Literature DB >> 18308523

Velocity of radial expansion of contrast-enhancing gliomas and the effectiveness of radiotherapy in individual patients: a proof of principle.

K R Swanson1, H L P Harpold, D L Peacock, R Rockne, C Pennington, L Kilbride, R Grant, J M Wardlaw, E C Alvord.   

Abstract

AIMS: The initial aims were to use recently available observations of glioblastomas (as part of a previous study) that had been imaged twice without intervening treatment before receiving radiotherapy in order to obtain quantitative measures of glioma growth and invasion according to a new bio-mathematical model. The results were so interesting as to raise the question whether the degree of radio-sensitivity of each tumour could be estimated by comparing the model-predicted and actual durations of survival and total numbers of glioma cells after radiotherapy.
MATERIALS AND METHODS: The gadolinium-enhanced T1-weighted and T2-weighted magnetic resonance imaging volumes were segmented and used to calculate the velocity of radial expansion (v) and the net rates of proliferation (rho) and invasion/dispersal (D) for each patient according to the bio-mathematical model.
RESULTS: The ranges of the values of v, D and rho show that glioblastomas, although clustering at the high end of rates, vary widely one from the other. The effects of X-ray therapy varied from patient to patient. About half survived as predicted without treatment, indicating radio-resistance of these tumours. The other half survived up to about twice as long as predicted without treatment and could have had a corresponding loss of glioma cells, indicating some degree of radio-sensitivity. These results approach the historical estimates that radiotherapy can double survival of the average patient with a glioblastoma.
CONCLUSIONS: These cases are among the first for which values of v, D and rho have been calculated for glioblastomas. The results constitute a 'proof of principle' by combining our bio-mathematical model for glioma growth and invasion with pre-treatment imaging observations to provide a new tool showing that individual glioblastomas may be identified as having been radio-resistant or radio-sensitive.

Entities:  

Mesh:

Substances:

Year:  2008        PMID: 18308523     DOI: 10.1016/j.clon.2008.01.006

Source DB:  PubMed          Journal:  Clin Oncol (R Coll Radiol)        ISSN: 0936-6555            Impact factor:   4.126


  25 in total

1.  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 2.  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

3.  Quantifying the role of angiogenesis in malignant progression of gliomas: in silico modeling integrates imaging and histology.

Authors:  Kristin R Swanson; Russell C Rockne; Jonathan Claridge; Mark A Chaplain; Ellsworth C Alvord; Alexander R A Anderson
Journal:  Cancer Res       Date:  2011-09-07       Impact factor: 12.701

4.  Quantifying the roles of cell motility and cell proliferation in a circular barrier assay.

Authors:  Matthew J Simpson; Katrina K Treloar; Benjamin J Binder; Parvathi Haridas; Kerry J Manton; David I Leavesley; D L Sean McElwain; Ruth E Baker
Journal:  J R Soc Interface       Date:  2013-02-20       Impact factor: 4.118

5.  Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach.

Authors:  R Rockne; J K Rockhill; M Mrugala; A M Spence; I Kalet; K Hendrickson; A Lai; T Cloughesy; E C Alvord; K R Swanson
Journal:  Phys Med Biol       Date:  2010-05-18       Impact factor: 3.609

Review 6.  Advanced magnetic resonance imaging of the physical processes in human glioblastoma.

Authors:  Jayashree Kalpathy-Cramer; Elizabeth R Gerstner; Kyrre E Emblem; Ovidiu Andronesi; Bruce Rosen
Journal:  Cancer Res       Date:  2014-09-01       Impact factor: 12.701

7.  Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival.

Authors:  Corbin A Rayfield; Fillan Grady; Gustavo De Leon; Russell Rockne; Eduardo Carrasco; Pamela Jackson; Mayur Vora; Sandra K Johnston; Andrea Hawkins-Daarud; Kamala R Clark-Swanson; Scott Whitmire; Mauricio E Gamez; Alyx Porter; Leland Hu; Luis Gonzalez-Cuyar; Bernard Bendok; Sujay Vora; Kristin R Swanson
Journal:  JCO Clin Cancer Inform       Date:  2018-12

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.  Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model.

Authors:  Christina H Wang; Jason K Rockhill; Maciej Mrugala; Danielle L Peacock; Albert Lai; Katy Jusenius; Joanna M Wardlaw; Timothy Cloughesy; Alexander M Spence; Russ Rockne; Ellsworth C Alvord; Kristin R Swanson
Journal:  Cancer Res       Date:  2009-11-24       Impact factor: 12.701

10.  Response classification based on a minimal model of glioblastoma growth is prognostic for clinical outcomes and distinguishes progression from pseudoprogression.

Authors:  Maxwell Lewis Neal; Andrew D Trister; Sunyoung Ahn; Anne Baldock; Carly A Bridge; Laura Guyman; Jordan Lange; Rita Sodt; Tyler Cloke; Albert Lai; Timothy F Cloughesy; Maciej M Mrugala; Jason K Rockhill; Russell C Rockne; Kristin R Swanson
Journal:  Cancer Res       Date:  2013-02-11       Impact factor: 12.701

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.