Mathematical modeling of human glioma growth based on brain topological structures
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Gliomas are the most common primary brain tumors and yet almost incurable due mainly to their great invasion capability, many times underestimated by present imaging techniques. This represents a challenge to present clinical oncology. Here, we introduce a three-dimensional (3D) space and time, reaction-di usion mathematical model whose space domain distinguish di ferent brain topological structures, that aims to improve tumor spreading capability defi nition. The model is solved numerically using patient-speci c parametrization, fi nite di erences and standard relaxation techniques. The spatial domain consists in a series of digitized images from brain slices covering the whole human brain. The Talairach atlas, incorporated in our model, describes brain structures at diff erent levels. Simulations considers an initial state with only cellular proliferation (benign tumor) and an advanced state where cellular in ltration begins (malignization). The inclusion of the Brodmann areas allows prediction of the brain functions that are being a ected during tumor evolution and the estimation of their correlated symptoms. The survival time is estimated on the basis of tumor size and location. The model was associated to two clinical cases predicting, in the fi rst one, that real infi ltration areas are underestimated by current diagnostic imaging. In the second clinical case, tumor spreading predictions were shown to be more accurate than those from previous models from the literature. Our model suggests that the inclusion of dif erential migration based on topological brain structures in glioma growth is another step towards a better prediction of the extension and shape of tumor in ltration at the moment of surgical or radiosurgical target de finition. Also, the addition of physiological/psychological considerations to the previous anatomical glioma growth model will surely provide a better and integral understanding of the patient disease at the moment of deciding therapeutic options, taking into account not only survival but also life quality (PlosOne 7(6): e39616, 2012)
Contacto: csuarez@dc.uba.ar Departamento de Computación Facultad de Ciencias Exactas y Naturales
Universidad de Buenos Aires
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