Mathematical Modeling of Human Glioma Growth Based on Brain Topological Structures: Study of Two Clinical Cases
- Autores
- Suárez, Cecilia Ana; Maglietti, Felipe Horacio; Colonna, Mario; Breitburd, Karina; Marshall, Guillermo Ricardo
- Año de publicación
- 2012
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- 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.
Fil: Suárez, Cecilia Ana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina
Fil: Maglietti, Felipe Horacio. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Colonna, Mario. Hospital Aleman; Argentina
Fil: Breitburd, Karina. Hospital Aleman; Argentina
Fil: Marshall, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina - Materia
-
ONCOLOGY
GLIOMA
MATHEMATICAL MODEL
GROWTH - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
.jpg)
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/274277
Ver los metadatos del registro completo
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Mathematical Modeling of Human Glioma Growth Based on Brain Topological Structures: Study of Two Clinical CasesSuárez, Cecilia AnaMaglietti, Felipe HoracioColonna, MarioBreitburd, KarinaMarshall, Guillermo RicardoONCOLOGYGLIOMAMATHEMATICAL MODELGROWTHhttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3Gliomas 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.Fil: Suárez, Cecilia Ana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Maglietti, Felipe Horacio. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Colonna, Mario. Hospital Aleman; ArgentinaFil: Breitburd, Karina. Hospital Aleman; ArgentinaFil: Marshall, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaPublic Library of Science2012-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/274277Suárez, Cecilia Ana; Maglietti, Felipe Horacio; Colonna, Mario; Breitburd, Karina; Marshall, Guillermo Ricardo; Mathematical Modeling of Human Glioma Growth Based on Brain Topological Structures: Study of Two Clinical Cases; Public Library of Science; Plos One; 7; 6; 6-2012; 1-111932-6203CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0039616info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0039616info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:56:32Zoai:ri.conicet.gov.ar:11336/274277instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-12 09:56:33.119CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
| dc.title.none.fl_str_mv |
Mathematical Modeling of Human Glioma Growth Based on Brain Topological Structures: Study of Two Clinical Cases |
| title |
Mathematical Modeling of Human Glioma Growth Based on Brain Topological Structures: Study of Two Clinical Cases |
| spellingShingle |
Mathematical Modeling of Human Glioma Growth Based on Brain Topological Structures: Study of Two Clinical Cases Suárez, Cecilia Ana ONCOLOGY GLIOMA MATHEMATICAL MODEL GROWTH |
| 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 |
| dc.creator.none.fl_str_mv |
Suárez, Cecilia Ana Maglietti, Felipe Horacio Colonna, Mario Breitburd, Karina Marshall, Guillermo Ricardo |
| author |
Suárez, Cecilia Ana |
| author_facet |
Suárez, Cecilia Ana Maglietti, Felipe Horacio Colonna, Mario Breitburd, Karina Marshall, Guillermo Ricardo |
| author_role |
author |
| author2 |
Maglietti, Felipe Horacio Colonna, Mario Breitburd, Karina Marshall, Guillermo Ricardo |
| author2_role |
author author author author |
| dc.subject.none.fl_str_mv |
ONCOLOGY GLIOMA MATHEMATICAL MODEL GROWTH |
| topic |
ONCOLOGY GLIOMA MATHEMATICAL MODEL GROWTH |
| purl_subject.fl_str_mv |
https://purl.org/becyt/ford/3.1 https://purl.org/becyt/ford/3 |
| dc.description.none.fl_txt_mv |
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. Fil: Suárez, Cecilia Ana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina Fil: Maglietti, Felipe Horacio. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Colonna, Mario. Hospital Aleman; Argentina Fil: Breitburd, Karina. Hospital Aleman; Argentina Fil: Marshall, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina |
| 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. |
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2012 |
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2012-06 |
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http://hdl.handle.net/11336/274277 Suárez, Cecilia Ana; Maglietti, Felipe Horacio; Colonna, Mario; Breitburd, Karina; Marshall, Guillermo Ricardo; Mathematical Modeling of Human Glioma Growth Based on Brain Topological Structures: Study of Two Clinical Cases; Public Library of Science; Plos One; 7; 6; 6-2012; 1-11 1932-6203 CONICET Digital CONICET |
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http://hdl.handle.net/11336/274277 |
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Suárez, Cecilia Ana; Maglietti, Felipe Horacio; Colonna, Mario; Breitburd, Karina; Marshall, Guillermo Ricardo; Mathematical Modeling of Human Glioma Growth Based on Brain Topological Structures: Study of Two Clinical Cases; Public Library of Science; Plos One; 7; 6; 6-2012; 1-11 1932-6203 CONICET Digital CONICET |
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