Predicting the effectiveness of chemotherapy using stochastic ODE models of tumor growth
Predicting the effectiveness of chemotherapy using stochastic ODE models of tumor growth
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DOI：
10.1016/j.cnsns.2021.105883
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
S. Sharpe;H. Dobrovolny
中科院分区：
文献类型：
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作者：
S. Sharpe;H. Dobrovolny
癌症生长的常微分方程 (ODE) 模型通常用于预测肿瘤生长，并构成个性化医疗中使用的更复杂模型的基础。不幸的是，ODE 模型提供了对细胞群平均行为的预测，忽略了细胞是受离散事件影响的离散对象这一事实。这种随机性可以极大地改变肿瘤生长的时间进程，特别是当细胞群较小时。在这里，我们研究了癌症生长的七种常见 ODE 模型的随机版本，以确定随机性在通过化疗根除肿瘤中的作用。我们发现，随机性导致不同模型对治愈肿瘤所需的化疗水平和治愈所需时间的预测存在差异。我们的结果强调需要更多地研究哪种模型能够最好地描述癌症的生长。
Ordinary differential equation (ODE) models of cancer growth are often used to predict tumor growth and form the basis for more complex models used in personalized medicine. Unfortunately, ODE models provide predictions of the average behavior of the cell population neglecting the fact that cells are discrete objects subject to discrete events. This kind of stochasticity can dramatically change the time course of tumor growth, particularly when the cell population is small. Here, we investigate stochastic versions of seven common ODE models of cancer growth to determine the role of stochasticity in eradicating tumors via chemotherapy. We find that stochasticity leads to differences in predictions among the different models of both the level of chemotherapy needed to cure a tumor and the time it takes to achieve a cure. Our results highlight the need for more investigation of which model provides the best description of cancer growth.