Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic response.
Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic response.
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DOI：
10.1038/sj.bjc.6605773
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发表时间：
2010-08-10
影响因子：
8.8
通讯作者：
中科院分区：
文献类型：
--
作者：
动态对比增强磁共振成像（DCE-MRI）包含有关肿瘤异质性和降低药物疗效的运输限制的关键信息。基于未充分利用的DCE-MRI数据的药物递送和细胞反应性的数学建模具有预测个体患者的治疗反应性的独特潜力。为了解释DCE-MRI数据，我们创建了一个建模框架，该框架在多个时间和长度尺度上运行，并结合了细胞内代谢，营养和药物扩散，跨血管渗透性和血管生成。计算方法被用来分析从八个乳腺癌患者在马萨诸塞州斯普林菲尔德的Baystate医疗中心收集的DCE-MR图像。计算机模拟表明，跨血管运输与肿瘤的侵袭性相关，因为增加的血管生长和渗透性为细胞增殖提供了更多的营养。模型模拟还表明，血管密度对组织生长和药物反应的影响最小，营养物质的可用性促进生长。最后，模拟表明，增加的运输异质性与增加的肿瘤生长和药物反应差。基于DCE-MRI的数学建模有可能帮助治疗决策并改善整体癌症护理。该模型是创建全面和预测性计算方法的关键第一步。
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contains crucial information about tumour heterogeneity and the transport limitations that reduce drug efficacy. Mathematical modelling of drug delivery and cellular responsiveness based on underutilised DCE-MRI data has the unique potential to predict therapeutic responsiveness for individual patients. To interpret DCE-MRI data, we created a modelling framework that operates over multiple time and length scales and incorporates intracellular metabolism, nutrient and drug diffusion, trans-vascular permeability, and angiogenesis. The computational methodology was used to analyse DCE-MR images collected from eight breast cancer patients at Baystate Medical Center in Springfield, MA. Computer simulations showed that trans-vascular transport was correlated with tumour aggressiveness because increased vessel growth and permeability provided more nutrients for cell proliferation. Model simulations also indicate that vessel density minimally affects tissue growth and drug response, and nutrient availability promotes growth. Finally, the simulations indicate that increased transport heterogeneity is coupled with increased tumour growth and poor drug response. Mathematical modelling based on DCE-MRI has the potential to aid treatment decisions and improve overall cancer care. This model is the critical first step in the creation of a comprehensive and predictive computational method.
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影响因子：
2.9
作者：
Hayes, C;Padhani, AR;Leach, MO
通讯作者：
Leach, MO
影响因子：
5.5
作者：
Krogh, A
通讯作者：
Krogh, A
影响因子：
3.4
作者：
GROEBE, K
通讯作者：
GROEBE, K
影响因子：
--
作者：
Hsiang, David J.;Yatnamoto, Maki;Butler, John A.
通讯作者：
Butler, John A.
影响因子：
45.3
作者：
GIANNI, L;KEARNS, CM;EGORIN, MJ
通讯作者：
EGORIN, MJ