Meeting Banner
Abstract #1730

A Mechanically Coupled Reaction-Diffusion Model for Predicting the Response of Breast Cancer to Neoadjuvant Chemotherapy

Jared A. Weis1, 2, Michael I. Miga, 13, Lori R. Arlinghaus4, Xia Li4, A. Bapsi Chakravarthy5, 6, Vandana G. Abramson, 67, Jaime Farley, 67, Thomas E. Yankeelov, 24

1Vanderbilt University Institute of Imaging Science , Vanderbilt University, Nashville, TN, United States; 2Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States; 3Biomedical Engineering, Vanderbilt University, Nashville, TN, United States; 4Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States; 5Radiation Oncology, Vanderbilt University, Nashville, TN, United States; 6Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, United States; 7Medical Oncology, Vanderbilt University, Nashville, TN, United States

There is currently a paucity of reliable techniques for predicting the response of breast cancer to neoadjuvant chemotherapy. One promising approach to address this clinical need is to integrate quantitative in-vivo imaging data into biomathematical models of tumor growth to predict eventual response based on early measurements during therapy. Using contrast enhanced, diffusion weighted, and structural MRI data acquired before and after the first cycle of therapy, we illustrate a mathematical modeling approach incorporating tissue mechanical properties leads to more accurate predictions of tumor response to therapy than when such properties are ignored.

Keywords

accuracy adaptive address agree agreement anatomical audience basis beginning best biomedical black blue breast cancer cell chemotherapy clinical comparing completion constrained constraint contrast converted coupled coupling course currently cycle cycles define depending describe described determined diffusion early effort eight employ engineering enhanced enhances equation equilibrium errors eventual evolution excellent exhibiting expansive experimental feedback final force forward generating goal governing governs greater growth guide in vivo incorporating incorporation indicate indicates infusion initial initialize institute integrate integrates intensity logistic longitudinally maps mathematical mechanical mechanically mechanics mechanistic medical mises model modeling models need observations oncology optimize optimized outlines patient patients paucity physically post predict predicted predicting prediction predictions preserving previously prior project projected promising properties quantitative radiation radiological radiology random reaction realistic regimen registered reliable respectively response scanner science sciences sets specificity status stiffness stress suggests surrounding target term therapy tissue transformed tumor tumors unity valuable volume