Abstract #0136

Effect of chemoradiation on high-grade gliomas can be forecasted by mid-treatment images via image-driven mathematical modeling

David A Hormuth II^1,2, Maguy Farhat³, Julianna Bronk³, Holly Langshaw³, Thomas E Yankeelov^1,2,4,5,6,7, and Caroline Chung³

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

Synopsis

Keywords: Tumors, RadiotherapyTechnical advances in imaging and radiotherapy in the last decade have motivated the development of patient-specific treatment plans accounting for the underlying tumor biology that ultimately informs treatment efficacy. A fundamental challenge is how to leverage imaging data to optimize patient response to radiotherapy. Towards this end, we have developed an approach to forecast tumor response prior to the conclusion of therapy to enable a spatially-resolved map of response to chemoradiation. Our forecasting approach can accurately identify statistically significant changes in cell density that could potentially inform treatment adaptations.

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