The vision for this proposal is to develop and validate a clinical-computational framework to predict and spatially-map more aggressive and treatment-resistant regions to enable personalized treatment in patients receiving radiation therapy for gliomas. We seek to accomplish this goal through the integration of multiparametric magnetic resonance imaging (mpMRI) data with mechanism-based mathematical modeling to predict disease response. This will enable clinicians to forecast tumor behavior during the course of radiotherapy (RT) instead of merely assessing post treatment.

The ability to predict spatiotemporal disease progression creates a paradigm shift as it enables timely adaptations of t...

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The vision for this proposal is to develop and validate a clinical-computational framework to predict and spatially-map more aggressive and treatment-resistant regions to enable personalized treatment in patients receiving radiation therapy for gliomas. We seek to accomplish this goal through the integration of multiparametric magnetic resonance imaging (mpMRI) data with mechanism-based mathematical modeling to predict disease response. This will enable clinicians to forecast tumor behavior during the course of radiotherapy (RT) instead of merely assessing post treatment.

The ability to predict spatiotemporal disease progression creates a paradigm shift as it enables timely adaptations of treatment plans to target resistant disease early on, before clinical progression is observed. We hypothesize that early image-driven mechanistic predictions of resistant tumor subregions will enable anticipatory pre-emptive targeting of RT to dramatically improve tumor control, patient survival and quality of life.

To test this hypothesis, we propose two specific aims. In Aim 1, we will continue to develop our novel mechanism-based computational model to be able to identify treatment-resistant regions in patients with high-grade gliomas. In Aim 2, we will develop a clinical-computational framework that describes the mechanisms of progression and treatment resistance of non-enhancing low and high grade gliomas. Both Aims will employ serial quantitative mpMRI to observe dynamics of tumor behavior before, during and after treatment with RT. We will develop a family of 3D mechanism-based models describing fundamental mechanisms of brain tumor growth and treatment response, which are then calibrated for each patient using their own longitudinal imaging data to predict response. Success of the project will provide an avenue to optimize and personalize therapy for each patient, and undoubtedly represent a dramatic shift in glioma treatment.

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