Bogdańska Magdalena U, Bodnar Marek, Piotrowska Monika J, Murek Michael, Schucht Philippe, Beck Jürgen, Martínez-González Alicia, Pérez-García Víctor M
Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland.
Departamento de Matemáticas, Universidad de Castilla-La Mancha, Ciudad Real, Spain.
PLoS One. 2017 Aug 1;12(8):e0179999. doi: 10.1371/journal.pone.0179999. eCollection 2017.
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.
神经胶质瘤是最常见的原发性脑肿瘤类型。低级别神经胶质瘤(LGG,世界卫生组织二级神经胶质瘤)可能会在很长一段时间内生长得非常缓慢,然而,由于所谓的恶性转化现象,它们不可避免地会导致死亡。这指的是LGG向更具侵袭性的高级别神经胶质瘤(HGG,世界卫生组织三级和四级神经胶质瘤)形式的转变。在本文中,我们提出了一个描述LGG向HGG时空转变的数学模型。我们的建模方法基于两个细胞群体,它们之间的转变由肿瘤细胞密度增长超过临界水平时发生的肿瘤微环境转变驱动。我们表明,所提出的模型能够很好地描述真实患者数据。我们讨论了患者预后与模型参数之间的关系。我们估算了恶性转化前的肿瘤半径和速度,以及估计了这一过程的起始时间。