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

A mathematical model describes the malignant transformation of low grade gliomas: Prognostic implications.
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数学模型描述了低级别神经胶质瘤的恶性转化:预后意义。

DOI:
10.1371/journal.pone.0179999
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发表时间:
2017
期刊:
影响因子:
3.7
通讯作者:
Pérez-García VM
Pérez-García VM
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Bogdańska MU;Bodnar M;Piotrowska MJ;Murek M;Schucht P;Beck J;Martínez-González A;Pérez-García VM

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神经胶质瘤是最常见的原发性脑肿瘤类型。低级别胶质瘤(LGG,WHO II 级胶质瘤)可能会在很长一段时间内生长非常缓慢,但由于恶性转化的现象,它们不可避免地会导致死亡。这是指 LGG 转变为更具侵袭性的高级神经胶质瘤(HGG、WHO III 级和 IV 级神经胶质瘤)。在本文中,我们提出了一个描述 LGG 到 HGG 的时空转变的数学模型。我们的建模方法基于两个细胞群,它们之间的转变是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的。我们表明所提出的模型很好地描述了真实的患者数据。我们讨论患者预后与模型参数之间的关系。我们估算恶变之前的肿瘤半径和速度,并估计该过程的开始。
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.
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