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.
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