Xia Li1, Subramani Mani1, Lori R. Arlinghaus1, A. Bapsi chakravarthy1, E. Brian Welch1, Thomas E. Yankeelov1
1Vanderbilt University Institute of Imaging Science, Nashville, TN, United States
Dynamic contrast enhanced MRI (DCE-MRI) can offer information related to tumor perfusion and permeability, vascular volume, extravascular extracellular volume fraction, and the intracellular water lifetime of a water molecule. There have been many efforts employing DCE-MRI as a surrogate biomarker for predicting the response of breast tumors to neoadjuvant chemotherapy. However, most studies perform univariate analysis on these parameters. In this study, we perform multivariate analysis using machine learning methods. The preliminary results demonstrate the feasibility of using DCE-MRI data and machine learning for predicting the response of breast tumors to a single cycle of neoadjuvant chemotherapy.