Tracking glioblastoma progression after initial resection with minimal reaction-diffusion models.

Tracking glioblastoma progression after initial resection with minimal reaction-diffusion models.
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DOI:
10.3934/mbe.2022256
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发表时间:
2022-03
期刊:
Mathematical biosciences and engineering : MBE
影响因子:
--
通讯作者:
Duane C. Harris;G. Mignucci-Jiménez;Yuan Xu;S. Eikenberry;C. Quarles;M. Preul;Y. Kuang;E. Kostelich
Duane C. Harris;G. Mignucci-Jiménez;Yuan Xu;S. Eikenberry;C. Quarles;M. Preul;Y. Kuang;E. Kostelich
中科院分区:
其他
文献类型:
--
作者:
Duane C. Harris;G. Mignucci-Jiménez;Yuan Xu;S. Eikenberry;C. Quarles;M. Preul;Y. Kuang;E. Kostelich

文献摘要

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我们描述了一个初步的努力,以模型的生长和进展的多形性胶质母细胞瘤,原发性脑癌的侵略性形式,在接受治疗的肿瘤复发后,初步手术和放化疗的患者。使用两个反应扩散模型:Fisher-Kolmogorov方程和作者开发的2-种群模型,该模型将肿瘤分为活跃增殖和静止(或坏死)细胞。这些模型是在由巴罗神经研究所提供的磁共振成像(MRI)扫描得到的三维大脑几何形状上模拟的。该研究由10名患者的17个临床时间间隔组成,这些患者均接受了详细随访,每个患者在连续随访扫描的1至3个月内均显示出肿瘤的显著进展。实施田口抽样设计以使用144种不同的模型参数选择来估计预测肿瘤的变异性。在9种情况下,可以识别模型参数,使得使用两种模型模拟的肿瘤包含观察到的肿瘤体积的至少40%。我们讨论了一些潜在的改进,可以对模型的参数化及其初始化。
We describe a preliminary effort to model the growth and progression of glioblastoma multiforme, an aggressive form of primary brain cancer, in patients undergoing treatment for recurrence of tumor following initial surgery and chemoradiation. Two reaction-diffusion models are used: the Fisher-Kolmogorov equation and a 2-population model, developed by the authors, that divides the tumor into actively proliferating and quiescent (or necrotic) cells. The models are simulated on 3-dimensional brain geometries derived from magnetic resonance imaging (MRI) scans provided by the Barrow Neurological Institute. The study consists of 17 clinical time intervals across 10 patients that have been followed in detail, each of whom shows significant progression of tumor over a period of 1 to 3 months on sequential follow up scans. A Taguchi sampling design is implemented to estimate the variability of the predicted tumors to using 144 different choices of model parameters. In 9 cases, model parameters can be identified such that the simulated tumor, using both models, contains at least 40 percent of the volume of the observed tumor. We discuss some potential improvements that can be made to the parameterizations of the models and their initialization.