The research proposed aims to develop and apply new methods for the quantitative evaluation of tumor growth and treatment response by magnetic resonance imaging (MRI) at the theoretical, pre-clinical, and clinical levels. In particular, we propose to develop and implement novel approaches to quantitative tissue characterization using dynamic contrast enhanced MRI (DCE-MRI), and to integrate these measurements with quantitative metrics derived from other imaging modalities and MR methods. These methods can non-invasively acquire information on, for example, a tumor's cell density, necrotic fraction, neovascularization, and vascular endothelial integrity -- all of which have been shown to be promising reporters on tumor growth and treatment response. Thus, a major goal is to develop noninvasive imaging biomarkers that can serve as surrogates for disease response. We will incorporate other existing and emerging imaging methods into the proposed DCE-MRI studies; namely, diffusion weighted MRI (DW-MRI), FDG-PET, CT, and optical methods, and compare their relative performance separately and in combination. DW-MRl measurements report on a tissue's cellularity and have been shown to correlate with favorable treatment response; FDG-PET reports on tissue metabolism and therefore provides information on cell proliferation rates; CT provides high resolution structural information which will facilitate co-registration of the MR and PET indices; and optical imaging will be employed to locate metastases in a mouse tumor model. Combing the functional information provided by DCE-MR1 techniques, the structural information provided by DW-MRI and CT, and the metabolic information of FDG-PET provides a formidable means of assessing of tumor growth and treatment response. To the best of our knowledge, such quantitative multi-parametric studies of tumors have not yet been performed. Moreover, these studies will make use of high field (3T for humans; 9.4T for animals) MRI which offer higher signal-to-noise ratio measurements not previously obtainable. To realize the goal of developing quantitative, accurate, reproducible, and easily implemented methods to characterize tumor growth and treatment response, we have identified three basic Specific Aims: I) development of appropriate mathematical models to evaluate DCE-MRI data accurately and quantitatively; II) apply and validate these methods in the MMTV-PyVT transgenic mouse model of human breast cancer; III) apply a subset of these methods to evaluate human breast cancer treatment response.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Mentored Quantitative Research Career Development Award (K25)
Project #
5K25EB005936-03
Application #
7270482
Study Section
Subcommittee G - Education (NCI)
Program Officer
Erim, Zeynep
Project Start
2005-08-01
Project End
2010-07-31
Budget Start
2007-08-01
Budget End
2008-07-31
Support Year
3
Fiscal Year
2007
Total Cost
$146,960
Indirect Cost
Name
Vanderbilt University Medical Center
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
004413456
City
Nashville
State
TN
Country
United States
Zip Code
37212
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Loveless, Mary E; Halliday, Jane; Liess, Carsten et al. (2012) A quantitative comparison of the influence of individual versus population-derived vascular input functions on dynamic contrast enhanced-MRI in small animals. Magn Reson Med 67:226-36
Quarles, C Chad; Gore, John C; Xu, Lei et al. (2012) Comparison of dual-echo DSC-MRI- and DCE-MRI-derived contrast agent kinetic parameters. Magn Reson Imaging 30:944-53
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Colvin, Daniel C; Jourquin, Jerome; Xu, Junzhong et al. (2011) Effects of intracellular organelles on the apparent diffusion coefficient of water molecules in cultured human embryonic kidney cells. Magn Reson Med 65:796-801
Arlinghaus, Lori R; Welch, E Brian; Chakravarthy, A Bapsi et al. (2011) Motion correction in diffusion-weighted MRI of the breast at 3T. J Magn Reson Imaging 33:1063-70
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Gore, John C; Xu, Junzhong; Colvin, Daniel C et al. (2010) Characterization of tissue structure at varying length scales using temporal diffusion spectroscopy. NMR Biomed 23:745-56
Yankeelov, Thomas E; Atuegwu, Nkiruka C; Deane, Natasha G et al. (2010) Modeling tumor growth and treatment response based on quantitative imaging data. Integr Biol (Camb) 2:338-45

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