Literature DB >> 25974347

Mathematically modeling the biological properties of gliomas: A review.

Nikolay L Martirosyan1, Erica M Rutter, Wyatt L Ramey, Eric J Kostelich, Yang Kuang, Mark C Preul.   

Abstract

Although mathematical modeling is a mainstay for industrial and many scientific studies, such approaches have found little application in neurosurgery. However, the fusion of biological studies and applied mathematics is rapidly changing this environment, especially for cancer research. This review focuses on the exciting potential for mathematical models to provide new avenues for studying the growth of gliomas to practical use. In vitro studies are often used to simulate the effects of specific model parameters that would be difficult in a larger-scale model. With regard to glioma invasive properties, metabolic and vascular attributes can be modeled to gain insight into the infiltrative mechanisms that are attributable to the tumor's aggressive behavior. Morphologically, gliomas show different characteristics that may allow their growth stage and invasive properties to be predicted, and models continue to offer insight about how these attributes are manifested visually. Recent studies have attempted to predict the efficacy of certain treatment modalities and exactly how they should be administered relative to each other. Imaging is also a crucial component in simulating clinically relevant tumors and their influence on the surrounding anatomical structures in the brain.

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Year:  2015        PMID: 25974347     DOI: 10.3934/mbe.2015.12.879

Source DB:  PubMed          Journal:  Math Biosci Eng        ISSN: 1547-1063            Impact factor:   2.080


  7 in total

1.  3D Mathematical Modeling of Glioblastoma Suggests That Transdifferentiated Vascular Endothelial Cells Mediate Resistance to Current Standard-of-Care Therapy.

Authors:  Huaming Yan; Mónica Romero-López; Lesly I Benitez; Kaijun Di; Hermann B Frieboes; Christopher C W Hughes; Daniela A Bota; John S Lowengrub
Journal:  Cancer Res       Date:  2017-05-23       Impact factor: 12.701

2.  THE PROHOROV METRIC FRAMEWORK AND AGGREGATE DATA INVERSE PROBLEMS FOR RANDOM PDEs.

Authors:  H T Banks; K B Flores; I G Rosen; E M Rutter; Melike Sirlanci; W Clayton Thompson
Journal:  Commun Appl Anal       Date:  2018-06-19

Review 3.  The biology and mathematical modelling of glioma invasion: a review.

Authors:  J C L Alfonso; K Talkenberger; M Seifert; B Klink; A Hawkins-Daarud; K R Swanson; H Hatzikirou; A Deutsch
Journal:  J R Soc Interface       Date:  2017-11       Impact factor: 4.118

4.  Mathematical Analysis of Glioma Growth in a Murine Model.

Authors:  Erica M Rutter; Tracy L Stepien; Barrett J Anderies; Jonathan D Plasencia; Eric C Woolf; Adrienne C Scheck; Gregory H Turner; Qingwei Liu; David Frakes; Vikram Kodibagkar; Yang Kuang; Mark C Preul; Eric J Kostelich
Journal:  Sci Rep       Date:  2017-05-31       Impact factor: 4.379

5.  Towards patient-specific modeling of brain tumor growth and formation of secondary nodes guided by DTI-MRI.

Authors:  Stelios Angeli; Kyrre E Emblem; Paulina Due-Tonnessen; Triantafyllos Stylianopoulos
Journal:  Neuroimage Clin       Date:  2018-08-31       Impact factor: 4.881

Review 6.  Advances in Glioblastoma Multiforme Treatment: New Models for Nanoparticle Therapy.

Authors:  Elif Ozdemir-Kaynak; Amina A Qutub; Ozlem Yesil-Celiktas
Journal:  Front Physiol       Date:  2018-03-19       Impact factor: 4.566

7.  Patient-specific parameter estimates of glioblastoma multiforme growth dynamics from a model with explicit birth and death rates.

Authors:  Li Feng Han; Steffen Eikenberry; Chang Han He; Lauren Johnson; Mark C Preul; Eric J Kostelich; Yang Kuang
Journal:  Math Biosci Eng       Date:  2019-06-11       Impact factor: 2.080

  7 in total

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