Soft feature evaluation indices for the identification of significant image cytometric factors in assessment of nodal involvement in breast cancer patients

H. Seker, M.O. Odetayo, Dobrila petrovic, R.N.G. Naguib

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

1 Citation (Scopus)

Abstract

This paper is focused on statistical, artificial neural networks and fuzzy logic based feature evaluation indices for determining the most/least clinically significant image cytometric prognostic factors for assessment of nodal involvement in breast cancer patients. Seven different prognostic factors, {tumour type, tumour grade, DNA ploidy, S-phase faction, G0G1/G2M ratio, minimum (start) and maximum (end) nuclear pleomorphism indices}, are assessed by means of a multilayer feedforward backpropagation neural networks based feature evaluation index as an artificial neural network approach, a fuzzy logic-based feature evaluation index derived from the fuzzy k-nearest neighbour classifier as a fuzzy logic method, and a logistic regression-based statistical analysis. The results suggest that the artificial neural network and fuzzy based indices may be more reliable than their statistical counterpart. Overall results obtained for all the three methods highlight the fact that only one method's outcome may not be adequate to reliably determine the most/least clinically important factors for assessment of nodal involvement in breast cancer patients. Our results appear to suggest that S-phase fraction and tumour type may be the most and least clinically significant markers, respectively, and should be closely investigated for the assessment of breast cancer nodal involvement
Original languageEnglish
Title of host publicationFuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on
PublisherIEEE
Pages1592-1595
Volume2
ISBN (Print)0-7803-7280-8
DOIs
Publication statusPublished - 2002

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Fuzzy logic
Neural networks
Tumors
Backpropagation
Logistics
Statistical methods
Multilayers
DNA
Classifiers

Bibliographical note

This paper is not available on the repository. The paper was given at the Fuzzy Systems, 2002. FUZZ-IEEE'02., 12-17 May 2002

Cite this

Seker, H., Odetayo, M. O., petrovic, D., & Naguib, R. N. G. (2002). Soft feature evaluation indices for the identification of significant image cytometric factors in assessment of nodal involvement in breast cancer patients. In Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on (Vol. 2, pp. 1592-1595). IEEE. https://doi.org/10.1109/FUZZ.2002.1006744

Soft feature evaluation indices for the identification of significant image cytometric factors in assessment of nodal involvement in breast cancer patients. / Seker, H.; Odetayo, M.O.; petrovic, Dobrila; Naguib, R.N.G.

Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on. Vol. 2 IEEE, 2002. p. 1592-1595.

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

Seker, H, Odetayo, MO, petrovic, D & Naguib, RNG 2002, Soft feature evaluation indices for the identification of significant image cytometric factors in assessment of nodal involvement in breast cancer patients. in Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on. vol. 2, IEEE, pp. 1592-1595. https://doi.org/10.1109/FUZZ.2002.1006744
Seker H, Odetayo MO, petrovic D, Naguib RNG. Soft feature evaluation indices for the identification of significant image cytometric factors in assessment of nodal involvement in breast cancer patients. In Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on. Vol. 2. IEEE. 2002. p. 1592-1595 https://doi.org/10.1109/FUZZ.2002.1006744
Seker, H. ; Odetayo, M.O. ; petrovic, Dobrila ; Naguib, R.N.G. / Soft feature evaluation indices for the identification of significant image cytometric factors in assessment of nodal involvement in breast cancer patients. Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on. Vol. 2 IEEE, 2002. pp. 1592-1595
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abstract = "This paper is focused on statistical, artificial neural networks and fuzzy logic based feature evaluation indices for determining the most/least clinically significant image cytometric prognostic factors for assessment of nodal involvement in breast cancer patients. Seven different prognostic factors, {tumour type, tumour grade, DNA ploidy, S-phase faction, G0G1/G2M ratio, minimum (start) and maximum (end) nuclear pleomorphism indices}, are assessed by means of a multilayer feedforward backpropagation neural networks based feature evaluation index as an artificial neural network approach, a fuzzy logic-based feature evaluation index derived from the fuzzy k-nearest neighbour classifier as a fuzzy logic method, and a logistic regression-based statistical analysis. The results suggest that the artificial neural network and fuzzy based indices may be more reliable than their statistical counterpart. Overall results obtained for all the three methods highlight the fact that only one method's outcome may not be adequate to reliably determine the most/least clinically important factors for assessment of nodal involvement in breast cancer patients. Our results appear to suggest that S-phase fraction and tumour type may be the most and least clinically significant markers, respectively, and should be closely investigated for the assessment of breast cancer nodal involvement",
author = "H. Seker and M.O. Odetayo and Dobrila petrovic and R.N.G. Naguib",
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N2 - This paper is focused on statistical, artificial neural networks and fuzzy logic based feature evaluation indices for determining the most/least clinically significant image cytometric prognostic factors for assessment of nodal involvement in breast cancer patients. Seven different prognostic factors, {tumour type, tumour grade, DNA ploidy, S-phase faction, G0G1/G2M ratio, minimum (start) and maximum (end) nuclear pleomorphism indices}, are assessed by means of a multilayer feedforward backpropagation neural networks based feature evaluation index as an artificial neural network approach, a fuzzy logic-based feature evaluation index derived from the fuzzy k-nearest neighbour classifier as a fuzzy logic method, and a logistic regression-based statistical analysis. The results suggest that the artificial neural network and fuzzy based indices may be more reliable than their statistical counterpart. Overall results obtained for all the three methods highlight the fact that only one method's outcome may not be adequate to reliably determine the most/least clinically important factors for assessment of nodal involvement in breast cancer patients. Our results appear to suggest that S-phase fraction and tumour type may be the most and least clinically significant markers, respectively, and should be closely investigated for the assessment of breast cancer nodal involvement

AB - This paper is focused on statistical, artificial neural networks and fuzzy logic based feature evaluation indices for determining the most/least clinically significant image cytometric prognostic factors for assessment of nodal involvement in breast cancer patients. Seven different prognostic factors, {tumour type, tumour grade, DNA ploidy, S-phase faction, G0G1/G2M ratio, minimum (start) and maximum (end) nuclear pleomorphism indices}, are assessed by means of a multilayer feedforward backpropagation neural networks based feature evaluation index as an artificial neural network approach, a fuzzy logic-based feature evaluation index derived from the fuzzy k-nearest neighbour classifier as a fuzzy logic method, and a logistic regression-based statistical analysis. The results suggest that the artificial neural network and fuzzy based indices may be more reliable than their statistical counterpart. Overall results obtained for all the three methods highlight the fact that only one method's outcome may not be adequate to reliably determine the most/least clinically important factors for assessment of nodal involvement in breast cancer patients. Our results appear to suggest that S-phase fraction and tumour type may be the most and least clinically significant markers, respectively, and should be closely investigated for the assessment of breast cancer nodal involvement

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