Differences in predictions of ODE models of tumor growth: a cautionary example.
Differences in predictions of ODE models of tumor growth: a cautionary example.
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DOI：
10.1186/s12885-016-2164-x
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发表时间：
2016-02-26
期刊：
影响因子：
3.8
通讯作者：
Dobrovolny HM
中科院分区：
文献类型：
--
作者：
Murphy H;Jaafari H;Dobrovolny HM
虽然数学模型通常用于预测癌症的进展和治疗结果，但如何最好地模拟肿瘤生长仍然存在不确定性。已经提出了七种肿瘤生长的常微分方程（ODE）模型（指数，Mendelsohn，logistic，线性，表面，Gompertz和Bertalanffy），但对于如何为特定癌症选择最合适的模型没有明确的指导。我们研究了所有七个先前提出的ODE模型在化疗的存在和不存在。我们推导出了最大肿瘤大小、倍增时间和抑制肿瘤所需的最小化疗量的方程，并使用样本数据集比较了这些量如何根据生长模型的选择而有所不同。我们发现，根据使用的生长模型，预测倍增时间存在12倍差异，预测抑制所需化疗量存在6倍差异。我们的研究结果强调了在开发用于癌症治疗计划的数学模型时，需要仔细考虑模型假设。
While mathematical models are often used to predict progression of cancer and treatment outcomes, there is still uncertainty over how to best model tumor growth. Seven ordinary differential equation (ODE) models of tumor growth (exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy) have been proposed, but there is no clear guidance on how to choose the most appropriate model for a particular cancer. We examined all seven of the previously proposed ODE models in the presence and absence of chemotherapy. We derived equations for the maximum tumor size, doubling time, and the minimum amount of chemotherapy needed to suppress the tumor and used a sample data set to compare how these quantities differ based on choice of growth model. We find that there is a 12-fold difference in predicting doubling times and a 6-fold difference in the predicted amount of chemotherapy needed for suppression depending on which growth model was used. Our results highlight the need for careful consideration of model assumptions when developing mathematical models for use in cancer treatment planning.
影响因子：
8.8
作者：
Brenner, M W;Holsti, L R;Perttala, Y
通讯作者：
Perttala, Y