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.
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