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.
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.
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
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
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
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
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
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
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
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
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