多形性胶质母细胞瘤增殖和坏死的图像驱动建模。

Q1 Mathematics
Vishal Patel, Leith Hathout
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引用次数: 9

摘要

背景:多形性胶质母细胞瘤患者对治疗反应的异质性表明,最佳治疗方法包括对预期病变进展的个性化评估。在这项工作中,我们为多形性胶质母细胞瘤的增殖和坏死建立了一个新的计算模型。方法:根据每个肿瘤的磁共振成像特征选择模型参数,所提出的技术考虑了固有细胞分裂,肿瘤细胞沿白质束迁移以及中心肿瘤坏死。为了验证这一方法,我们利用多形性胶质母细胞瘤患者的成像参数,在一名健康成年志愿者的大脑中模拟了肿瘤的生长。在模拟的肿瘤轮廓和观察到的肿瘤之间计算互信息度量。结果:将本文模型生成的肿瘤进展曲线与现有模型生成的肿瘤进展曲线以及实际观察到的肿瘤进展曲线进行了比较。定性和定量分析都表明,与先前的技术相比,本研究中引入的模型更准确地复制了胶质母细胞瘤的观察进展。结论:这种图像驱动的模型可以改善肿瘤进展情况,并可能有助于开发更可靠的多形性胶质母细胞瘤患者预后评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Image-driven modeling of the proliferation and necrosis of glioblastoma multiforme.

Image-driven modeling of the proliferation and necrosis of glioblastoma multiforme.

Image-driven modeling of the proliferation and necrosis of glioblastoma multiforme.

Image-driven modeling of the proliferation and necrosis of glioblastoma multiforme.

Background: The heterogeneity of response to treatment in patients with glioblastoma multiforme suggests that the optimal therapeutic approach incorporates an individualized assessment of expected lesion progression. In this work, we develop a novel computational model for the proliferation and necrosis of glioblastoma multiforme.

Methods: The model parameters are selected based on the magnetic resonance imaging features of each tumor, and the proposed technique accounts for intrinsic cell division, tumor cell migration along white matter tracts, as well as central tumor necrosis. As a validation of this approach, tumor growth is simulated in the brain of a healthy adult volunteer using parameters derived from the imaging of a patient with glioblastoma multiforme. A mutual information metric is calculated between the simulated tumor profile and observed tumor.

Results: The tumor progression profile generated by the proposed model is compared with those produced by existing models and with the actual observed tumor progression. Both qualitative and quantitative analyses show that the model introduced in this work replicates the observed progression of glioblastoma more accurately relative to prior techniques.

Conclusions: This image-driven model generates improved tumor progression profiles and may contribute to the development of more reliable prognostic estimates in patients with glioblastoma multiforme.

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来源期刊
Theoretical Biology and Medical Modelling
Theoretical Biology and Medical Modelling MATHEMATICAL & COMPUTATIONAL BIOLOGY-
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: Theoretical Biology and Medical Modelling is an open access peer-reviewed journal adopting a broad definition of "biology" and focusing on theoretical ideas and models associated with developments in biology and medicine. Mathematicians, biologists and clinicians of various specialisms, philosophers and historians of science are all contributing to the emergence of novel concepts in an age of systems biology, bioinformatics and computer modelling. This is the field in which Theoretical Biology and Medical Modelling operates. We welcome submissions that are technically sound and offering either improved understanding in biology and medicine or progress in theory or method.
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