Abstract

Motivation: To advance the field of pharmacokinetic analysis of breast DCE-MRI by developing a model accounting for inter-fluid transport within tumor tissueGoal(s): To develop a novel DCE-MRI pharmacokinetic method to quantify and visualize fluid flows in tumors and identify predictive imaging biomarkers of therapeutic response to neoadjuvant chemotherapy (NACT) in breast cancer. Approach: We developed a mathematical model in computational fluid dynamics termed the unbalanced regularized optimal mass transport (urOMT)Results: Our urOMT model provides fluid transport properties of the tumor using breast DCE-MRI; the urOMT-derived quantitative metrics may be future predictive imaging biomarkers to measure treatment effectiveness in patients treated with NACT. Impact: We developed a novel mathematical model to quantify, track, and visualize fluid flows in tumors with breast DCE-MRI data. The proposed quantitative metrics after validation may serve as predictive imaging biomarkers for breast cancer patients treated with neoadjuvant chemotherapy.