The long-term vision of this program is to improve patient care by optimizing, validating, and extending quantitative MRI methods for the early prediction of breast cancer response to neoadjuvant therapy (NAT). During the first period of support, we developed several experimental and computational tools for improving quantitative dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) of the breast. These tools were successfully applied in clinical trials and the resulting data were incorporated into a statistical model to predict, after only one cycle of treatment, the eventual pathological complete response (pCR) of breast tumors to NAT. We now have the opportunity to deploy these techniques in two multi-site clinical trials, focused on triple negative breast cancer (TNBC), to be opened simultaneously at Vanderbilt University and the University of Chicago. These trials offer the opportunity to validate and then extend our imaging techniques in both simple and complex trial environments. Thus, we have identified the following Specific Aims:
Aim 1. Optimize quantitative DCE- and DW-MRI for two multi-site breast cancer clinical trials Aim 2. Validate quantitative MRI for predicting breast cancer treatment response early during NAT Aim 3. Extend quantitative MRI by predicting breast cancer treatment response during a complex NAT trial Our overarching hypothesis (guided by the results from the first period of support) is that the synthesis of quantitative DCE- and DW-MRI measured after the first cycle of NAT will achieve an area under the receiver operating characteristic curve of at least 0.87 for predicting the eventual response of TNBC patients to NAT. If this hypothesis is validated, we will be able to provide significant direction on developing personalized treatment strategies for this important patient population. Furthermore, we will be well-positioned to proceed to larger multi-site trials-a necessary step towards adoption into routine clinical algorithms.
The overall goal of this program is to significantly improve patient care by optimizing, validating, and then extending quantitative MRI methods for the early prediction of breast cancer response to neoadjuvant therapy. The knowledge acquired through this study will provide direction on developing personalized treatment strategies for breast cancer patients undergoing neoadjuvant therapy and may motivate a shift in existing paradigms of therapy monitoring and selection in breast cancer. Furthermore, MRI assessment of early response could be broadly applicable to other solid tumors where neoadjuvant therapy is appropriate.
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