Currently, breast tumor response to chemotherapy is monitored by frank changes in tumor morphology as measured by physical exam, mammography and/or ultrasound. Clinical judgments of the effectiveness of treatments are subjective and prone to error. A repeatable, non-invasive imaging method which can reliably assess tumor response would greatly improve clinical breast cancer care. The specialized magnetic resonance imaging (MRI) methods of dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) have matured to the point where they offer unique information on tumor status. DCE-MRI reports on relevant physiological parameters including vessel perfusion, vessel wall permeability, extravascular extracellular volume fraction, and (recently) cell size. DW-MRI can provide detailed information on tissue cellularity. We propose to combine a novel analysis of DCE-MRI data with DW-MRI data obtained at 3T to provide functional assessments of the response of breast cancer to treatment. We hypothesize that integrating these quantitative MRI methods will provide accurate and predictive measurements of tumor response after the first cycle of treatment. Furthermore, we will validate the imaging metrics by performing quantitative co- registering the in vivo MR images to histopathological staining of ex vivo mastectomy specimens. To test these hypotheses we will pursue three specific aims: 1. In patients selected by a specific treatment protocol, differentiate responders vs. non-responders by the differences in tumor vessel blood flow and integrity, tissue volume fractions, and tumor cell density. 2. Perform uni- and multi-variate correlation analysis between blood flow, vessel perfusion, extravascular extracellular volume fraction, intracellular water lifetime, and cell density to provide a more complete understanding of the breast tumor environment. 3. Perform quantitative co-registration between in vivo MR images and ex vivo histological specimens to validate the MRI measures. The proposed research will combine several new imaging methods to obtain quantitative information on how breast tumors respond to treatment. We hypothesize that this will let us distinguish responders from non- responders early in the course of treatment so that treatments can be optimized on an individual basis.
The proposed research will combine specialized magnetic resonance imaging (MRI) methods to obtain quantitative information on how human breast tumors respond to treatment. Developing methods of tumor characterization that could be applied early in treatment to assess response would have profound impact on the management of many patients. We hypothesize that the combined analysis of contrast enhanced MRI and diffusion MRI data will provide predictive, non-invasive measurements of tumor response to treatment.
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