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Identification of cancer diagnosis estimation models using evolutionary algorithms: a case study for breast cancer, melanoma, and cancer in the respiratory system

Authors:

Stephan M. Winkler,

Michael Affenzeller,

Herbert StekelAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

Pages 503 - 510

https://doi.org/10.1145/2001858.2002040

Published: 12 July 2011 Publication History

Abstract

In this paper we present results of empirical research work done on the data based identification of estimation models for cancer diagnoses: Based on patients' data records including standard blood parameters, tumor markers, and information about the diagnosis of tumors we have trained mathematical models for estimating cancer diagnoses.

Several data based modeling approaches implemented in HeuristicLab have been applied for identifying estimators for selected cancer diagnoses: Linear regression, k-nearest neighbor learning, artificial neural networks, and support vector machines (all optimized using evolutionary algorithms) as well as genetic programming. The investigated diagnoses of breast cancer, melanoma, and respiratory system cancer can be estimated correctly in up to 81%, 74%, and 91% of the analyzed test cases, respectively; without tumor markers up to 75%, 74%, and 87% of the test samples are correctly estimated, respectively.

References

[1]

M. Affenzeller and S. Wagner. SASEGASA: A new generic parallel evolutionary algorithm for achieving highest quality results. Journal of Heuristics - Special Issue on New Advances on Parallel Meta-Heuristics for Complex Problems, 10:239--263, 2004.

Digital Library

[2]

M. Affenzeller, S. Winkler, S. Wagner, and A. Beham. Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. Chapman & Hall / CRC, 2009.

Digital Library

[3]

E. Alba, J. G.-N. L. Jourdan, and E.-G. Talbi. Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms. IEEE Congress on Evolutionary Computation 2007, pages 284--290, 2007.

[4]

G. L. Andriole, E. D. Crawford, R. L. Grubband, S. S. Buys, D. Chia, T. R. Church, et al. Mortality results from a randomized prostate-cancer screening trial. New England Journal of Medicine, 360(13):1310--1319, 2009.

[5]

W. Banzhaf and C. Lasarczyk. Genetic programming of an algorithmic chemistry. In U. O'Reilly, T. Yu, R. Riolo, and B. Worzel, editors, Genetic Programming Theory and Practice II, pages 175--190. Ann Arbor, 2004.

[6]

N. Bitterlich and J. Schneider. Cut-off-independent tumour marker evaluation using ROC approximation. Anticancer Research, 27:4305--4310, 2007.

[7]

C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

[8]

N. Clegg, C. Ferguson, L. True, H. Arnold, A. Moorman, J. Quinn, R. Vessella, and P. Nelson. Molecular characterization of prostatic small-cell neuroendocrine carcinoma. Prostate, 55(1):55--64, 2003.

[9]

G. Crombach, H. Würz, F. Herrmann, R. Kreienberg, V. Möbus, P. Schmidt-Rhode, G. Sturm, H. Caffier, and H. Kaesemann. The importance of the scc antigen in the diagnosis and follow-up of cervix carcinoma. Deutsche Medizinische Wochenschrift, 114(18):700--705, 1989.

[10]

R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. Wiley Interscience, 2nd edition, 2000.

Digital Library

[11]

M. J. Duffy and J. Crown. A personalized approach to cancer treatment: how biomarkers can help. Clinical Chemistry, 54(11):1770--1779, 2008.

[12]

A. Eiben and J. Smith. Introduction to Evolutionary Computation. Natural Computing Series. Springer-Verlag Berlin Heidelberg, 2003.

Digital Library

[13]

B. Frey, R. Morant, H. Senn, and W. Riesen. Clinical assessment of the new tumor marker tps. International Journal for Cancer Research and Treatment, 17:270--276, 1994.

[14]

P. Gold and S. O. Freedman. Demonstration of tumor-specific antigens in human colonic carcinomata by immunological tolerance and absorption techniques. The Journal of Experimental Medicine, 121:439--462, 1965.

[15]

J. H. Holland. Adaption in Natural and Artifical Systems. University of Michigan Press, 1975.

[16]

J. A. Koepke. Molecular marker test standardization. Cancer, 69:1578--1581, 1992.

[17]

R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th international joint conference on Artificial intelligence, volume 2, pages 1137--1143. Morgan Kaufmann, 1995.

Digital Library

[18]

H. Koprowski, M. Herlyn, Z. Steplewski, and H. Sears. Specific antigen in serum of patients with colon carcinoma. Science, 212(4490):53--55, 1981.

