Bayesian calibration, validation and uncertainty quantification for predictive modelling of tumour growth: a tutorial

Collis, Joe and Connor, Anthony J. and Paczkowski, Marcin and Kannan, Pavitra and Pitt-Francis, Joe and Byrne, Helen M. and Hubbard, Matthew E. (2017) Bayesian calibration, validation and uncertainty quantification for predictive modelling of tumour growth: a tutorial. Bulletin of Mathematical Biology, 79 (4). pp. 939-974. ISSN 1522-9602

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Abstract

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 is 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.

Item Type: Article
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/s11538-017-0258-5
Keywords: Bayesian Calibration, Tumour Growth, Model Validation
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
Identification Number: 10.1007/s11538-017-0258-5
Depositing User: Hubbard, Matthew
Date Deposited: 24 Feb 2017 12:18
Last Modified: 26 Apr 2017 23:17
URI: http://eprints.nottingham.ac.uk/id/eprint/40823

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