Skip to main content
Book cover

Advanced Methods and Applications in Computational Intelligence pp 95–122Cite as

On the Identification of Virtual Tumor Markers and Tumor Diagnosis Predictors Using Evolutionary Algorithms

  • Chapter

  • 828 Accesses

Part of the Topics in Intelligent Engineering and Informatics book series (TIEI,volume 6)

Abstract

In this chapter we present results of empirical research work done on the data based identification of estimation models for tumor markers and 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 that represent virtual tumor markers and predictors for cancer diagnoses, respectively. We have used a medical database compiled at the Central Laboratory of the General Hospital Linz, Austria, and applied several data based modeling approaches for identifying mathematical models for estimating selected tumor marker values on the basis of routinely available blood values; in detail, estimators for the tumor markers AFP, CA-125, CA15-3, CEA, CYFRA, and PSA have been identified and are discussed here. Furthermore, 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.

Keywords

  • Support Vector Machine
  • Feature Selection
  • Tumor Marker
  • Evolutionary Algorithm
  • Modeling Task

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-01436-4_6
  • Chapter length: 28 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   119.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-01436-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   159.99
Price excludes VAT (USA)
Hardcover Book
USD   199.99
Price excludes VAT (USA)
Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Affenzeller, M., Wagner, S.: 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)

    Google Scholar 

  2. Affenzeller, M., Wagner, S.: Offspring selection: A new self-adaptive selection scheme for genetic algorithms. In: Ribeiro, B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds.) Adaptive and Natural Computing Algorithms, Springer Computer Science, pp. 218–221. Springer (2005)

    Google Scholar 

  3. Affenzeller, M., Wagner, S., Winkler, S.: Goal-oriented preservation of essential genetic information by offspring selection. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), vol. 2, pp. 1595–1596. Association for Computing Machinery, ACM (2005)

    Google Scholar 

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

    Google Scholar 

  5. Alba, E., Garca-Nieto, J., Jourdan, L., Talbi, E.G.: Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms. In: IEEE Congress on Evolutionary Computation 2007, pp. 284–290 (2007)

    Google Scholar 

  6. Alberts, B.: Leukocyte functions and percentage breakdown. In: Molecular Biology of the Cell. NCBI Bookshelf (2005)

    Google Scholar 

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

    CrossRef  Google Scholar 

  8. Ariew, R.: Ockham’s Razor: A Historical and Philosophical Analysis of Ockham’s Principle of Parsimony. University of Illinois, Champaign-Urbana (1976)

    Google Scholar 

  9. Banzhaf, W., Lasarczyk, C.: Genetic programming of an algorithmic chemistry. In: O’Reilly, U., Yu, T., Riolo, R., Worzel, B. (eds.) Genetic Programming Theory and Practice II, pp. 175–190. Ann Arbor (2004)

    Google Scholar 

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

    Google Scholar 

  11. Brown, G.: A new perspective for information theoretic feature selection. In: International Conference on Artificial Intelligence and Statistics, pp. 49–56 (2009)

    Google Scholar 

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

  13. Cheng, H., Qin, Z., Feng, C., Wang, Y., Li, F.: Conditional mutual information-based feature selection analyzing for synergy and redundancy. Electronics and Telecommunications Research Institute (ETRI) Journal 33(2) (2011)

    Google Scholar 

  14. Cover, T.M., Thomas, J.A.: Elements of information theory. Wiley-Interscience, New York (1991)

    CrossRef  MATH  Google Scholar 

  15. Duch, W.: Feature Extraction: Foundations and Applications. Springer (2006)

    Google Scholar 

  16. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience (2000)

    Google Scholar 

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

    CrossRef  Google Scholar 

  18. Efroymson, M.A.: Multiple regression analysis. Mathematical Methods for Digital Computers. Wiley (1960)

    Google Scholar 

  19. Eiben, A., Smith, J.: Introduction to Evolutionary Computation. Natural Computing Series. Springer, Heidelberg (2003)

    CrossRef  Google Scholar 

  20. El Akadi, A., El Ouardighi, A., Aboutajdine, D.: A powerful feature selection approach based on mutual information. International Journal of Computer Science and Network Security 8(4), 116–121 (2008)

