Prognostic value of histology and lymph node status in bilharziasis-bladder cancer: Outcome prediction using neural networks

W. Ji, R.N.G. Naguib, D. Petrovic, E. Gaura, M.A. Ghoneim

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

In this paper, the evaluation of two features in predicting the outcomes of patients with bilharziasis bladder cancer has been investigated using an RBF neural network. Prior to prediction, the feature subsets were extracted from the whole set of features for the purpose of providing a high performance of the network. Throughout the analysis of the prognostic feature combinations, two features, histological type and lymph node status, have been identified as the important indicators for outcome prediction of this type of cancer. The highest predictive accuracy reached 85.0% in this study.
Original languageEnglish
Title of host publicationProceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001
PublisherIEEE
Pages3870-3873
Number of pages4
DOIs
Publication statusPublished - 7 Nov 2002
EventAnnual International Conference of the IEEE Engineering in Medicine and Biology Society - Istanbul, Turkey
Duration: 25 Oct 200128 Oct 2001
Conference number: 23

Conference

ConferenceAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
CountryTurkey
CityIstanbul
Period25/10/0128/10/01

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Histology
Neural networks
Set theory

Keywords

  • Lymph nodes
  • Cancer
  • Bladder
  • Neural networks
  • Accuracy
  • Feature extraction
  • Pathology
  • History
  • Tumors
  • feature extraction
  • cancer
  • biological tissues
  • medical diagnostic computing
  • radial basis function networks
  • data set partition
  • bilharziasis bladder cancer prognosis
  • outcome prediction
  • RBF neural network
  • survival analysis
  • epidemiology
  • schistosomiasis
  • histology
  • lymph node status
  • feature subset extraction
  • prognostic feature combinations
  • predictive accuracy
  • pathological markers

Cite this

Ji, W., Naguib, R. N. G., Petrovic, D., Gaura, E., & Ghoneim, M. A. (2002). Prognostic value of histology and lymph node status in bilharziasis-bladder cancer: Outcome prediction using neural networks. In Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001 (pp. 3870-3873). IEEE. https://doi.org/10.1109/IEMBS.2001.1019685

Prognostic value of histology and lymph node status in bilharziasis-bladder cancer : Outcome prediction using neural networks. / Ji, W.; Naguib, R.N.G.; Petrovic, D.; Gaura, E.; Ghoneim, M.A.

Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001. IEEE, 2002. p. 3870-3873.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Ji, W, Naguib, RNG, Petrovic, D, Gaura, E & Ghoneim, MA 2002, Prognostic value of histology and lymph node status in bilharziasis-bladder cancer: Outcome prediction using neural networks. in Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001. IEEE, pp. 3870-3873, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, 25/10/01. https://doi.org/10.1109/IEMBS.2001.1019685
Ji W, Naguib RNG, Petrovic D, Gaura E, Ghoneim MA. Prognostic value of histology and lymph node status in bilharziasis-bladder cancer: Outcome prediction using neural networks. In Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001. IEEE. 2002. p. 3870-3873 https://doi.org/10.1109/IEMBS.2001.1019685
Ji, W. ; Naguib, R.N.G. ; Petrovic, D. ; Gaura, E. ; Ghoneim, M.A. / Prognostic value of histology and lymph node status in bilharziasis-bladder cancer : Outcome prediction using neural networks. Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001. IEEE, 2002. pp. 3870-3873
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abstract = "In this paper, the evaluation of two features in predicting the outcomes of patients with bilharziasis bladder cancer has been investigated using an RBF neural network. Prior to prediction, the feature subsets were extracted from the whole set of features for the purpose of providing a high performance of the network. Throughout the analysis of the prognostic feature combinations, two features, histological type and lymph node status, have been identified as the important indicators for outcome prediction of this type of cancer. The highest predictive accuracy reached 85.0{\%} in this study.",
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