Buchner, A. and May, M. and Burger, M. and Bolenz, C. and Herrmann, E. and Fritsche, H. -M. and Ellinger, J. and Hoefner, T. and Nuhn, P. and Gratzke, C. and Brookman-May, S. and Melchior, S. and Peter, J. and Moritz, R. and Tilki, D. and Gilfrich, C. and Roigas, J. and Zacharias, M. and Hohenfellner, M. and Haferkamp, A. and Trojan, L. and Wieland, W. F. and Muller, S. C. and Stief, C. G. and Bastian, P. J. (2013) Prediction of outcome in patients with urothelial carcinoma of the bladder following radical cystectomy using artificial neural networks. EJSO, 39 (4). pp. 372-379. ISSN 0748-7983, 1532-2157
Full text not available from this repository. (Request a copy)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 follow-up was 30 months (IQR: 12-60). Age, gender, tumour stage and grade (TURB/RC), carcinoma in situ (TURB/RC), lymph node status, and lymphovascular invasion were used as input data for the ANN. Endpoints were tumour recurrence, cancer-specific mortality (CSM) and all-cause death (ACD). Additionally, the predictive accuracies (PA) of the ANNs were compared with the PA of Cox proportional hazards regression models. Results: The recurrence-, CSM-, and ACD- rates after 5 years were 36%, 33%, and 46%, respectively. The best ANN had 74%, 76% and 69% accuracy for tumour recurrence, CSM and ACD, respectively. Lymph node status was one of the most important factors for the network's decision. The PA of the ANNs for recurrence, CSM and ACD were improved by 1.6% (p = 0.247), 4.7% (p < 0.001) and 3.5% (p = 0.007), respectively, in comparison to the Cox models. Conclusions: ANN predicted tumour recurrence, CSM, and ACD in UCB patients after RC with reasonable accuracy. In this study, ANN significantly outperformed the Cox models regarding prediction of CSM and ACD using the same patients and variables. ANNs are a promising approach for individual risk stratification and may optimize individual treatment planning. (C) 2013 Elsevier Ltd. All rights reserved.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | EXTERNAL VALIDATION; PROSTATE-CANCER; RECURRENCE; SURVIVAL; IMPROVE; REGRESSION; NOMOGRAMS; MORTALITY; ACCURACY; Radical cystectomy; Outcome; Artificial neural network; Bladder cancer; Predictive accuracy |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Urologie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 21 Apr 2020 06:10 |
| Last Modified: | 21 Apr 2020 06:10 |
| URI: | https://pred.uni-regensburg.de/id/eprint/16949 |
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