    Google Scholar 

  21. Fleuret, F.: Fast binary feature selection with conditional mutual information. The Journal of Machine Learning Research 5, 1531–1555 (2004), http://dl.acm.org/citation.cfm?id=1005332.1044711

    MathSciNet  MATH  Google Scholar 

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

    CrossRef  Google Scholar 

  23. Hammarstrom, S.: The carcinoembryonic antigen (cea) family: structures, suggested functions and expression in normal and malignant tissues. Seminars in Cancer Biology 9, 67–81 (1999)

    CrossRef  Google Scholar 

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

    Google Scholar 

  25. Keshaviah, A., Dellapasqua, S., Rotmensz, N., Lindtner, J., Crivellari, D., et al.: Ca15-3 and alkaline phosphatase as predictors for breast cancer recurrence: a combined analysis of seven international breast cancer study group trials. Annals of Oncology 18(4), 701–708 (2007)

    CrossRef  Google Scholar 

  26. Koepke, J.A.: Molecular marker test standardization. Cancer 69, 1578–1581 (1992)

    CrossRef  Google Scholar 

  27. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, vol. 2, pp. 1137–1143. Morgan Kaufmann (1995)

    Google Scholar 

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

    Google Scholar 

  29. Kronberger, G.K.: Symbolic regression for knowledge discovery - bloat, overfitting, and variable interaction networks. Ph.D. thesis, Institute for Formal Models and Verification, Johannes Kepler University Linz (2010)

    Google Scholar 

  30. LaFleur-Brooks, M.: Exploring Medical Language: A Student-Directed Approach, 7th edn. Mosby Elsevier, St. Louis (2008)

    Google Scholar 

  31. Lai, R.S., Chen, C.C., Lee, P.C., Lu, J.Y.: 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 (1999)

    CrossRef  Google Scholar 

  32. Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2002)

    CrossRef  MATH  Google Scholar 

  33. Ljung, L.: System Identification – Theory For the User, 2nd edn. PTR Prentice Hall, Upper Saddle River (1999)

    Google Scholar 

  34. Maton, A., Hopkins, J., McLaughlin, C.W., Johnson, S., Warner, M.Q., LaHart, D., Wright, J.D.: Human Biology and Health. Prentice Hall, Englewood Cliffs (1993)

    Google Scholar 

  35. Meyer, P., Bontempi, G.: On the use of variable complementarity for feature selection in cancer classification. In: Evolutionary Computation and Machine Learning in Bioinformatics, pp. 91–102 (2006)

    Google Scholar 

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

    Google Scholar 

  37. Nelles, O.: Nonlinear System Identification. Springer, Heidelberg (2001)

    CrossRef  MATH  Google Scholar 

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

    CrossRef  Google Scholar 

  39. Osman, N., O’Leary, N., Mulcahy, E., Barrett, N., Wallis, F., Hickey, K., Gupta, R.: Correlation of serum ca125 with stage, grade and survival of patients with epithelial ovarian cancer at a single centre. Irish Medical Journal 101(8), 245–247 (2008)

    Google Scholar 

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

    Google Scholar 

  41. Rosen, D.G., Wang, L., Atkinson, J.N., Yu, Y., Lu, K.H., Diamandis, E.P., Hellstrom, I., Mok, S.C., Liu, J., Bast, R.C.: Potential markers that complement expression of ca125 in epithelial ovarian cancer. Gynecologic Oncology 99(2), 267–277 (2005)

    CrossRef  Google Scholar 

  42. Shannon, C.E.: A mathematical theory of communication. The Bell Systems Technical Journal 27, 379–423 (1948)

    MathSciNet  MATH  Google Scholar 

  43. Tallitsch, R.B., Martini, F., Timmons, M.J.: Human anatomy, 5th edn. Pearson/Benjamin Cummings, San Francisco (2006)

    Google Scholar 

  44. Tesmer, M., Estevez, P.A.: Amifs: Adaptive feature selection by using mutual information. In: IEEE International Joint Conference on Neural Networks, vol. 1 (2004)

