Abstract #4071

**Forecasting glioblastoma response to anti-angiogenic therapy via image-driven mathematical models**

Tarini Thiagarajan¹, Thomas E Yankeelov^2,3,4,5,6,7, and David A Hormuth, II^2,3

¹Aerospace Engineering, The University of Texas at Austin, Austin, TX, United States, ²Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States, ³Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, United States, ⁴Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States, ⁵Diagnostic Medicine, The University of Texas at Austin, Austin, TX, United States, ⁶Oncology, The University of Texas at Austin, Austin, TX, United States, ⁷Imaging Physics, MD Anderson Cancer Center, Houston, TX, United States

Synopsis

A fundamental challenge in the care of patients with recurrent high-grade gliomas is the selection of appropriate salvage therapies to control further disease progression. To address this challenge, we have developed a biology-based mathematical model of tumor growth and response initialized and calibrated from patient-specific MRI data to predict which patients will respond to anti-angiogenic therapy. We evaluated the predictive accuracy of this image-driven modeling framework in an initial cohort of four patients. The model accurately predicted future total tumor cell number with an average error of 15%.

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