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Cell-nuclear data reduction and prognostic model selection in bladder tumor recurrence

Tasoulis, Dimitrios K and Spyridonos, P. and Pavlidis, Nicos and Cavouras, D. and Ravazoula, P. and Nikiforidis, G. and Vrahatis, Michael N. (2006) Cell-nuclear data reduction and prognostic model selection in bladder tumor recurrence. Artificial Intelligence in Medicine, 38 (3). pp. 291-303. ISSN 0933-3657

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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. Material and 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.

Item Type: Article
Journal or Publication Title: Artificial Intelligence in Medicine
Uncontrolled Keywords: Prognosis of cancer recurrence ; Neural networks ; Unsupervised clustering ; Feature selection
Subjects: UNSPECIFIED
Departments: Lancaster University Management School > Management Science
ID Code: 50920
Deposited By: ep_importer_pure
Deposited On: 09 Nov 2011 14:01
Refereed?: Yes
Published?: Published
Last Modified: 26 Jul 2012 19:45
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/50920

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