    Google Scholar 

  45. Thompson, I.M., Pauler, D.K., Goodman, P.J., Tangen, C.M., et al.: Prevalence of prostate cancer among men with a prostate-specific antigen level < = 4.0 ng per milliliter. New England Journal of Medicine 350(22), 2239–2246 (2004)

    CrossRef  Google Scholar 

  46. Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  47. Wagner, S.: Heuristic optimization software systems – modeling of heuristic optimization algorithms in the heuristiclab software environment. Ph.D. thesis, Johannes Kepler University Linz (2009)

    Google Scholar 

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

    Google Scholar 

  49. Williams, P.W., Gray, H.D.: Gray’s anatomy, 37th edn. C. Livingstone, New York (1989)

    Google Scholar 

  50. Winkler, S.: Evolutionary system identification - modern concepts and practical applications. Ph.D. thesis, Institute for Formal Models and Verification, Johannes Kepler University Linz (2008)

    Google Scholar 

  51. Winkler, S., Affenzeller, M., Jacak, W., Stekel, H.: 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)

    Google Scholar 

  52. Winkler, S., Affenzeller, M., Jacak, W., Stekel, H.: Identification of cancer diagnosis estimation models using evolutionary algorithms - a case study for breast cancer, melanoma, and cancer in the respiratory system. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010 (2011)

    Google Scholar 

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

    Google Scholar 

  54. Winkler, S., Affenzeller, M., Kronberger, G., Kommenda, M., Wagner, S., Jacak, W., Stekel, H.: On the use of estimated tumor marker classifications in tumor diagnosis prediction - a case study for breast cancer. In: Proceedings of 23rd IEEE European Modeling & Simulation Symposium, EMSS 2011 (2011)

    Google Scholar 

  55. Yin, B.W., Dnistrian, A., Lloyd, K.O.: Ovarian cancer antigen CA125 is encoded by the MUC16 mucin gene. International Journal of Cancer 98(5), 737–740 (2002)

    CrossRef  Google Scholar 

  56. Yonemori, K., Ando, M., Taro, T.S., Katsumata, N., Matsumoto, K., Yamanaka, Y., Kouno, T., Shimizu, C., Fujiwara, Y.: 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)

    CrossRef  Google Scholar 

  57. Zhong, L., Zhou, X., Wei, K., Yang, X., Ma, C., Zhang, C., Zhang, Z.: 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)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, School of Informatics, Communication and Media, Softwarepark 11, 4232, Hagenberg, Austria

    Stephan M. Winkler, Michael Affenzeller, Gabriel K. Kronberger, Michael Kommenda, Stefan Wagner & Witold Jacak

  2. Central Laboratory, General Hospital Linz, Krankenhausstraße 9, 4021, Linz, Austria

    Herbert Stekel

Authors
  1. Stephan M. Winkler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Affenzeller
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gabriel K. Kronberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael Kommenda
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stefan Wagner
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Witold Jacak
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Herbert Stekel
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Wroclaw University of Technology, Wybrzeze Wyspianskiego Street 27, Wroclaw, 50-370, Poland

    Ryszard Klempous

  2. Wroclaw University of Technology, Wybrzeze Wyspianskiego Street 27, Wroclaw, 50-370, Poland

    Jan Nikodem

  3. of Applied Sciences, Upper Austria University, Softwarepark 11, Hagenberg, 4232, Austria

    Witold Jacak

  4. , Bld 1 Level 22, University of Technology, Broadway, Ultimo/Syd, Sydney, 2007, New South Wales, Australia

    Zenon Chaczko

Rights and permissions

Reprints and Permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Winkler, S.M. et al. (2014). On the Identification of Virtual Tumor Markers and Tumor Diagnosis Predictors Using Evolutionary Algorithms. In: Klempous, R., Nikodem, J., Jacak, W., Chaczko, Z. (eds) Advanced Methods and Applications in Computational Intelligence. Topics in Intelligent Engineering and Informatics, vol 6. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01436-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-01436-4_6

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-01435-7

  • Online ISBN: 978-3-319-01436-4

  • eBook Packages: EngineeringEngineering (R0)