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Eur J Surg Oncol. 2013 Apr;39(4):372-9. doi: 10.1016/j.ejso.2013.02.009.

Prediction of outcome in patients with urothelial carcinoma of the bladder following radical cystectomy using artificial neural networks.

Author information

1
Department of Urology, Ludwig-Maximilians-University Munich, Grosshadern, Marchioninistr. 15, 81377 Munich, Germany. alexander.buchner@med.uni-muenchen.de

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.

PMID:
23465180
DOI:
10.1016/j.ejso.2013.02.009
[Indexed for MEDLINE]
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