Bayesian Calibration, Validation and Uncertainty Quantification for Predictive Modelling of Tumour Growth: A Tutorial
Bayesian Calibration, Validation and Uncertainty Quantification for Predictive Modelling of Tumour Growth: A Tutorial
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DOI：
10.1007/s11538-017-0258-5
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发表时间：
2017-04-01
影响因子：
3.5
通讯作者：
Hubbard, Matthew E.
中科院分区：
文献类型：
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作者：
Collis, Joe;Connor, Anthony J.;Hubbard, Matthew E.
在这项工作中，我们提出了一个教学肿瘤生长的例子，其中我们将校准和验证技术应用于肿瘤球体生长的不确定冈伯兹模型。本文的主要贡献是在一个简单模型的背景下讨论和应用这些方法（这些方法在癌症建模领域不常用），其确定性模拟在社区内广为人知。在示例的过程中，我们根据可能存在测量误差的实验数据校准模型，然后验证由此产生的不确定模型预测。然后，我们分析了模型预测对潜在测量模型的敏感性。最后，我们提出了一种基本的学习方法来调整验证过程中的阈值参数，以最大限度地提高我们验证模型的预测精度。
In this work, we present a pedagogical tumour growth example, in which we apply calibration and validation techniques to an uncertain, Gompertzian model of tumour spheroid growth. The key contribution of this article is the discussion and application of these methods (that are not commonly employed in the field of cancer modelling) in the context of a simple model, whose deterministic analogue is widely known within the community. In the course of the example, we calibrate the model against experimental data that are subject to measurement errors, and then validate the resulting uncertain model predictions. We then analyse the sensitivity of the model predictions to the underlying measurement model. Finally, we propose an elementary learning approach for tuning a threshold parameter in the validation procedure in order to maximize predictive accuracy of our validated model.