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Journal of Healthcare Engineering
Volume 3 (2012), Issue 4, Pages 571-586
Research Article

Statistics-Based Prediction Analysis for Head and Neck Cancer Tumor Deformation

Maryam Azimi,1 Ali K. Kamrani,1,2,3 and Hazem J. Smadi4

1Lenovo Corporation, Morrisville, NC, USA
2Design and Free Form Fabrication Laboratory, University of Houston, Houston, TX, USA
3FARCAMT, Industrial Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi Arabia
4Industrial Engineering Department, Jordan University of Science and Technology, Irbid, Jordan

Received 1 December 2011; Accepted 1 July 2012

Copyright © 2012 Hindawi Publishing Corporation. 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. A. Kamrani and M. Azimi, “Geometrical modeling of H&N cancer tumor,” Journal of Rapid Prototyping, vol. 17, no. 1, pp. 55–63, 2011. View at Google Scholar
  2. A. Mohamed, K. S. Kyriacou, and C. Davatzikos, “A statistical approach for estimating brain tumor induced deformation,” in IEEE workshop on Mathematical Methods in Biomedical Image Analysis, pp. 52–59, 2001.
  3. A. Zizzari, U. Seiffert, B. Michaelis, G. Gademann, and S. Swiderski, “Detection of tumor in digital images of the brain,” in Proceedings of the IASTED International Conference on Signal Processing, Pattern recognition and Applications, pp. 132–137, Rhodes, Greece, 2001.
  4. A. Kamrani, I. Heramb, and R. George, “Rapid Prototyping Applications in Cancer Tumor Deformation Modeling,” in 34th International Conference on Computers and Industrial Engineering, San Francisco, CA, 2004.
  5. K. H. Hohne, M. Bomans, A. Pommert et al., “3D-Visualization of tomographic volume data using the generalized voxel model,” The Visual Computer, vol. 6, pp. 28–36, 1990. View at Google Scholar
  6. D. Shen, E. H. Herskovits, and C. Davatzikos, “An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures,” IEEE transactions on medical imaging, vol. 20, pp. 257–270, 2001. View at Google Scholar
  7. T. F. Cootes, D. Cooper, C. J. Taylor, and J. Grahant, “Active shape models-their training and application,” Computer Vision and Image Understanding, vol. 61, pp. 38–59, 1995. View at Google Scholar
  8. Y. Wang and L. H. Staib, “Elastic model based non-rigid registration incorporating statistical shape Information,” in Proceeding of MICCAI, pp. 1162–1173, 1998.
  9. P. Korn, N. Sidiropoulos, C. Faloutsos, E. Siegel, and Z. Protopapas, “Fast and effective retrieval of medical tumor shapes,” IEEE transactions on knowledge and data engineering, vol. 10, pp. 889–904, 1998. View at Google Scholar
  10. C. Kervrann and F. Heitz, “Statistical deformable model-based segmentation of image motion,” IEEE transactions on image processing, vol. 8, pp. 583–588, 1999. View at Google Scholar
  11. M. Ferrant, B. Macq, A. Nabavi, and S. K. Warfield, “Deformable Modeling for Characterizing Biomedical Shape Changes,” in Proceedings of 9th international conference on Discrete Geometry for Computer Imagery, pp. 235–248, Springer, Berlin, Heidelberg, 2001. View at Google Scholar
  12. K. Miller and K. Chinzei, “Constitutive modeling of brain tissue,” Experiment and Theory. J. Biomechanics, vol. 30, pp. 1115–1121, 1997. View at Google Scholar
  13. D. Davatzikos, D. Shen, A. Mohamed, and S. Kyriacou, “A framework for predictive modeling of anatomical deformations,” IEEE Transactions on Medical Imaging, vol. 20, pp. 836–843, 2001. View at Google Scholar
  14. P. R. Andersen, F. L. Bookstein, K. Conradsen, B. K. Ersboll, J. L. Marsh, and S. Kreiborg, “Surface bounded growth modeling applied to human mandibles,” IEEE transactions on medical imaging, vol. 19, pp. 1053–1063, 2000. View at Google Scholar
  15. A. A. Asachenkov, “Nonlinear models and data analysis of cancer patients,” in IEEE international symposium on circuits and systems, vol. 3, pp. 1617–1619, 1989.
  16. M. Garbey and G. Zouridakis, “Modeling tumor growth: From differential deformable models to growth prediction of tumors detected in PET images,” in Proceedings of the 25th Annual International Conference on IEEE EMBS, vol. 3, pp. 2687–2690, Cancun, Mexico, 2003.
  17. A. Kamrani and M. Azimi, “Geometrical Modeling of H&N Cancer Tumor,” Journal of Rapid Prototyping, vol. 17, no. 1, pp. 55–63, 2011. View at Google Scholar
  18. D. Delen, G. Walker, and A. Kadam, “Predicting breast cancer survivability: a comparison of three data mining methods,” Artificial Intelligence in Medicine, vol. 34, pp. 113–127, 2005. View at Google Scholar