[19]

J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, 1992.

Digital Library

[20]

M. LaFleur-Brooks. Exploring Medical Language: A Student-Directed Approach. St. Louis, Missouri, USA: Mosby Elsevier, 7th edition, 2008.

[21]

R. S. Lai, C. C. Chen, P. C. Lee, and J. Y. Lu. Evaluation of cytokeratin 19 fragment (cyfra 21-1) as a tumor marker in malignant pleural effusion. Japanese Journal of Clinical Oncology, 29(9):421--424, 199.

[22]

L. Ljung. System Identification - Theory For the User, 2nd edition. PTR Prentice Hall, Upper Saddle River, N.J., 1999.

Digital Library

[23]

G. J. Mizejewski. Alpha-fetoprotein structure and function: relevance to isoforms, epitopes, and conformational variants. Experimental biology and medicine, 226(5):377--408, 2001.

[24]

O. Nelles. Nonlinear System Identification. Springer Verlag, Berlin Heidelberg New York, 2001.

[25]

Y. Niv. Muc1 and colorectal cancer pathophysiology considerations. World Journal of Gastroenterology, 14(14):2139--2141, 2008.

[26]

D. Nonaka, L. Chiriboga, and B. Rubin. Differential expression of s100 protein subtypes in malignant melanoma, and benign and malignant peripheral nerve sheath tumors. Journal of Cutaneous Pathology, 35(11):1014--1019, 2008.

[27]

A. J. Rai, Z. Zhang, J. Rosenzweig, I. ming Shih, T. Pham, E. T. Fung, L. J. Sokoll, and D. W. Chan. Proteomic approaches to tumor marker discovery. Archives of Pathology & Laboratory Medicine, 126(12):1518--1526, 2002.

[28]

V. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.

[29]

S. Wagner. Heuristic Optimization Software Systems - Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment. PhD thesis, Johannes Kepler University Linz, 2009.

[30]

S. Wagner and M. Affenzeller. SexualGA: Gender-specific selection for genetic algorithms. In N. Callaos, W. Lesso, and E. Hansen, editors, Proceedings of the 9th World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI) 2005, volume 4, pages 76--81. International Institute of Informatics and Systemics, 2005.

[31]

P. W. Williams and H. D. Gray. Gray's anatomy. New York: C. Livingstone, 37th edition, 1989.

[32]

S. Winkler. Evolutionary System Identification - Modern Concepts and Practical Applications. PhD thesis, Institute for Formal Models and Verification, Johannes Kepler University Linz, 2008.

[33]

S. Winkler, M. Affenzeller, W. Jacak, and H. Stekel. Classification of tumor marker values using heuristic data mining methods. In Proceedings of the GECCO 2010 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC 2010), 2010.

Digital Library

[34]

S. Winkler, M. Affenzeller, G. Kronberger, M. Kommenda, S. Wagner, W. Jacak, and H. Stekel. Feature selection in the analysis of tumor marker data using evolutionary algorithms. In Proceedings of the 7th International Mediterranean and Latin American Modelling Multiconference, pages 1--6, 2010.

[35]

B. W. Yin, A. Dnistrian, and K. O. Lloyd. Ovarian cancer antigen CA125 is encoded by the MUC16 mucin gene. International Journal of Cancer, 98(5):737--40, 2002.

[36]

K. Yonemori, M. Ando, T. S. Taro, N. Katsumata, K. Matsumoto, Y. Yamanaka, T. Kouno, C. Shimizu, and Y. Fujiwara. Tumor-marker analysis and verification of prognostic models in patients with cancer of unknown primary, receiving platinum-based combination chemotherapy. Journal of Cancer Research and Clinical Oncology, 132(10):635--642, 2006.

[37]

L. Zhong, X. Zhou, K. Wei, X. Yang, C. Ma, C. Zhang, and Z. Zhang. Application of serum tumor markers and support vector machine in the diagnosis of oral squamous cell carcinoma. Shanghai Kou Qiang Yi Xue (Shanghai Journal of Stomatology), 17(5):457--460, 2008.

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Identification of cancer diagnosis estimation models using evolutionary algorithms: a case study for breast cancer, melanoma, and cancer in the respiratory system

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

July 2011

1548 pages

ISBN:9781450306904

DOI:10.1145/2001858

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 July 2011

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GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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