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BioMed Research International
Volume 2013 (2013), Article ID 176519, 11 pages
Research Article

Brain Tumor Classification Using AFM in Combination with Data Mining Techniques

1School of Applied Health and Social Sciences, University of Applied Sciences Upper Austria, Garnisonstraße 21, 4020 Linz, Austria
2Department of Pathology, Nerve Clinic Linz Wagner Jauregg, Wagner-Jauregg-Weg 15, 4020 Linz, Austria
3University of Applied Sciences Upper Austria, Research & Development Wels, Stelzhamerstraße 23, 4600 Wels, Austria

Received 30 April 2013; Accepted 18 July 2013

Academic Editor: Kaisorn L. Chaichana

Copyright © 2013 Marlene Huml et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. N. Louis, H. Ohgaki, O. D. Wiestler et al., Eds., The 2007 WHO Classification of Tumors of the Central Nervous System, IARC Press, Lyon, France, 2007.
  2. J. Felsberg and G. Reifenberger, “Neuropathology and molecular genetics of diffusely infiltrating cerebral gliomas,” Medical Laser Application, vol. 17, no. 2, pp. 133–146, 2002. View at Scopus
  3. M. J. Riemenschneider and G. Reifenberger, “Astrocytic tumors,” Recent Results in Cancer Research, vol. 171, pp. 3–24, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. K. K. Herfarth, S. Gutwein, and J. Debus, “Postoperative radiotherapy of astrocytomas,” Seminars in Surgical Oncology, vol. 20, no. 1, pp. 13–23, 2001. View at Publisher · View at Google Scholar · View at Scopus
  5. L. M. DeAngelis, “Brain tumors,” The New England Journal of Medicine, vol. 344, no. 2, pp. 114–123, 2001. View at Publisher · View at Google Scholar · View at Scopus
  6. W. Coons, P. Johnson, B. Scheithauer, A. Yates, and D. Pearl, “Improving diagnostic accuracy and interobserver concordance in the classification and grading of Primary Gliomas,” Cancer, vol. 79, pp. 1381–1393, 1997.
  7. A. Bonfiglio, M. T. Parodi, and G. P. Tonini, “Subcellular details of early events of differentiation induced by retinoic acid in human neuroblastoma cells detected by atomic force microscope,” Experimental Cell Research, vol. 216, no. 1, pp. 73–79, 1995. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Nagayama, M. Morimoto, K. Kawanata et al., “AFM observation of three-dimensional fine structural changes in living neurons,” Bioimages, vol. 4, pp. 111–116, 1996.
  9. S. Nagayama, T. Tojima, M. Morimoto et al., “Practical scan speed in atomic force microscopy for live neurons in a physiological solution,” Japanese Journal of Applied Physics, vol. 36, no. 6, pp. 3877–3880, 1997. View at Scopus
  10. V. Parpura, P. G. Haydon, and E. Henderson, “Three-dimensional imaging of living neurons and glia with the automic force microscope,” Journal of Cell Science, vol. 104, no. 2, pp. 427–432, 1993. View at Scopus
  11. E. Henderson, P. G. Haydon, and D. S. Sakaguchi, “Actin filament dynamics in living glial cells imaged by atomic force microscopy,” Science, vol. 257, no. 5078, pp. 1944–1946, 1992. View at Scopus
  12. E. Henderson and D. S. Sakaguchi, “Imaging F-actin in fixed glial cells with a combined optical fluorescence/atomic force microscope,” NeuroImage, vol. 1, no. 2, pp. 145–150, 1993. View at Scopus
  13. L. Teodori, M. C. Albertini, F. Uguccioni et al., “Static magnetic fields affect cell size, shape, orientation, and membrane surface of human glioblastoma cells, as demonstrated by electron, optic, and atomic force microscopy,” Cytometry A, vol. 69, no. 2, pp. 75–85, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. E. Bystrenova, M. Jelitai, I. Tonazzini et al., “Neural networks grown on organic semiconductors,” Advanced Functional Materials, vol. 18, no. 12, pp. 1751–1756, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Yamane, H. Shiga, H. Asou et al., “Dynamics of astrocyte adhesion as analyzed by a combination of atomic force microscopy and immunocytochemistry: the involvement of actin filaments and connexin 43 in the early stage of adhesion,” Archives of Histology and Cytology, vol. 62, no. 4, pp. 355–361, 1999. View at Scopus
  16. H. L. Fillmore, I. Chasiotis, S. W. Cho, and G. T. Gillies, “Atomic force microscopy observations of tumour cell invadopodia: novel cellular nanomorphologies on collagen substrates,” Nanotechnology, vol. 14, no. 1, pp. 73–76, 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Melling, S. Hochmeister, R. Blumer et al., “Atomic force microscopy imaging of the human trigeminal ganglion,” NeuroImage, vol. 14, no. 6, pp. 1348–1352, 2001. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Melling, D. Karimian-Teherani, M. Behnam, and S. Mostler, “Morphological study of the healthy human oculomotor nerve by atomic force microscopy,” NeuroImage, vol. 20, no. 2, pp. 795–801, 2003. View at Publisher · View at Google Scholar · View at Scopus
