Predictability and identifiability assessment of models for prostate cancer under androgen suppression therapy.
Predictability and identifiability assessment of models for prostate cancer under androgen suppression therapy.
复制标题
雄激素抑制治疗下前列腺癌模型的可预测性和可识别性评估。
DOI：
10.3934/mbe.2019176
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
E. Kostelich
中科院分区：
文献类型：
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作者：
Zhimin Wu;Tin Phan;Javier Baez;Y. Kuang;E. Kostelich
在过去的二十年里，已经看到了许多数学模型的发展，以研究前列腺癌在临床环境中的各个方面。这些模型通常包含大量参数，并依赖于有限的数据集进行验证。在治疗中的前列腺癌的动态的定量分析可能会受到阻碍，从现有的数据，这限制了模型的预测能力的参数的可识别性的缺乏。以三个常微分方程模型为例，对模型参数的可辨识性和不确定性量化进行了数值研究。在大多数情况下，这些参数无法从前列腺特异性抗原的时间序列中识别，前列腺特异性抗原被用作肿瘤进展的临床代表。可能无法对不可识别的参数定义有限的置信界限，并且在某些情况下，即使可识别的参数的相对不确定性也可能很大。Fisher信息矩阵可用于确定给定模型的可识别参数子集。如果有生物限制和其他类型的测量方法可用，则使用这些方法可减少这些不确定性。尽管在估计“患者特异性”参数时必须小心，但Enhancement Kalman滤波可以从不完美模型中提供临床有用的患者结局短期预测。我们的研究结果表明，参数可识别性的重要性，在验证和预测能力的数学模型的前列腺肿瘤治疗。观测系统的模拟实验，广泛应用于气象学，可能会被证明是有用的生物数学模型的发展，旨在为未来的临床应用。
The past two decades have seen the development of numerous mathematical models to study various aspects of prostate cancer in clinical settings. These models often contain large sets of parameters and rely on limited data sets for validation. The quantitative analysis of the dynamics of prostate cancer under treatment may be hindered by the lack of identifiability of the parameters from the available data, which limits the predictive ability of the model. Using three ordinary differential equation models as case studies, we carry out a numerical investigation of the identifiability and uncer- tainty quantification of the model parameters. In most cases, the parameters are not identifiable from time series of prostate-specific antigen, which is used as a clinical proxy for tumor progression. It may not be possible to define a finite confidence bound on an unidentifiable parameter, and the relative uncertainties in even identifiable parameters may be large in some cases. The Fisher information ma- trix may be used to determine identifiable parameter subsets for a given model. The use of biological constraints and additional types of measurements, should they become available, may reduce these uncertainties. Ensemble Kalman filtering may provide clinically useful, short-term predictions of pa- tient outcomes from imperfect models, though care must be taken when estimating "patient-specific" parameters. Our results demonstrate the importance of parameter identifiability in the validation and predictive ability of mathematical models of prostate tumor treatment. Observing-system simulation experiments, widely used in meteorology, may prove useful in the development of biomathematical models intended for future clinical application.