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A mathematical model describes the malignant transformation of low grade gliomas: Prognostic implications

Author

Listed:
  • Magdalena U Bogdańska
  • Marek Bodnar
  • Monika J Piotrowska
  • Michael Murek
  • Philippe Schucht
  • Jürgen Beck
  • Alicia Martínez-González
  • Víctor M Pérez-García

Abstract

Gliomas are the most frequent type of primary brain tumours. Low grade gliomas (LGGs, WHO grade II gliomas) may grow very slowly for the long periods of time, however they inevitably cause death due to the phenomenon known as the malignant transformation. This refers to the transition of LGGs to more aggressive forms of high grade gliomas (HGGs, WHO grade III and IV gliomas). In this paper we propose a mathematical model describing the spatio-temporal transition of LGGs into HGGs. Our modelling approach is based on two cellular populations with transitions between them being driven by the tumour microenvironment transformation occurring when the tumour cell density grows beyond a critical level. We show that the proposed model describes real patient data well. We discuss the relationship between patient prognosis and model parameters. We approximate tumour radius and velocity before malignant transformation as well as estimate the onset of this process.

Suggested Citation

  • Magdalena U Bogdańska & Marek Bodnar & Monika J Piotrowska & Michael Murek & Philippe Schucht & Jürgen Beck & Alicia Martínez-González & Víctor M Pérez-García, 2017. "A mathematical model describes the malignant transformation of low grade gliomas: Prognostic implications," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-24, August.
    • Handle: RePEc:plo:pone00:0179999
    DOI: 10.1371/journal.pone.0179999
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    Cited by:

    1. José Gerardo Suárez-García & Javier Miguel Hernández-López & Eduardo Moreno-Barbosa & Benito de Celis-Alonso, 2020. "A simple model for glioma grading based on texture analysis applied to conventional brain MRI," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-19, May.

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