  19. A. Oztekin, D. Delen, and Z. J. Kong, “Predicting the graft survival for heart-lung transplantation patients: an integrated data mining methodology,” International Journal of Medical Informatics, vol. 78, pp. e84–e96, 2009. View at Google Scholar
  20. A. Kusiak, B. Dixon, and S. Shah, “Predicting survival time for kidney dialysis patient: a data mining approach,” Computers in Biology and Medicine, vol. 35, pp. 311–327, 2005. View at Google Scholar
  21. P. C. Pendharkar, J. A. Rodger, G. J. Yaverbaum, N. Herman, and M. Benner, “Association, statistical, mathematical and neural approaches for mining breast cancer patterns,” Expert System with Applications, vol. 17, pp. 223–232, 1999. View at Google Scholar
  22. C. T. Su, C. H. Yang, K. H. Hsu, and W. K. Chiu, “Data mining for diagnosis of type II diabetes from three-dimensional body surface anthropometrical scanning data,” Computers and Mathematics with Applications, vol. 51, pp. 1075–1092, 2006. View at Google Scholar
  23. S. M. Chou, T. S. Lee, Y. E. Shao, and I. F. Chen, “Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines,” Expert Systems with Application, vol. 27, pp. 133–142, 2004. View at Google Scholar
  24. W. J. Kuo, R. F. Chang, D. R. Chen, and C. C. Lee, “Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images,” Breast Cancer Research and Treatment, vol. 66, pp. 51–57, 2001. View at Google Scholar
  25. T. A. Jilani, H. Yasin, M. Yasin, and C. Ardil, “Acute coronary syndrome prediction using data mining techniques-An application,” World Academy of Science, Engineering and Technology, vol. 59, pp. 474–478, 2009. View at Google Scholar
  26. J. Chhatwal, O. Alagoz, M. J. Lindstrom, C. E. Kahn, K. A. Shaffer, and E. S. Burnside, “A logistic regression model based on national mammography database format to aid breast cancer diagnosis,” AJR Am J Roentgenol, vol. 192, pp. 1117–1127, 2009. View at Google Scholar
  27. Y. Chen, S. Wang, C. H. Shen, and F. K. Choy, “Intelligent identification of childhood musical murmurs,” Journal of Healthcare Engineerng, vol. 3, no. 1, pp. 125–140, 2012. View at Google Scholar
  28. A. A. Hanna and N. D. Keizer, “Integrating classification trees with local logistic regression in intensive Care prognosis,” Artificial Intelligence in Medicine, vol. 29, pp. 5–23, 2003. View at Google Scholar
  29. M. Ture, F. Tokatli, and I. K. Omurlu, “The comparisons of prognostic indexes using data mining techniques and Cox regression analysis in the breast cancer data,” Expert Systems with Applications, vol. 36, pp. 8247–8254, 2009. View at Google Scholar
  30. A. Osareh and B. Shadgar, “Classification and diagnostic prediction of cancers using gene microarray data analysis,” Journal of Applied Sciences, vol. 9, pp. 459–468, 2009. View at Google Scholar
  31. T. Jonsdottir, E. T. Hvannberg, H. Sigurdsson, and S. Sigurdsson, “The feasibility of constructing a predictive outcome model for breast cancer using the tools of data mining,” Expert System with Applications, vol. 34, pp. 108–118, 2008. View at Google Scholar
  32. J. M. Jerez-Aragones, J. A. Gomez-Ruiz, G. Ramos-Jimenez, J. Munoz-Perez, and E. Alba-Cpnejo, “A combined neural network and decision trees model for prognosis of breast cancer relapse,” Artificial Intelligence in Medicine, vol. 27, pp. 45–63, 2003. View at Google Scholar
  33. H. Inamdar, A. Kamrani, and R. George, “Rapid prototyping application in cancer tumor deformation modeling,” in Proceeding of the 34th international conference on computers and industrial engineering, 2004.
  34. R. Olszewski, “Surgical engineering in Cranio-Maxillofacial surgery: A literature review,” Journal of Healthcare Engineering, vol. 3, no. 1, pp. 53–86, 2012. View at Google Scholar
  35. R. M. Mickey, O. L. Dunn, and V. A. Clark, Applied Statistics Analysis of Variance and Regression, Wiley and Sons Inc, New Jersey, 3rd edition, 2004.
  36. M. Kima, A. Ghateb, and M. H. Phillipsa, “A stochastic control formalism for dynamic biologically conformal radiation therapy,” European Journal of operation research, vol. 219, pp. 541–556, 2012. View at Google Scholar
  37. M. Kim, A. Ghate, and M. H. Phillips, “A Markov decision process approach to temporal modulation of dose fractions in radiation therapy planning,” Physics in Medicine and Biology, vol. 54, pp. 4455–4476, 2009. View at Google Scholar
  38. M. C. Ferris and M. M. Voelker, “Fractionation in radiation treatment planning,” Math Programming, vol. 101, pp. 387–413, 2004. View at Google Scholar
  39. K. R. Swanson, “Can math cure cancer?” Forbes magazine, 2008. View at Google Scholar