Buchner, A. 
; May, M. ; Burger, M. ; Bolenz, C. ; Herrmann, E. ; Fritsche, H.-M. ; Ellinger, J. ; Höfner, T. ; Nuhn, P. ; Gratzke, C. ; Brookman-May, S. ; Melchior, S. ; Peter, J. ; Moritz, R. ; Tilki, D. ; Gilfrich, C. ; Roigas, J. ; Zacharias, M. ; Hohenfellner, M. ; Haferkamp, A. ; Trojan, L. ; Wieland, W.F. ; Müller, S.C. ; Stief, C.G. ; Bastian, P.J.
; May, M. ; Burger, M. ; Bolenz, C. ; Herrmann, E. ; Fritsche, H.-M. ; Ellinger, J. ; Höfner, T. ; Nuhn, P. ; Gratzke, C. ; Brookman-May, S. ; Melchior, S. ; Peter, J. ; Moritz, R. ; Tilki, D. ; Gilfrich, C. ; Roigas, J. ; Zacharias, M. ; Hohenfellner, M. ; Haferkamp, A. ; Trojan, L. ; Wieland, W.F. ; Müller, S.C. ; Stief, C.G. ; Bastian, P.J. 
Abstract
Aim: The outcome of patients with urothelial carcinoma of the bladder (UCB) after radical cystectomy (RC) shows remarkable variability. We evaluated the ability of artificial neural networks (ANN) to perform risk stratification in UCB patients based on common parameters available at the time of RC. Methods: Data from 2111 UCB patients that underwent RC in eight centers were analysed; the median ...
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