Waldemar Różański, Francesco Soria, Shahrokh F Shariat, Wojciech Fendler

Department of Urology, Copernicus Memorial Hospital, Medical University of Lodz, Lodz, Poland; Department of Urology, The Hospital Ministry of the Interior and Administration, Lodz, Poland., Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland. Electronic address: ., Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland., Department of Urology, Medical University of Silesia, Zabrze, Poland; Department of Urology, Medical University of Vienna, Vienna, Austria., Department of Urology, Copernicus Memorial Hospital, Medical University of Lodz, Lodz, Poland., Department of Urology, Medical University of Vienna, Vienna, Austria; Division of Urology, Department of Surgical Sciences, Torino School of Medicine, Turin, Italy., Department of Urology, Medical University of Vienna, Vienna, Austria; Karl Landsteiner Institute of Urology and Andrology, Vienna, Austria; Department of Urology, Weill Cornell Medical College, New York, NY, USA; Department of Urology, University of Texas Southwestern, Dallas, TX, USA; Department of Urology, Second Faculty of Medicine, Charles University, Prague, Czech Republic; Institute for Urology and Reproductive Health, Sechenov University, Moscow, Russia., Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland; Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.