Atormac
Neurology India
Open access journal indexed with Index Medicus
  Users online: 593  
 Home | Login 
About Current Issue Archive Ahead of print Search Instructions Online Submission Subscribe Videos Etcetera Contact
  Navigate Here 
 »   Next article
 »   Previous article
 »   Table of Contents

 Resource Links
 »   Similar in PUBMED
 »  Search Pubmed for
 »  Search in Google Scholar for
 »Related articles
 »   Citation Manager
 »   Access Statistics
 »   Reader Comments
 »   Email Alert *
 »   Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed112    
    Printed5    
    Emailed0    
    PDF Downloaded26    
    Comments [Add]    

Recommend this journal

 

 GUEST COMMENTARY
Year : 2018  |  Volume : 66  |  Issue : 6  |  Page : 1575--1583

Basic principles of mathematical growth modeling applied to high-grade gliomas: A brief clinical review for clinicians


1 Directorate of Research, General Hospital of Mexico “Dr. Eduardo Liceaga”, Mexico City, Mexico
2 Department of Neurosurgery, General Hospital of Mexico “Dr. Eduardo Liceaga”, Mexico City, Mexico
3 Directorate of Research, General Hospital of Mexico “Dr. Eduardo Liceaga”, Mexico City, Mexico; I.M. Sechenov First Moscow State Medical University (Sechenov University), Department of Radiology, Moscow, Russia

Correspondence Address:
Dr. Ernesto Roldan-Valadez
Directorate of Research, General Hospital of Mexico “Dr. Eduardo Liceaga”, Mexico City, Mexico; I.M. Sechenov First Moscow State Medical University (Sechenov University), Department of Radiology, Moscow, Russia

Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0028-3886.246238

Rights and Permissions

The battle against cancer has intensified in the last decade. New experimental techniques and theoretical models have been been proposed to understand the behavior, growth, and evolution of different types of brain tumors. Unfortunately, for glioblastoma multiforme (GBM), except for methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter that has some benefit in the local control of tumors using alkylating agents such as temozolomide, to date personalized treatments do not exist. In this article, we present a comprehensive review of different aspects intertwined in the mathematical growth modeling applied to high-grade gliomas. We briefly cover the following fundamental aspects related to the conventional imaging in GBM: defining the tumor regions in GBM, segmentation of the tumor regions using magnetic resonance imaging (MRI) of the brain, response assessment using the neuro-oncology response criteria versus the Macdonald criteria, availability of software for the segmentation of MRI of the brain, mathematical modeling applied to tumor growth, principles of mathematical modeling, factors involved in tumor growth models, mathematical modeling based on imaging data, most common equations used in high-grade glioma growth modeling, integration of mathematical growth models in computer simulators, tumor growth modeling as a part of brain's complex system, and challenges in mathematical growth modeling. We conclude by saying that it is the combination of biomedical imaging and mathematical modeling that allows the assembling of clinically relevant models of tumor growth and treatment response; the most appropriate model will depend on the premise and findings of each experiment.






[FULL TEXT] [PDF]*


        
Print this article     Email this article

Online since 20th March '04
Published by Wolters Kluwer - Medknow