title: Cell-nuclear data reduction and prognostic model selection in bladder tumor recurrence.
abstract: OBJECTIVE
  The paper aims at improving the prediction of superficial bladder recurrence. To this end, feedforward neural networks (FNNs) and a feature selection method based on unsupervised clustering, were employed.
  
  
  METHODS
  A retrospective prognostic study of 127 patients diagnosed with superficial urinary bladder cancer was performed. Images from biopsies were digitized and cell nuclei features were extracted. To design FNN classifiers, different training methods and architectures were investigated. The unsupervised k-windows (UKW) and the fuzzy c-means clustering algorithms were applied on the feature set to identify the most informative feature subsets.
  
  
  RESULTS
  UKW managed to reduce the dimensionality of the feature space significantly, and yielded prediction rates 87.95% and 91.41%, for non-recurrent and recurrent cases, respectively. The prediction rates achieved with the reduced feature set were marginally lower compared to the ones attained with the complete feature set. The training algorithm that exhibited the best performance in all cases was the adaptive on-line backpropagation algorithm.
  
  
  CONCLUSIONS
  FNNs can contribute to the accurate prognosis of bladder cancer recurrence. The proposed feature selection method can remove redundant information without a significant loss in predictive accuracy, and thereby render the prognostic model less complex, more robust, and hence suitable for clinical use.
authors.affiliation: Computational Intelligence Laboratory, Department of Mathematics, University of Patras, GR-26110 Patras, Greece. dtas@math.upatras.gr
authors.forename: Dimitris K
authors.initials: DK
authors.lastname: Tasoulis
authors.forename: Panagiota
authors.initials: P
authors.lastname: Spyridonos
authors.forename: Nicos G
authors.initials: NG
authors.lastname: Pavlidis
authors.forename: Vassilis P
authors.initials: VP
authors.lastname: Plagianakos
authors.forename: Panagiota
authors.initials: P
authors.lastname: Ravazoula
authors.forename: Georgios
authors.initials: G
authors.lastname: Nikiforidis
authors.forename: Michael N
authors.initials: MN
authors.lastname: Vrahatis
mesh_terms: D000465:Algorithms; D002467:Cell Nucleus / Q000473:pathology*; D017143:Fuzzy Logic; D006801:Humans; D008954:Models, Biological; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis* / Q000473:pathology / Q000628:therapy; D009367:Neoplasm Staging; D011379:Prognosis; D001749:Urinary Bladder Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000473:pathology* / Q000628:therapy