Literature DB >> 17204931

The evolution of mathematical modeling of glioma proliferation and invasion.

Hana L P Harpold1, Ellsworth C Alvord, Kristin R Swanson.   

Abstract

Gliomas are well known for their potential for aggressive proliferation as well as their diffuse invasion of the normal-appearing parenchyma peripheral to the bulk lesion. This review presents a history of the use of mathematical modeling in the study of the proliferative-invasive growth of gliomas, illustrating the progress made in understanding the in vivo dynamics of invasion and proliferation of tumor cells. Mathematical modeling is based on a sequence of observation, speculation, development of hypotheses to be tested, and comparisons between theory and reality. These mathematical investigations, iteratively compared with experimental and clinical work, demonstrate the essential relationship between experimental and theoretical approaches. Together, these efforts have extended our knowledge and insight into in vivo brain tumor growth dynamics that should enhance current diagnoses and treatments.

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Year:  2007        PMID: 17204931     DOI: 10.1097/nen.0b013e31802d9000

Source DB:  PubMed          Journal:  J Neuropathol Exp Neurol        ISSN: 0022-3069            Impact factor:   3.685


  117 in total

1.  Improving the time-machine: estimating date of birth of grade II gliomas.

Authors:  C Gerin; J Pallud; B Grammaticos; E Mandonnet; C Deroulers; P Varlet; L Capelle; L Taillandier; L Bauchet; H Duffau; M Badoual
Journal:  Cell Prolif       Date:  2011-12-14       Impact factor: 6.831

2.  An Adaptive Multigrid Algorithm for Simulating Solid Tumor Growth Using Mixture Models.

Authors:  S M Wise; J S Lowengrub; V Cristini
Journal:  Math Comput Model       Date:  2011-01-01

3.  Glial progenitor cell recruitment drives aggressive glioma growth: mathematical and experimental modelling.

Authors:  Susan Christine Massey; Marcela C Assanah; Kim A Lopez; Peter Canoll; Kristin R Swanson
Journal:  J R Soc Interface       Date:  2012-02-07       Impact factor: 4.118

Review 4.  Magnetic resonance imaging characteristics of glioblastoma multiforme: implications for understanding glioma ontogeny.

Authors:  Leif-Erik Bohman; Kristin R Swanson; Julia L Moore; Russ Rockne; Christopher Mandigo; Todd Hankinson; Marcela Assanah; Peter Canoll; Jeffrey N Bruce
Journal:  Neurosurgery       Date:  2010-11       Impact factor: 4.654

Review 5.  Mechanism-Based Modeling of Tumor Growth and Treatment Response Constrained by Multiparametric Imaging Data.

Authors:  David A Hormuth; Angela M Jarrett; Ernesto A B F Lima; Matthew T McKenna; David T Fuentes; Thomas E Yankeelov
Journal:  JCO Clin Cancer Inform       Date:  2019-02

Review 6.  Computational modeling of the WHO grade II glioma dynamics: principles and applications to management paradigm.

Authors:  Emmanuel Mandonnet; Johan Pallud; Olivier Clatz; Luc Taillandier; Ender Konukoglu; Hugues Duffau; Laurent Capelle
Journal:  Neurosurg Rev       Date:  2008-02-26       Impact factor: 3.042

Review 7.  Clinical implications of in silico mathematical modeling for glioblastoma: a critical review.

Authors:  Maria Protopapa; Anna Zygogianni; Georgios S Stamatakos; Christos Antypas; Christina Armpilia; Nikolaos K Uzunoglu; Vassilis Kouloulias
Journal:  J Neurooncol       Date:  2017-10-28       Impact factor: 4.130

8.  Mathematical Modeling Of Glioma Proliferation And Diffusion.

Authors:  Mahlet Assefa; Russell Rockne; Mindy Szeto; Kristin R Swanson
Journal:  Ethn Dis       Date:  2009       Impact factor: 1.847

9.  The effect of interstitial pressure on tumor growth: coupling with the blood and lymphatic vascular systems.

Authors:  Min Wu; Hermann B Frieboes; Steven R McDougall; Mark A J Chaplain; Vittorio Cristini; John Lowengrub
Journal:  J Theor Biol       Date:  2012-12-07       Impact factor: 2.691

10.  A mathematical model for brain tumor response to radiation therapy.

Authors:  R Rockne; E C Alvord; J K Rockhill; K R Swanson
Journal:  J Math Biol       Date:  2008-09-25       Impact factor: 2.259

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