Mathematical modeling of human glioma growth based on brain topological structures: Study of two clinical cases

• Autores: Suarez, C.; Maglietti, F.; Colonna, M.; Breitburd, K.; Marshall, G.
• Año de publicación: 2012
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: PLoS ONE 2012;7(6)
• Materia: adult; advanced cancer; article; brain function; brain tissue; Broadmann areas; cancer invasion; case report; cell infiltration; cell migration; cell proliferation; controlled study; diagnostic accuracy; diffusion; evolution; glioma; human; human tissue; male; malignant neoplastic disease; mathematical model; nuclear magnetic resonance imaging; pathological anatomy; prediction; problem solving; simulation; survival time; symptomatology; Talairach atlas; three dimensional imaging; tumor growth; tumor localization; tumor volume; Adult; Brain Neoplasms; Computer Simulation; Disease Progression; Glioma; Humans; Male; Middle Aged; Models, Theoretical; Temporal Lobe
• Institución: Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales

Gliomas are the most common primary brain tumors and yet almost incurable due mainly to their great invasion capability. This represents a challenge to present clinical oncology. Here, we introduce a mathematical model aiming to improve tumor spreading capability definition. The model consists in a time dependent reaction-diffusion equation in a three-dimensional spatial domain that distinguishes between different brain topological structures. The model uses a series of digitized images from brain slices covering the whole human brain. The Talairach atlas included in the model describes brain structures at different levels. Also, the inclusion of the Brodmann areas allows prediction of the brain functions affected during tumor evolution and the estimation of correlated symptoms. The model is solved numerically using patient-specific parametrization and finite differences. Simulations consider an initial state with cellular proliferation alone (benign tumor), and an advanced state when infiltration starts (malign tumor). Survival time is estimated on the basis of tumor size and location. The model is used to predict tumor evolution in two clinical cases. In the first case, predictions show that real infiltrative areas are underestimated by current diagnostic imaging. In the second case, tumor spreading predictions were shown to be more accurate than those derived from previous models in the literature. Our results suggest that the inclusion of differential migration in glioma growth models constitutes another step towards a better prediction of tumor infiltration at the moment of surgical or radiosurgical target definition. Also, the addition of physiological/psychological considerations to classical anatomical models will 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. © 2012 Suarez et al.
Fil:Suarez, C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.