Skip to main content
Search
Search
Quick Search anywhere
Quick Search anywhere
Quick Search anywhere
Quick Search anywhere
Quick Search anywhere
Quick Search anywhere
Advanced Search
Skip main navigation
No Access

On the use of estimated tumour marker classifications in tumour diagnosis prediction – a case study for breast cancer

Published Online:September 11, 2013pp 29-41https://doi.org/10.1504/IJSPM.2013.055192

Abstract

In this article, we describe the use of tumour marker estimation models in the prediction of tumour diagnoses. In previous works, we have identified classification models for tumour markers that can be used for estimating tumour marker values on the basis of standard blood parameters. These virtual tumour markers are now used in combination with standard blood parameters for learning classifiers that are used for predicting tumour diagnoses. Several data-based modelling approaches implemented in HeuristicLab have been applied for identifying estimators for selected tumour markers and cancer diagnoses: Linear regression, k-nearest neighbour (k-NN) learning, artificial neural networks (ANNs) and support vector machines (SVMs) (all optimised using evolutionary algorithms), as well as genetic programming (GP). We have applied these modelling approaches for identifying models for breast cancer diagnoses; in the results section, we summarise classification accuracies for breast cancer and we compare classification results achieved by models that use measured marker values as well as models that use virtual tumour markers.

Keywords

EAs, evolutionary algorithms, medical data analysis, tumour marker modelling, data mining

