Multivariate Analysis of DCE-MRI for Early Prediction of Breast Tumor Response Using Machine Learning

Xia Li¹, Subramani Mani¹, Lori R. Arlinghaus¹, A. Bapsi chakravarthy¹, E. Brian Welch¹, Thomas E. Yankeelov¹

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.