Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data.
Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data.
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DOI：
10.1088/1478-3975/12/4/046006
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发表时间：
2015-06-04
期刊：
影响因子：
2
通讯作者：
Yankeelov TE
中科院分区：
文献类型：
--
作者：
Hormuth DA 2nd;Weis JA;Barnes SL;Miga MI;Rericha EC;Quaranta V;Yankeelov TE
反应扩散模型已被广泛用于模拟胶质瘤生长。然而，尚未显示该模型可以使用模型参数（即，肿瘤细胞扩散和增殖）。为此，我们使用计算机模拟研究来开发准确估计肿瘤特异性反应扩散模型参数所需的方法，然后测试这些参数预测未来生长的准确性。然后在胶质瘤生长的鼠模型中进行类似的研究。根据反应扩散方程，使用“生长”10天的计算机模拟肿瘤测试参数估计方法。从早期时间点估计参数，并用于预测随后的生长。在全球（总体积和骰子值）和当地（一致性相关系数，CCC）水平上评估预测准确性。在计算机模拟研究的指导下，用扩散加权磁共振成像（DW-MRI）成像的患有C6胶质瘤的大鼠（n=9）用于评价模型预测体内肿瘤生长的准确性。计算机模拟研究导致在未来六天内预测的全局（肿瘤体积误差<8.8%，Dice > 0.92）和局部（CCC值> 0.80）水平误差较低。体内研究显示在相同的时间段内较高的全局（肿瘤体积误差> 11.7%，Dice < 0.81）和较高的局部（CCC < 0.33）水平误差。计算机模拟研究表明，模型参数可以准确估计，并用于准确预测全球和局部规模的未来肿瘤生长。然而，在实验研究中的预测准确性差表明反应扩散方程是体内C6胶质瘤生物学的不完整描述，并且可能需要进一步建模肿瘤内相互作用，包括（例如）增殖和坏死区域的分割。
Reaction-diffusion models have been widely used to model glioma growth. However, it has not been shown how accurate this model can predict future tumor status using model parameters (i.e., tumor cell diffusion and proliferation) estimated from quantitative in vivo imaging data. Towards this end, we used in silico studies to develop the methods needed to accurately estimate tumor specific reaction-diffusion model parameters, and then tested the accuracy with which these parameters can predict future growth. The analogous study was then performed in a murine model of glioma growth. The parameter estimation approach was tested using an in silico tumor “grown” for ten days as dictated by the reaction-diffusion equation. Parameters were estimated from early time points and used to predict subsequent growth. Prediction accuracy was assessed at global (total volume and Dice value) and local (concordance correlation coefficient, CCC) levels. Guided by the in silico study, rats (n=9) with C6 gliomas, imaged with diffusion weighted magnetic resonance imaging (DW-MRI), were used to evaluate the model’s accuracy for predicting in vivo tumor growth. The in silico study resulted in low global (tumor volume error < 8.8 %, Dice > 0.92) and local (CCC values > 0.80) level errors for predictions up to six days into the future. The in vivo study showed higher global (tumor volume error > 11.7%, Dice < 0.81) and higher local (CCC < 0.33) level errors over the same time period. The in silico study shows that model parameters can be accurately estimated and used to accurately predict future tumor growth at both the global and local scale. However, the poor predictive accuracy in the experimental study suggests the reaction-diffusion equation is an incomplete description of in vivo C6 glioma biology and may require further modeling of intra-tumor interactions including segmentation of (for example) proliferative and necrotic regions.
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影响因子：
3.3
作者：
Atuegwu, Nkiruka C.;Arlinghaus, Lori R.;Li, Xia;BrianWelch, E.;Chakravarthy, Bapsi A.;Gore, John C.;Yankeelov, Thomas E.
通讯作者：
Yankeelov, Thomas E.
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作者：
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作者：
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影响因子：
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作者：
Iliadis G;Kotoula V;Chatzisotiriou A;Televantou D;Eleftheraki AG;Lambaki S;Misailidou D;Selviaridis P;Fountzilas G
通讯作者：
Fountzilas G
影响因子：
46.9
作者：
Helmlinger, G;Netti, PA;Jain, RK
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Jain, RK