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

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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Cecilia Suarez, Felipe Maglietti, Mario Colonna, Karina Breitburd, Guillermo Marshall
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b985a8ba5a694fb682355e325a5fcbed
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b985a8ba5a694fb682355e325a5fcbed
record_format	dspace
spelling	oai:doaj.org-article:b985a8ba5a694fb682355e325a5fcbed2021-11-18T07:14:07ZMathematical modeling of human glioma growth based on brain topological structures: study of two clinical cases.1932-620310.1371/journal.pone.0039616https://doaj.org/article/b985a8ba5a694fb682355e325a5fcbed2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22761843/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Gliomas 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.Cecilia SuarezFelipe MagliettiMario ColonnaKarina BreitburdGuillermo MarshallPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 6, p e39616 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Cecilia Suarez Felipe Maglietti Mario Colonna Karina Breitburd Guillermo Marshall Mathematical modeling of human glioma growth based on brain topological structures: study of two clinical cases.
description	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.
format	article
author	Cecilia Suarez Felipe Maglietti Mario Colonna Karina Breitburd Guillermo Marshall
author_facet	Cecilia Suarez Felipe Maglietti Mario Colonna Karina Breitburd Guillermo Marshall
author_sort	Cecilia Suarez
title	Mathematical modeling of human glioma growth based on brain topological structures: study of two clinical cases.
title_short	Mathematical modeling of human glioma growth based on brain topological structures: study of two clinical cases.
title_full	Mathematical modeling of human glioma growth based on brain topological structures: study of two clinical cases.
title_fullStr	Mathematical modeling of human glioma growth based on brain topological structures: study of two clinical cases.
title_full_unstemmed	Mathematical modeling of human glioma growth based on brain topological structures: study of two clinical cases.
title_sort	mathematical modeling of human glioma growth based on brain topological structures: study of two clinical cases.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/b985a8ba5a694fb682355e325a5fcbed
work_keys_str_mv	AT ceciliasuarez mathematicalmodelingofhumangliomagrowthbasedonbraintopologicalstructuresstudyoftwoclinicalcases AT felipemaglietti mathematicalmodelingofhumangliomagrowthbasedonbraintopologicalstructuresstudyoftwoclinicalcases AT mariocolonna mathematicalmodelingofhumangliomagrowthbasedonbraintopologicalstructuresstudyoftwoclinicalcases AT karinabreitburd mathematicalmodelingofhumangliomagrowthbasedonbraintopologicalstructuresstudyoftwoclinicalcases AT guillermomarshall mathematicalmodelingofhumangliomagrowthbasedonbraintopologicalstructuresstudyoftwoclinicalcases
_version_	1718423752281161728

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

Ejemplares similares