Abstract #4071
Forecasting glioblastoma response to anti-angiogenic therapy via image-driven mathematical models
Tarini Thiagarajan1, Thomas E Yankeelov2,3,4,5,6,7, and David A Hormuth, II2,3
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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