Program
| MSH2c | |
|---|---|
| Gargi Chakraborty | |
| Department of Pathology, University of Washington | |
| Title | Bridging from Anatomic Imaging to Molecular Imaging through Multi-scale Models for Brain Tumor Growth and Invasion |
| Abstract | Gliomas are diffuse and invasive primary brain tumors that are notoriously difficult to treat and uniformly fatal. Much of the difficulty in improving the outcomes of patients with gliomas lies with the extensive invasive potential of these tumors. To help quantify the effect of tumor cell dispersal on the overall growth of gliomas, over the last several years, we have explored a spatio-temporal bio-mathematical model of gliomas based primarily on two key elements, net rates of proliferation (ρ) and dispersal/invasion (D) of glioma cells - aka proliferation-invasion (PI) model. We have already shown that these two key rates, characteristic of biological aggressiveness, are obtainable from routinely available serial clinical MRIs and, somewhat surprisingly, seem to remain fixed over extended periods of time in individual patients (Harpold et al, 2007). Overall, this model has successfully defined many of the behavioral features of low- and high-grade gliomas, specifically the orderliness of their over-all imageable growth, and predictable durations of survival if not treated or if treated by surgical resection of various extents (Swanson et al, 2002; Mandonnet et al, 2003; Harpold et al, 2007; Swanson et al, 2008). Despite many successes, the PI model is limited in its ability to differentiate between low and high- grade gliomas and the related imaging and histologic changes that occur during progression between theses extremes thought to be linked by the angiogenic cascade. These successes have emboldened us to enhance the mechanistic detail of the model in order to now explore quantitatively the histologic characteristics underlying the biological heterogeneity seen amongst gliomas, effectively linking immunohistochemistry, imaging and individual patients, in vivo. I will discuss how such a multi-scale model can be used to integrate data provided on anatomical imaging (MRI) and functional information provided by molecular imaging (PET) in individual glioma patients. This integrative framework provides novel insight into cancer biology and allows for improved predictions of treatment responsiveness in individual patients. References: 1) Harpold et.al (2007) The evolution of mathematical modeling of glioma proliferation and invasion. J Neuropathol Exp Neurol 66: 1-9 2) Swanson and Alvord (2002) Serial imaging observations and postmortem examination of an untreated glioblastoma: A traveling wave of glioma growth and invasion. Neuro-Oncol 4: 340 3) Mandonnet et.al (2003) Continuous growth of mean tumor diameter in a subset of grade II gliomas. Ann Neurol 53: 524-528 4) Swanson et.al(2008) Predicting Survival of Patients with Glioblastoma by Combining a Mathematical Model and Pre-operative MR imaging Characteristics: A Proof of Principle, British J Cancer 98:113-9 |
| Location | Friedman 153 |