title: Prediction of outcome in patients with urothelial carcinoma of the bladder following radical cystectomy using artificial neural networks.
abstract: OBJECTIVE
  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.
authors: Department of Urology, Ludwig-Maximilians-University Munich, Grosshadern, Marchioninistr. 15, 81377 Munich, Germany. alexander.buchner@med.uni-muenchen.de
authors: A
authors: A
authors: Buchner
authors: M
authors: M
authors: May
authors: M
authors: M
authors: Burger
authors: C
authors: C
authors: Bolenz
authors: E
authors: E
authors: Herrmann
authors: H-M
authors: HM
authors: Fritsche
authors: J
authors: J
authors: Ellinger
authors: T
authors: T
authors: Höfner
authors: P
authors: P
authors: Nuhn
authors: C
authors: C
authors: Gratzke
authors: S
authors: S
authors: Brookman-May
authors: S
authors: S
authors: Melchior
authors: J
authors: J
authors: Peter
authors: R
authors: R
authors: Moritz
authors: D
authors: D
authors: Tilki
authors: C
authors: C
authors: Gilfrich
authors: J
authors: J
authors: Roigas
authors: M
authors: M
authors: Zacharias
authors: M
authors: M
authors: Hohenfellner
authors: A
authors: A
authors: Haferkamp
authors: L
authors: L
authors: Trojan
authors: W F
authors: WF
authors: Wieland
authors: S C
authors: SC
authors: Müller
authors: C G
authors: CG
authors: Stief
authors: P J
authors: PJ
authors: Bastian
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002295:Carcinoma, Transitional Cell / Q000473:pathology* / Q000601:surgery; D015653:Cystectomy; D003936:Diagnosis, Computer-Assisted; D018572:Disease-Free Survival; D005260:Female; D005858:Germany; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011379:Prognosis; D016016:Proportional Hazards Models; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D001749:Urinary Bladder Neoplasms / Q000473:pathology* / Q000601:surgery