References

  • 1. M. Affenzeller, S. Winkler, S. Wagner, A. Beham, ‘Genetic Algorithms and Genetic Programming – Modern Concepts and Practical Applications’, Chapman & Hall / CRC , ISBN 978–1584886297. (: 2009)A. Beham‘Genetic Algorithms and Genetic Programming – Modern Concepts and Practical Applications’, Chapman & Hall / CRC , ISBN 978–1584886297.2009Google Scholar
  • 2. E. Alba, J. García-Nieto, L. Jourdan, E-G. Talbi, ‘Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms’, IEEE Congress on Evolutionary Computation 2007 , pp.284–290. (: 2005)E-G. Talbi‘Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms’, IEEE Congress on Evolutionary Computation 2007 , pp.284–290.2005Google Scholar
  • 3. R. Ariew, Ockham’s Razor: A Historical and Philosophical Analysis of Ockham’s Principle of Parsimony (Champaign-Urbana: University of Illinois 1976)R. AriewOckham’s Razor: A Historical and Philosophical Analysis of Ockham’s Principle of Parsimony1976Google Scholar
  • 4. G. Brown, 'A new perspective for information theoretic feature selection' International Conference on Artificial Intelligence and Statistics 5 (2009): 49-56G. BrownA new perspective for information theoretic feature selectionInternational Conference on Artificial Intelligence and Statistics200954956Google Scholar
  • 5. C-C. Chang, C-J. Lin, ‘LIBSVM: a library for support vector machines’, Software available at (: 2001)C-J. Lin‘LIBSVM: a library for support vector machines’, Software available at2001Google Scholar
  • 6. H. Cheng, Z. Qin, C. Feng, Y. Wang, F. Li, 'Conditional mutual information-based feature selection analyzing for synergy and redundancy' Electronics and Telecommunications Research Institute (ETRI) Journal 33 2 (2011): 210-218F. LiConditional mutual information-based feature selection analyzing for synergy and redundancyElectronics and Telecommunications Research Institute (ETRI) Journal201133210218Google Scholar
  • 7. T.M. Cover, J.A. Thomas, Elements of Information Theory (New York: Wiley-Interscience 1991)J.A. ThomasElements of Information Theory1991Google Scholar
  • 8. S. Droste, ‘Genetic programming with guaranteed quality’, Genetic Programming 1998: Proceedings of the Third Annual Conference , Morgan Kaufmann, pp.54–59. (: 1998)S. Droste‘Genetic programming with guaranteed quality’, Genetic Programming 1998: Proceedings of the Third Annual Conference , Morgan Kaufmann, pp.54–59.1998Google Scholar
  • 9. R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification 2nd Ed. (New York, Chichester, Weinheim, Brisbane, Singapore, Toronto: John Wiley & Sons, Inc. 2000)D.G. StorkPattern Classification 2nd Ed.2000Google Scholar
  • 10. M.A. Efroymson, ‘Multiple regression analysis’, In Ralston, A. and Wilf, H.S. (Eds.), Mathematical Methods for Digital Computers (New York: Wiley 1960): 191-203M.A. Efroymson‘Multiple regression analysis’, In Ralston, A. and Wilf, H.S. (Eds.), Mathematical Methods for Digital Computers1960191203Google Scholar
  • 11. A.E. Eiben, J.E. Smith, ‘Introduction to evolutionary computation’, Natural Computing Series (Berlin Heidelberg: Springer-Verlag 2003)J.E. Smith‘Introduction to evolutionary computation’, Natural Computing Series2003Google Scholar
  • 12. A. El Akadi, A. El Ouardighi, D. Aboutajdine, ‘A powerful feature selection approach based on mutual information’, International Journal of Computer Science and Network Security , Vol. 8, No. 4, p.116. (: 2008)D. Aboutajdine‘A powerful feature selection approach based on mutual information’, International Journal of Computer Science and Network Security , Vol. 8, No. 4, p.116.2008Google Scholar
  • 13. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, 'From data mining to knowledge discovery in databases' Al Magazine 17 3 (1996): 37-54P. SmythFrom data mining to knowledge discovery in databasesAl Magazine1996173754Google Scholar
  • 14. F. Fleuret, 'Fast binary feature selection with conditional mutual information' Journal of Machine Learning Research 5 (2004): 1531-1555F. FleuretFast binary feature selection with conditional mutual informationJournal of Machine Learning Research2004515311555Google Scholar
  • 15. P. Gold, S.O. Freedman, 'Demonstration of tumor-specific antigens in human colonic carcinomata by immunological tolerance and absorption techniques' The Journal of Experimental Medicine 121 (1965): 439-462S.O. FreedmanDemonstration of tumor-specific antigens in human colonic carcinomata by immunological tolerance and absorption techniquesThe Journal of Experimental Medicine1965121439462Google Scholar
