My current research interests are in the integration of predictive mathematical models with patient-specific data (imaging or otherwise) to help guide cancer care. Currently, I am developing and validating a set of image-driven biophysical models of glioma growth and treatment response at the preclinical level. This project centers on the integration of two areas of research to increase the applicability of subject-specific modeling. The first area is medical imaging which can provide noninvasive, quantitative, and 3D characterizations of, for example, blood flow, vessel permeability, blood volume, cellularity, hypoxia, metabolism, and cell proliferation. These measureable tumor characteristics can be incorporated into a realistic biophysical model of tumor growth that can be used to predict tumor growth and therapy response on an individual basis. The second area is mathematical models of cancer which often require measurements that are either difficult or impossible to obtain noninvasively limiting their application to subject-specficic predictions. My current projects are focusing on novel ways to model and adapt radiation therapy in a murine model of glioma. The end goal of this work is to be able to develop individualized tumor “forecasts” which can be used by to select treatment, optimize treatment, and identify treatment response.
Plot of tumor volume (left) and cell number maps (right) for a rat receiving 20 Gy whole brain radiotherapy. The effect of radiation is modeled as instant death (Md model), reduced proliferation (Mp) or a combination of instant death and reduced proliferation (Md+p).The Md+p results in the lowest error between the model and measurement.