  19. K. Schilcher, R. Silye, and H. P. Huber, “Neuropil fiber reduction in brain tumors observed by atomic force microscopy,” Journal of Advanced Microscopy Research, vol. 5, pp. 86–90, 2010.
  20. A. Hutter, K. E. Schwetye, A. J. Bierhals, and R. C. McKinstry, “Brain neoplasms: epidemiology, diagnosis, and prospects for cost-effective imaging,” Neuroimaging Clinics of North America, vol. 13, no. 2, pp. 237–250, 2003. View at Scopus
  21. E. I. Papageorgiou, P. P. Spyridonos, D. T. Glotsos et al., “Brain tumor characterization using the soft computing technique of fuzzy cognitive maps,” Applied Soft Computing Journal, vol. 8, no. 1, pp. 820–828, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. M. Law, S. Yang, H. Wang et al., “Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging,” American Journal of Neuroradiology, vol. 24, no. 10, pp. 1989–1998, 2003. View at Scopus
  23. I. S. Khayal, T. R. McKnight, C. McGue et al., “Apparent diffusion coefficient and fractional anisotropy of newly diagnosed grade II gliomas,” NMR in Biomedicine, vol. 22, no. 4, pp. 449–455, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. S. Cuellar-Baena, L. M. T. S. Morais, F. Cendes, A. V. Faria, and G. Castellano, “Manual and semi-automatic quantification of in vivo 1H-MRS data for the classification of human primary brain tumors,” Brazilian Journal of Medical and Biological Research, vol. 44, no. 4, pp. 345–353, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. D. J. Hemanth, C. K. Selva Vijila, and J. Anitha, “Application of neuro-fuzzy model for mr brain tumor image classification,” Biomedical Soft Computing and Human Sciences, vol. 16, no. 1, pp. 95–102, 1995.
  26. M. J. Mckeown and D. A. Ramsay, “Classification of astrocytomas and malignant astrocytomas by principal components analysis and a neural net,” Journal of Neuropathology and Experimental Neurology, vol. 55, no. 12, pp. 1238–1245, 1996. View at Scopus
  27. C. Decaestecker, I. Salmon, O. Dewitte et al., “Nearest-neighbor classification for identification of aggressive versus nonaggressive low-grade astrocytic tumors by means of image cytometry- generated variables,” Journal of Neurosurgery, vol. 86, no. 3, pp. 532–537, 1997. View at Scopus
  28. P. K. Sallinen, S.-L. Sallinen, P. T. Helén et al., “Grading of diffusely infiltrating astrocytomas by quantitative histopathology, cell proliferation and image cytometric DNA analysis,” Neuropathology and Applied Neurobiology, vol. 26, no. 4, pp. 319–331, 2000. View at Publisher · View at Google Scholar · View at Scopus
  29. R. Nafe and W. Schlote, “Topometric analysis of diffuse astrocytomas,” Analytical and Quantitative Cytology and Histology, vol. 25, no. 1, pp. 12–18, 2003. View at Scopus
  30. R. Nafe and W. Schlote, “Densitometric analysis of tumor cell nuclei in low-grade and high-grade astrocytomas,” Electronic Journal of Pathology and Histology, vol. 8, no. 2-3, pp. 23021–230218, 2002. View at Scopus
  31. D. Glotsos, P. Spyridonos, P. Petalas et al., “Computer based malignancy grading of astrocytomas employing a support vector machine classifier, the WHO grading system and the regular hematoxylin-eosin diagnostic staining procedure,” Analytical and Quantitative Cytology and Histology, vol. 26, no. 2, pp. 77–83, 2004. View at Scopus
  32. D. Glotsos, J. Tohka, P. Ravazoula, D. Cavouras, and G. Nikiforidis, “Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines,” International Journal of Neural Systems, vol. 15, no. 1-2, pp. 1–11, 2005. View at Publisher · View at Google Scholar · View at Scopus
  33. D. Glotsos, I. Kalatzis, P. Spyridonos et al., “Improving accuracy in astrocytomas grading by integrating a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme,” Computer Methods and Programs in Biomedicine, vol. 90, no. 3, pp. 251–261, 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. D. C. Shubhangi and P. S. Hiremath, “Support Vector Machine (SVM) classifier for brain tumor detection,” in Proceedings of the International Conference on Advances in Computing, Communication and Control (ICAC3 '09), pp. 444–448, India, January 2009. View at Publisher · View at Google Scholar · View at Scopus