  • 16. I. Guyon, S. Gunn, M. Nikravesh, L.A. Zadeh, ‘Feature Extraction: Foundations and Applications’, Studies in Fuzziness & Soft Computing , Springer, ISBN 3–540-35487–5. (: 2006)L.A. Zadeh‘Feature Extraction: Foundations and Applications’, Studies in Fuzziness & Soft Computing , Springer, ISBN 3–540-35487–5.2006Google Scholar
  • 17. S. Hammarstrom, 'The carcinoembryonic antigen (CEA) family: structures, suggested functions and expression in normal and malignant tissues' Seminars in Cancer Biology 9 (1999): 67-81S. HammarstromThe carcinoembryonic antigen (CEA) family: structures, suggested functions and expression in normal and malignant tissuesSeminars in Cancer Biology199996781Google Scholar
  • 18. J.H. Holland, Adaption in Natural and Artificial Systems (1975)J.H. HollandAdaption in Natural and Artificial Systems1975Google Scholar
  • 19. A. Keshaviah, S. Dellapasqua, N. Rotmensz, J. Lindtner, D. Crivellari, '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 (2007): 701-708D. CrivellariCA15–3 and alkaline phosphatase as predictors for breast cancer recurrence: a combined analysis of seven International Breast Cancer Study Group trialsAnnals of Oncology200718701708Google Scholar
  • 20. J.A. Koepke, 'Molecular marker test standardization' Cancer 69 (1992): 1578-1581J.A. KoepkeMolecular marker test standardizationCancer19926915781581Google Scholar
  • 21. J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection (Cambridge, MA, USA: The MIT Press 1992)J. KozaGenetic Programming: On the Programming of Computers by Means of Natural Selection1992Google Scholar
  • 22. G. Kronberger, Symbolic Regression for Knowledge Discovery – Bloat, Overfitting and Variable Interaction Networks PhD Thesis, Johannes Kepler University Linz, Austria (: 2010)G. KronbergerSymbolic Regression for Knowledge Discovery – Bloat, Overfitting and Variable Interaction Networks PhD Thesis, Johannes Kepler University Linz, Austria2010Google Scholar
  • 23. W.B. Langdon, R. Poli, Foundations of Genetic Programming (Berlin Heidelberg New York: Springer Verlag 2002)R. PoliFoundations of Genetic Programming2002Google Scholar
  • 24. L. Ljung, System Identification – Theory For The User 2nd edition (Upper Saddle River, NJ, USA: PTR Prentice Hall 1999)L. LjungSystem Identification – Theory For The User 2nd edition1999Google Scholar
  • 25. P. Meyer, G. Bontempi, ‘On the use of variable complementarity for feature selection in cancer classification’, in Applications of Evolutionary Computing , Lecture Notes in Computer Science, Springer Berlin Heidelberg, Vol. 3907, pp. 91–102. (: 2006)G. Bontempi‘On the use of variable complementarity for feature selection in cancer classification’, in Applications of Evolutionary Computing , Lecture Notes in Computer Science, Springer Berlin Heidelberg, Vol. 3907, pp. 91–102.2006Google Scholar
  • 26. T. Mitchell, Machine Learning (Boston, Burr Ridge, Dubuque, Madison, New York, San Francisco, St. Louis: McGraw Hill 1997)T. MitchellMachine Learning1997Google Scholar
  • 27. O. Nelles, Nonlinear System Identification (Berlin Heidelberg New York: Springer Verlag 2001)O. NellesNonlinear System Identification2001Google Scholar
  • 28. Y. Niv, 'MUC1 and colorectal cancer pathophysiology considerations' World Journal of Gastroenterology 14 (2008): 2139-2141Y. NivMUC1 and colorectal cancer pathophysiology considerationsWorld Journal of Gastroenterology20081421392141Google Scholar
  • 29. N. Osman, N. O’Leary, E. Mulcahy, N. Barrett, F. Wallis, K. Hickey, R. Gupta, 'Correlation of serum CA125 with stage, grade and survival of patients with epithelial ovarian cancer at a single centre' Irish Medical Journal 101 (2008): 245-247R. GuptaCorrelation of serum CA125 with stage, grade and survival of patients with epithelial ovarian cancer at a single centreIrish Medical Journal2008101245247Google Scholar
  • 30. I. Rechenberg, ‘Evolutions strategie’, Friedrich Frommann Verlag , Stuttgart. (: 1973)I. Rechenberg‘Evolutions strategie’, Friedrich Frommann Verlag , Stuttgart.1973Google Scholar
  • 31. D.G. Rosen, L. Wang, J.N. Atkinson, Y. Yu, K.H. Lu, E.P. Diamandis, I. Hellstrom, S.C. Mok, J. Liu, R.C. Bast, 'Potential markers that complement expression of CA125 in epithelial ovarian cancer' Gynecologic Oncology 99 2 (2005): 267-277R.C. BastPotential markers that complement expression of CA125 in epithelial ovarian cancerGynecologic Oncology200599267277Google Scholar
  • 32. H-P. Schwefel, Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie (Basel: Birkhäuser Verlag 1994)H-P. SchwefelNumerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie1994Google Scholar
