| Abstract | Gliomas are diffuse and highly invasive primary brain tumors that account for approximately half of all conditions of this nature [1]. Due to its motility within the brain tissue, the border defining the separation between tumor and healthy tissue is nebulous and gliomas are consequently difficult to treat. In addition, this characteristic of gliomas make them difficult to model with simple exponential models [2]. Work by the Swanson group has produced an angiogenesis-based model that is simple enough to use in principle but accounts for both the diffusivity and proliferative aspects of spatio-temporal glioma growth [3]. There is a distinct difference from the quantitative mathematical model of glioma spatio-temporal growth and the qualitative results from clinical imaging modalities. In the case of positron emission tomography (PET) scans, typically a short-lived radioactive tracer is injected into the the patient\'s bloodstream, whereupon a circular detector monitors the tracer\'s positron emission decay. This work presents a potential tool for simulating clinically observable PET images from glioma model results. This is done by creating a pharmacokinetic/pharmacodynamic (PK/PD) model of tracer activity in the brain and by performing the same data manipulations done in PET acquisitions to the model results, such as filtered backprojection. The PK/PD model was inspired by previous work done in radioactive tracer modeling [4]. The tracer concentration solution is solved using the kinetic model at each voxel of the virtual brain. The tracer activity at each voxel is combined with information provided by an model for angiogenesis and hypoxia in glioma growth and invasion [5] that describes the differing types of tissue at each voxel. Combing these two models, along with filtered backprojection, allows for more realistic simulations of clincially observable PET scans. This may serve as method of validating the glioma model and providing biological insight to the PK/PD action of the FMISO tracer in the brain. [1] E.C. Alvord, Jr. and C.M. Shaw, Neoplasms affecting the nervous system of the elderly. In: S. Duckett, Editor, The Pathology of the Aging Human Nervous System, Lea and Fabiger, Philadelphia (1991), pp. 210–86. [2] F.G. Blankenberg et al., The influence of volumetric tumor doubling time, DNA ploidy, and histologic grade on the survival of patients with intracranial astrocytomas. AJNR Am. J. NeuroRad. 16 (1995),1001–12. [3] Swanson, K. R., et al. (2003). Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion. Journal of the Neurological Sciences. 216 (1), 1. [4] Thorwarth, D., et al. (2005). A kinetic model for dynamic [18F]-Fmiso PET data to analyse tumour hypoxia. Physics in Medicine & Biology. 50 (10), 2209-24. [5] H. Harpold, et al. (2006) Silico Model Integrating the Angiogenic Cascade Accurately Simulates Low and High-Grade Human Gliomas. Brain Pathology 16: S4-S4 007 |
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