  35. C. M. Bentaouza and M. Benyettou, “Support vector machines for brain tumours cells classification,” Journal of Applied Sciences, vol. 10, no. 16, pp. 1755–1761, 2010. View at Scopus
  36. M. F. B. Othman, N. B. Abdullah, and N. F. B. Kamal, “MRI brain classification using support vector machine,” in Proceedings of the 4th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO '11), April 2011. View at Publisher · View at Google Scholar · View at Scopus
  37. C. L. Nutt, R. A. Betensky, M. A. Brower, T. T. Batchelor, D. N. Louis, and A. O. Stemmer-Rachamimov, “YKL-40 is a differential diagnostic marker for histologic subtypes of high-grade gliomas,” Clinical Cancer Research, vol. 11, no. 6, pp. 2258–2264, 2005. View at Publisher · View at Google Scholar · View at Scopus
  38. C. L. Nutt, D. R. Mani, R. A. Betensky et al., “Gene expression-based classification of malignant gliomas correlates better with survival than histological classification,” Cancer Research, vol. 63, no. 7, pp. 1602–1607, 2003. View at Scopus
  39. L. P. Petalidis, A. Oulas, M. Backlund et al., “Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data,” Molecular Cancer Therapeutics, vol. 7, no. 5, pp. 1013–1024, 2008. View at Publisher · View at Google Scholar · View at Scopus
  40. J. Schmalzing, M. Kerscher, and T. Buchert, “Minkowski functionals in cosmology,” in Proceedings of the International School of Physics Enrico Fermi. Course CII: Dark Matter in the Universe, S. Bonometto, J. Primack, and A. Provenzale, Eds., pp. 281–291, 1996.
  41. J. Schmalzing and T. Buchert, “Beyond genus statistics: a unifying approach to the morphology of cosmic structure,” Astrophysical Journal Letters, vol. 482, no. 1, pp. L1–L4, 1997. View at Scopus
  42. C. Räth, R. Monetti, J. Bauer et al., “Strength through structure: visualization and local assessment of the trabecular bone structure,” New Journal of Physics, vol. 10, Article ID 125010, 17 pages, 2008. View at Publisher · View at Google Scholar · View at Scopus
  43. H. F. Boehm, T. Schneider, S. M. Buhmann-Kirchhoff et al., “Automated classification of breast parenchymal density: topologic analysis of X-ray attenuation patterns depicted with digital mammography,” American Journal of Roentgenology, vol. 191, no. 6, pp. W275–W282, 2008. View at Publisher · View at Google Scholar · View at Scopus
  44. C. Decaestecker, I. Camby, N. Nagy, J. Brotchi, R. Kiss, and I. Salmon, “Improving morphology-based malignancy grading schemes in astrocytic tumors by means of computer-assisted techniques,” Brain Pathology, vol. 8, no. 1, pp. 29–38, 1998. View at Scopus
  45. H. Martin, K. Voss, P. Hufnagl, and K. Frolich, “Automated image analysis of gliomas. An objective and reproducible method for tumor grading,” Acta Neuropathologica, vol. 63, no. 2, pp. 160–169, 1984. View at Scopus
  46. M. Scarpelli, R. Montironi, D. Thompson, and P. H. Bartels, “Computer-assisted discrimination of glioblastomas,” Analytical and Quantitative Cytology and Histology, vol. 19, no. 5, pp. 369–375, 1997. View at Scopus
  47. D. Schiffer, A. Chio, M. T. Giordana et al., “Histologic prognostic factors in ependymoma,” Child's Nervous System, vol. 7, no. 4, pp. 177–182, 1991. View at Scopus
  48. H. Kolles, A. Von Wangenheim, J. Rahmel, I. Niedermayer, and W. Feiden, “Data-driven approaches to decision making in automated tumor grading: an example of astrocytoma grading,” Analytical and Quantitative Cytology and Histology, vol. 18, no. 4, pp. 298–304, 1996. View at Scopus
  49. H. Martin and K. Voss, “Automated image analysis of glioblastomas and other gliomas,” Acta Neuropathologica, vol. 58, no. 1, pp. 9–16, 1982. View at Scopus
  50. M. Scarpelli, P. H. Bartels, R. Montironi, C. M. Galluzzi, and D. Thompson, “Morphometrically assisted grading of astrocytomas,” Analytical and Quantitative Cytology and Histology, vol. 16, no. 5, pp. 351–356, 1994. View at Scopus
  51. Hutterer St, G. Zauner, M. Huml, R. Silye, and K. Schilcher, “Data mining techniques for AFM- based tumor classification,” in Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB '12), pp. 105–111, 2012.
  52. M. Affenzeller, S. M. Winkler, S. Wagner, and A. Beham, Genetic Algorithms and Genetic Programming, CRC Press, New York, NY, USA, 2009.
  53. J. R. Koza, Genetic Programming: on the Programming of Computers by Means of Natural Selection, The MIT Press, Cambridge, Mass, USA, 1992.