Literature DB >> 16632619

A four-dimensional computer simulation model of the in vivo response to radiotherapy of glioblastoma multiforme: studies on the effect of clonogenic cell density.

G S Stamatakos1, V P Antipas, N K Uzunoglu, R G Dale.   

Abstract

Tumours behave as complex, self-organizing, opportunistic dynamic systems. In an attempt to better understand and describe the highly complicated tumour behaviour, a novel four-dimensional simulation model of in vivo tumour growth and response to radiotherapy has been developed. This paper presents the latest improvements to the model as well as a parametric validation of it. Improvements include an advanced algorithm leading to conformal tumour shrinkage, a quantitative consideration of the influence of oxygenation on radiosensitivity and a more realistic, imaging based description of the neovasculature distribution. The tumours selected for the validation of the model are a wild type and a mutated p53 gene glioblastomas multiforme. According to the model predictions, a whole tumour with larger cell cycle duration tends to repopulate more slowly. A lower oxygen enhancement ratio value leads to a more radiosensitive whole tumour. Higher clonogenic cell density (CCD) produces a higher number of proliferating tumour cells and, therefore, a more difficult tumour to treat. Simulation predictions agree at least semi-quantitatively with clinical experience, and particularly with the outcome of the Radiation Therapy Oncology Group (RTOG) Study 83-02. It is stressed that the model allows a quantitative study of the interrelationship between the competing influences in a complex, dynamic tumour environment. Therefore, the model can already be useful as an educational tool with which to study, understand and demonstrate the role of various parameters in tumour growth and response to irradiation. A long term quantitative clinical adaptation and validation of the model aiming at its integration into the treatment planning procedure is in progress.

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Year:  2006        PMID: 16632619     DOI: 10.1259/bjr/30604050

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  19 in total

1.  Monte Carlo radiotherapy simulations of accelerated repopulation and reoxygenation for hypoxic head and neck cancer.

Authors:  W M Harriss-Phillips; E Bezak; E K Yeoh
Journal:  Br J Radiol       Date:  2011-10       Impact factor: 3.039

2.  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

3.  Evaluation of current clinical target volume definitions for glioblastoma using cell-based dosimetry stochastic methods.

Authors:  L Moghaddasi; E Bezak; W Harriss-Phillips
Journal:  Br J Radiol       Date:  2015-07-03       Impact factor: 3.039

4.  Mathematical modeling of PDGF-driven glioblastoma reveals optimized radiation dosing schedules.

Authors:  Kevin Leder; Ken Pitter; Quincey LaPlant; Dolores Hambardzumyan; Brian D Ross; Timothy A Chan; Eric C Holland; Franziska Michor
Journal:  Cell       Date:  2014-01-30       Impact factor: 41.582

5.  Optimization of radiation dosing schedules for proneural glioblastoma.

Authors:  H Badri; K Pitter; E C Holland; F Michor; K Leder
Journal:  J Math Biol       Date:  2015-06-21       Impact factor: 2.259

6.  Multi-scale, multi-resolution brain cancer modeling.

Authors:  Le Zhang; L Leon Chen; Thomas S Deisboeck
Journal:  Math Comput Simul       Date:  2009-03-01       Impact factor: 2.463

7.  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

Review 8.  In silico cancer modeling: is it ready for prime time?

Authors:  Thomas S Deisboeck; Le Zhang; Jeongah Yoon; Jose Costa
Journal:  Nat Clin Pract Oncol       Date:  2008-10-14

Review 9.  An imaging-based tumour growth and treatment response model: investigating the effect of tumour oxygenation on radiation therapy response.

Authors:  Benjamin Titz; Robert Jeraj
Journal:  Phys Med Biol       Date:  2008-08-01       Impact factor: 3.609

10.  Introduction of hypermatrix and operator notation into a discrete mathematics simulation model of malignant tumour response to therapeutic schemes in vivo. Some operator properties.

Authors:  Georgios S Stamatakos; Dimitra D Dionysiou
Journal:  Cancer Inform       Date:  2009-10-21
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