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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 481935, 8 pages
Research Article

Computer Implementation of a New Therapeutic Model for GBM Tumor

1Department of Medical Radiation Engineering, Islamic Azad University, Tehran Science and Research Branch, Tehran 14515-775, Iran
2Electrical Engineering Department, Control Engineering Group, Malek Ashtar University of Technology, Tehran, Iran
3Department of Biomedical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

Received 22 April 2014; Revised 17 June 2014; Accepted 8 July 2014; Published 5 August 2014

Academic Editor: Humberto González-Díaz

Copyright © 2014 Ali Jamali Nazari 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.


Modeling the tumor behavior in the host organ as function of time and radiation dose has been a major study in the previous decades. Here the effort in estimation of cancerous and normal cell proliferation and growth in glioblastoma multiform (GBM) tumor is presented. This paper introduces a new mathematical model in the form of differential equation of tumor growth. The model contains dose delivery amount in the treatment scheme as an input term. It also can be utilized to optimize the treatment process in order to increase the patient survival period. Gene expression programming (GEP) as a new concept is used for estimating this model. The LQ model has also been applied to GEP as an initial value, causing acceleration and improvement of the algorithm estimation. The model shows the number of the tumor and normal brain cells during the treatment process using the status of normal and cancerous cells in the initiation of treatment, the timing and amount of dose delivery to the patient, and a coefficient that describes the brain condition. A critical level is defined for normal cell when the patient’s death occurs. In the end the model has been verified by clinical data obtained from previous accepted formulae and some of our experimental resources. The proposed model helps to predict tumor growth during treatment process in which further treatment processes can be controlled.