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Bulletin of Mathematical Biology

, Volume 79, Issue 4, pp 939–974 | Cite as

Bayesian Calibration, Validation and Uncertainty Quantification for Predictive Modelling of Tumour Growth: A Tutorial

  • Joe Collis
  • Anthony J. Connor
  • Marcin Paczkowski
  • Pavitra Kannan
  • Joe Pitt-Francis
  • Helen M. Byrne
  • Matthew E. Hubbard
Research Methods Article

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 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.

Keywords

Bayesian calibration Tumour growth Model validation 

Notes

Acknowledgements

J. Collis and M. E. Hubbard acknowledge the support of EPSRC Grant Number EP/K039342/1. This project has received funding from the European Unions Seventh Framework Programme for research, technological development and demonstration under Grant Agreement No. 600841.

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Copyright information

© Society for Mathematical Biology 2017

Authors and Affiliations

  1. 1.School of Mathematical SciencesUniversity of NottinghamNottinghamUK
  2. 2.Mathematical InstituteUniversity of OxfordOxfordUK
  3. 3.Gray Institute for Radiation Oncology and BiologyUniversity of OxfordOxfordUK
  4. 4.Department of Computer ScienceUniversity of OxfordOxfordUK

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