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

Azimi, Maryam; Kamrani, Ali K.; Smadi, Hazem J.

doi:https://doi.org/10.1260/2040-2295.3.4.571

Journal of Healthcare Engineering

On this page

Abstract References Copyright Related Articles

Research Article | Open Access

Volume 3 | Article ID 841876 | https://doi.org/10.1260/2040-2295.3.4.571

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

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

Received01 Dec 2011

Accepted01 Jul 2012

Abstract

Most of the current radiation therapy planning systems, which are based on pre-treatment Computer Tomography (CT) images, assume that the tumor geometry does not change during the course of treatment. However, tumor geometry is shown to be changing over time. We propose a methodology to monitor and predict daily size changes of head and neck cancer tumors during the entire radiation therapy period. Using collected patients' CT scan data, MATLAB routines are developed to quantify the progressive geometric changes occurring in patients during radiation therapy. Regression analysis is implemented to develop predictive models for tumor size changes through entire period. The generated models are validated using leave-one-out cross validation. The proposed method will increase the accuracy of therapy and improve patient's safety and quality of life by reducing the number of harmful unnecessary CT scans.

References

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
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.
View at: Google Scholar
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.
View at: Google Scholar
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.
View at: Google Scholar
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
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
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
Y. Wang and L. H. Staib, “Elastic model based non-rigid registration incorporating statistical shape Information,” in Proceeding of MICCAI, pp. 1162–1173, 1998.
View at: Google Scholar
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
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
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
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
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
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
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.
View at: Google Scholar
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.
View at: Google Scholar
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
View at: Google Scholar
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
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.
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
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
M. C. Ferris and M. M. Voelker, “Fractionation in radiation treatment planning,” Math Programming, vol. 101, pp. 387–413, 2004.
View at: Google Scholar
K. R. Swanson, “Can math cure cancer?” Forbes magazine, 2008.
View at: Google Scholar

Copyright

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.

PDF Download Citation Order printed copies

Views

824

Downloads

675

Citations