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

Technical 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.