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Vanderbilt University

PET-MRI for Assessing Treatment Response in Breast Cancer Clinical Trials
Thomas E. Yankeelov. Ph.D.
Vanderbilt University
Grant Number: U01 CA142565

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, they 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. They 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 their imaging techniques in both simple and complex trial environments. The group has identified the following Specific Aims to be accomplished:

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

Their 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 which should 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, they will be able to provide significant direction on developing personalized treatment strategies for this important patient population. Furthermore, they will be well-positioned to proceed to larger multi-site trials—a necessary step towards adoption into routine clinical algorithms.