  • 33. C.E. Shannon, 'A mathematical theory of communication' Bell Systems Technical Journal 27 3 (1948): 379-423C.E. ShannonA mathematical theory of communicationBell Systems Technical Journal194827379423Google Scholar
  • 34. M. Tesmer, P.A. Estévez, ‘AMIFS: adaptive feature selection by using mutual information’, IEEE International Joint Conference on Neural Networks , Budapest, Hungary, 26–29 July, Vol. 1, pp.303–308. (: 2004)P.A. Estévez‘AMIFS: adaptive feature selection by using mutual information’, IEEE International Joint Conference on Neural Networks , Budapest, Hungary, 26–29 July, Vol. 1, pp.303–308.2004Google Scholar
  • 35. V. Vapnik, Statistical Learning Theory (New York, Chichester, Weinheim, Brisbane, Singapore, Toronto: John Wiley & Sons Inc. 1998)V. VapnikStatistical Learning Theory1998Google Scholar
  • 36. S. Wagner, Heuristic Optimization Software Systems – Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment, PhD Thesis, Institute for Formal Models and Verification, Johannes Kepler University Linz, Austria (: 2009)S. WagnerHeuristic Optimization Software Systems – Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment, PhD Thesis, Institute for Formal Models and Verification, Johannes Kepler University Linz, Austria2009Google Scholar
  • 37. S. Wagner, M. Affenzeller, ‘SexualGA: Gender-Specific Selection for Genetic Algorithms’, Proceedings of the 9th World Multi-Conference on Systemics, Cybernetics and Informatics 2005 , pp.76–81. (: 2005)M. Affenzeller‘SexualGA: Gender-Specific Selection for Genetic Algorithms’, Proceedings of the 9th World Multi-Conference on Systemics, Cybernetics and Informatics 2005 , pp.76–81.2005Google Scholar
  • 38. S. Winkler, ‘Evolutionary system identification – modern concepts and practical applications’, Schriften der Johannes Kepler Universität Linz, Reihe C: Technik and Naturwissenschaften, Universitätsverlag Rudolf Trauner , ISBN 978–3-85499–569-2. (: 2009)S. Winkler‘Evolutionary system identification – modern concepts and practical applications’, Schriften der Johannes Kepler Universität Linz, Reihe C: Technik and Naturwissenschaften, Universitätsverlag Rudolf Trauner , ISBN 978–3-85499–569-2.2009Google Scholar
  • 39. S. Winkler, M. Affenzeller, W. Jacak, H. Stekel, ‘Classification of tumor marker values using heuristic data mining methods’, Proceedings of Genetic and Evolutionary Computation Conference 2010, Workshop on Medical Applications of Genetic and Evolutionary Computation , pp.1915–1922. (: 2010)H. Stekel‘Classification of tumor marker values using heuristic data mining methods’, Proceedings of Genetic and Evolutionary Computation Conference 2010, Workshop on Medical Applications of Genetic and Evolutionary Computation , pp.1915–1922.2010Google Scholar
  • 40. S. Winkler, M. Affenzeller, W. Jacak, H. Stekel, ‘Identification of cancer diagnosis estimation models using evolutionary algorithms – a case study for breast cancer, melanoma and cancer in the respiratory system’, 13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, Companion Material Proceedings , Dublin, Ireland, 12–16 July, ACM 2011, ISBN 978–1-4503–0690-4, pp.503–510. (: 2011)H. Stekel‘Identification of cancer diagnosis estimation models using evolutionary algorithms – a case study for breast cancer, melanoma and cancer in the respiratory system’, 13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, Companion Material Proceedings , Dublin, Ireland, 12–16 July, ACM 2011, ISBN 978–1-4503–0690-4, pp.503–510.2011Google Scholar
  • 41. B.W. Yin, A. Dnistrian, K.O. Lloyd, 'Ovarian cancer antigen CA125 is encoded by the MUC16 mucin gene' International Journal of Cancer (: 2002): 737-740K.O. LloydOvarian cancer antigen CA125 is encoded by the MUC16 mucin geneInternational Journal of Cancer200298737740Google Scholar
  • 42. K. Yonemori, m. Ando, T.S. Taro, N. Katsumata, K. Matsumoto, Y. Yamanaka, T. Kouno, C. Shimizu, 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 (2006): 635-642Y. FujiwaraTumor-marker analysis and verification of prognostic models in patients with cancer of unknown primary, receiving platinum-based combination chemotherapyJournal of Cancer Research and Clinical Oncology2006132635642Google Scholar
  • 43. HeuristicLab website: (: )HeuristicLab website:0Google Scholar
  • 44. HEAL research group website: (: )HEAL research group website:0Google Scholar
  • 45. Heureka! website: (: )Heureka! website:0Google Scholar
Previous article
Next article