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ISRN Biomathematics
Volume 2012 (2012), Article ID 287394, 12 pages
http://dx.doi.org/10.5402/2012/287394
Review Article

Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer

Thomas E. Yankeelov1,2,3,4,5,6

1Institute of Imaging Science, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2310, USA
2Department of Radiology and Radiological Sciences, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2310, USA
3Department of Biomedical Engineering, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2310, USA
4Department of Physics, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2310, USA
5Department of Cancer Biology, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2310, USA
6Vanderbilt Ingram Cancer Center, Vanderbilt University, 1161 21st Avenue South, Nashville, TN 37232-2310, USA

Received 1 November 2012; Accepted 21 November 2012

Academic Editors: J. Chow, S.-C. Ngan, and J. Suehnel

Copyright © 2012 Thomas E. Yankeelov. 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.

Abstract

While there is a mature literature on biomathematical and biophysical modeling in cancer, many of the existing approaches are not of clinical utility, as they require input data that are extremely difficult to obtain in an intact organism, and/or require a large number of assumptions on the free parameters included in the models. Thus, there has only been very limited application of such models to solve problems of clinical import. More recently, however, there has been increased activity at the interface of quantitative, noninvasive imaging data, and tumor mathematical modeling. In addition to reporting on bulk tumor morphology and volume, emerging imaging techniques can quantitatively report on for example tumor vascularity, glucose metabolism, cell density and proliferation, and hypoxia. In this paper, we first motivate the problem of predicting therapy response by highlighting some (acknowledged) shortcomings in existing methods. We then provide introductions to a number of representative quantitative imaging methods and describe how they are currently (and potentially can be) used to initialize and constrain patient specific mathematical and biophysical models of tumor growth and treatment response, thereby increasing the clinical utility of such approaches. We conclude by highlighting some of the exciting research directions when one integrates quantitative imaging and tumor modeling.