A challenge to oncologists in the 21st century will be the individualization of patient care. Already in some cancers, treatment decisions are based on patients'biochemical indicators at presentation and the response of these indicators to therapy. Clearly, the development of non-invasive and robust methods to monitor therapeutic response would be helpful to the overall care of individualizing cancer treatment. If such methods were developed, patients could be continued on successful therapies and changed from therapies that are proving ineffective. We hypothesize that diffusion MRI, which measures the apparent diffusion coefficient (ADC) of tissue water, has great potential to be a non-invasive marker for effective cancer chemotherapy. A significant amount of pre-clinical data indicates that the ADC of tumor water becomes elevated after successful anti-cancer chemotherapy. Based on the strength of these data, changes in ADC have been proposed as a quantitative biomarker for response in a clinical setting. Preliminary human data from a few research groups supports the hypothesis that early increases in ADC can predict ultimate clinical benefit. Our group has contributed to this endeavor by focusing on metastatic breast cancer. We have focused on this disease setting primarily because there are therapeutic choices, and hence, patients could benefit from knowledge of response (or non response). In preliminary data, we have observed therapy related changes in ADC in diffusion MR images from the common metastatic sites of liver and bone from breast cancer patients. The purpose of this proposal is to expand on this work to determine if early changes in the ADC can quantitatively presage clinical response in liver (Aim 1), bone (Aim 2) and brain (Aim 3) metastases of breast cancer.
A fourth aim i s focused on developing more sophisticated image analysis tools with the goal of extracting the most information from DWMRI datasets. The current protocol will use approved therapies with known response rates in order to validate diffusion MRI as a response biomarker. If justified from this study, further validation will be pursued in a multicenter trial designed to test if diffusion MRI will be useful to monitor therapeutic responses in routine clinical practice as well as in trials of experimental therapies.
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| Rosado-Toro, José A; Barr, Tomoe; Galons, Jean-Philippe et al. (2015) Automated breast segmentation of fat and water MR images using dynamic programming. Acad Radiol 22:139-48 |
| Jha, Abhinav K; Kupinski, Matthew A; Rodríguez, Jeffrey J et al. (2012) Task-based evaluation of segmentation algorithms for diffusion-weighted MRI without using a gold standard. Phys Med Biol 57:4425-46 |
| Jha, Abhinav K; Rodríguez, Jeffrey J; Stephen, Renu M et al. (2010) A Clustering Algorithm for Liver Lesion Segmentation of Diffusion-Weighted MR Images. Proc IEEE Southwest Symp Image Anal Interpret 2010:93-96 |
| Jha, Abhinav K; Kupinski, Matthew A; Rodríguez, Jeffrey J et al. (2010) Evaluating segmentation algorithms for diffusion-weighted MR images: a task-based approach. Proc SPIE Int Soc Opt Eng 7627: |
