The integration of quantitative multi-modality imaging data into mathematical models of tumors.
The integration of quantitative multi-modality imaging data into mathematical models of tumors.
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DOI：
10.1088/0031-9155/55/9/001
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发表时间：
2010-05-07
影响因子：
3.5
通讯作者：
Yankeelov TE
中科院分区：
文献类型：
--
作者：
Atuegwu NC;Gore JC;Yankeelov TE
从多种模态获得的定量成像数据可以整合到肿瘤生长和治疗反应的数学模型中，以实现实际预测价值的其他见解。坏死的核心。根据模拟的细胞毒性治疗的效率，我们表明肿瘤可能会继续扩展，或者新型模型包括肿瘤细胞运动的驱动力对于这些模型，基于与肿瘤细胞数量相关的成像数据（根据扩散加权MRI估计），凋亡（从99mtc-annexin-v Spect），细胞增殖和缺氧（来自PET）。模态成像数据中的数学模型是肿瘤生长的一种有望组合，可以捕获肿瘤生长和治疗反应的显着特征，这表明了其他研究的方向。
Quantitative imaging data obtained from multiple modalities may be integrated into mathematical models of tumor growth and treatment response to achieve additional insights of practical predictive value. We show how this approach can describe the development of tumors that appear realistic in terms of producing proliferating tumor rims and necrotic cores. Two established models (the logistic model with and without the effects of treatment) and one novel model built a priori from available imaging data have been studied. We modify the logistic model to predict the spatial expansion of a tumor driven by tumor cell migration after a voxel’s carrying capacity has been reached. Depending on the efficacy of a simulated cytoxic treatment, we show that the tumor may either continue to expand, or contract. The novel model includes hypoxia as a driver of tumor cell movement. The starting conditions for these models are based on imaging data related to the tumor cell number (as estimated from diffusion-weighted MRI), apoptosis (from 99mTc-Annexin-V SPECT), cell proliferation and hypoxia (from PET). We conclude that integrating multi-modality imaging data into mathematical models of tumor growth is a promising combination that can capture the salient features of tumor growth and treatment response and this indicates the direction for additional research.
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影响因子：
8.4
作者：
Eisenhauer, E. A.;Therasse, P.;Verweij, J.
通讯作者：
Verweij, J.
影响因子：
2.1
作者：
Kiran, Kanchi Lakshmi;Jayachandran, Devaraj;Lakshminarayanan, S.
通讯作者：
Lakshminarayanan, S.
影响因子：
3.3
作者：
EIS, M;ELS, T;HOEHNBERLAGE, M
通讯作者：
HOEHNBERLAGE, M
影响因子：
8.5
作者：
Swanson, KR;Alvord, EC;Murray, JD
通讯作者：
Murray, JD
影响因子：
4.7
作者：
Busini, Valentina;Arosio, Paolo;Masi, Maurizio
通讯作者：
Masi, Maurizio