Phan Tin, Bennett Justin, Patten Taylor
Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87544, USA.
School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85281, USA.
Life (Basel). 2023 Feb 1;13(2):410. doi: 10.3390/life13020410.
Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual's characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability.
数学模型是癌症理论基础的核心组成部分，并且已发展成为精准医学中的临床工具。针对临床应用的建模研究通常假定个体特征可以表示为模型中的参数，并用于解释、预测和优化治疗结果。然而，这种方法依赖于基础数学模型的可识别性。在本研究中，我们基于观测系统模拟实验的框架，研究几种癌症生长模型的可识别性，重点关注每个模型的预后参数。我们的结果表明，数据收集的频率、数据类型（如癌症替代指标）以及测量的准确性在确定模型的可识别性方面都起着关键作用。我们还发现，高精度数据可以对某些参数进行合理准确的估计，这可能是在实践中实现模型可识别性的关键。由于更复杂的模型需要更多数据来进行识别，我们的结果支持在临床环境中使用具有清晰机制来跟踪疾病进展的模型这一观点。对于这样的模型，与疾病进展相关的模型参数子集自然会使模型可识别性所需的数据量最小化。
