Upcoming Event: PhD Dissertation Defense

Utilizing MRI-derived habitat modeling to predict brain tumor dynamics during radiotherapy

Ayesha Das, CSEM Ph.D. Candidate

Abstract

The overall goal of this project is to develop and apply quantitative magnetic resonance imaging (MRI) measurements of tumor cellularity, perfusion, and hypoxia to inform mathematical models for predicting radiation response to brain tumors. Variations in these three parameters leads to intratumoral heterogeneity which is associated with increased resistance to radiotherapy and poor patient outcomes. Therefore, it is necessary to establish noninvasive methods for quantifying these important properties of the tumor microenvironment. To accomplish this goal, in Aim 1 we use quantitative MRI measurements of cellularity, perfusion, and hypoxia to identify tumor subregions (i.e., “habitats”) in a preclinical model of glioma. This approach provides a foundation for longitudinally tracking changes in tumor composition during therapy, enabling more precise assessment of treatment response. In Aim 2, we use these image-derived habitats in preclinical glioma to predict tumor response during radiotherapy via biology-based, mathematical modeling. We test the application of using up to four habitats to monitor changes during treatment with mathematical modeling. Finally in Aim 3, we extend our habitat development approach to predict the response of patients diagnosed with high grade glioma to radiotherapy Overall, these developments demonstrate the effectiveness of habitat identification in both the preclinical and clinical setting and the predictive ability of using habitats in predicting treatment response in radiotherapy in a patient-specific basis.