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

Collis, Joe; Connor, Anthony J.; Paczkowski, Marcin; Kannan, Pavitra; Pitt-Francis, Joe; Byrne, Helen M.; Hubbard, Matthew E.

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Authors

Joe Collis

Anthony J. Connor

Marcin Paczkowski

Pavitra Kannan

Joe Pitt-Francis

Helen M. Byrne

MATTHEW HUBBARD MATTHEW.HUBBARD@NOTTINGHAM.AC.UK
Professor of Computational and Applied Mathematics



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.

Journal Article Type Article
Acceptance Date Feb 5, 2017
Online Publication Date Mar 13, 2017
Publication Date Apr 1, 2017
Deposit Date Feb 24, 2017
Publicly Available Date Mar 13, 2017
Journal Bulletin of Mathematical Biology
Print ISSN 0092-8240
Electronic ISSN 1522-9602
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 79
Issue 4
Pages 939-974
DOI https://doi.org/10.1007/s11538-017-0258-5
Keywords Bayesian Calibration, Tumour Growth, Model Validation
Public URL https://nottingham-repository.worktribe.com/output/969875
Publisher URL https://link.springer.com/article/10.1007%2Fs11538-017-0258-5
Additional Information The final publication is available at Springer via http://dx.doi.org/10.1007/s11538-017-0258-5

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