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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 637181, 13 pages
Research Article

Modeling of Non-Small Cell Lung Cancer Volume Changes during CT-Based Image Guided Radiotherapy: Patterns Observed and Clinical Implications

1Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, Campus Box 8224, St. Louis, MO, 63110, USA
2Maine Medical Center, Southern Maine Radiation Therapy Institute, 22 Bramhall Street, Portland, ME 04102, USA
3Department of Radiation Oncology, Pocono Medical Center, 206 East Brown Street, East Stroudsburg, PA 18301, USA
4Nash Cancer Treatment Center, 2450 Curtis Ellis Drive, Rocky Mount, NC 27804, USA
5Department of Medical Oncology, The Brody School of Medicine, East Carolina University, 600 Moye Boulevard, Greenville, NC 27834, USA
621st Century Oncology, 801 W.H. Smith Boulevard, Greenville, NC 27834, USA
7Laboratory of Anticancer Pharmacology, Department of Oncology, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milan, Italy

Received 7 March 2013; Revised 29 July 2013; Accepted 26 August 2013

Academic Editor: Thierry Busso

Copyright © 2013 Hiram A. Gay 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.


Background. To characterize the lung tumor volume response during conventional and hypofractionated radiotherapy (RT) based on diagnostic quality CT images prior to each treatment fraction. Methods. Out of 26 consecutive patients who had received CT-on-rails IGRT to the lung from 2004 to 2008, 18 were selected because they had lung lesions that could be easily distinguished. The time course of the tumor volume for each patient was individually analyzed using a computer program. Results. The model fits of group L (conventional fractionation) patients were very close to experimental data, with a median Δ% (average percent difference between data and fit) of 5.1% (range 3.5–10.2%). The fits obtained in group S (hypofractionation) patients were generally good, with a median Δ% of 7.2% (range 3.7–23.9%) for the best fitting model. Four types of tumor responses were observed—Type A: “high” kill and “slow” dying rate; Type B: “high” kill and “fast” dying rate; Type C: “low” kill and “slow” dying rate; and Type D: “low” kill and “fast” dying rate. Conclusions. The models used in this study performed well in fitting the available dataset. The models provided useful insights into the possible underlying mechanisms responsible for the RT tumor volume response.