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Journal of Healthcare Engineering
Volume 3, 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.


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.