Bayesian calibration, validation and uncertainty quantification for predictive modelling of tumour growth: a tutorial
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 i...
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| Format: | Article |
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Springer
2017
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| Online Access: | https://eprints.nottingham.ac.uk/40823/ |
| _version_ | 1848796140927975424 |
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| author | Collis, Joe Connor, Anthony J. Paczkowski, Marcin Kannan, Pavitra Pitt-Francis, Joe Byrne, Helen M. Hubbard, Matthew E. |
| author_facet | Collis, Joe Connor, Anthony J. Paczkowski, Marcin Kannan, Pavitra Pitt-Francis, Joe Byrne, Helen M. Hubbard, Matthew E. |
| author_sort | Collis, Joe |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:43:15Z |
| format | Article |
| id | nottingham-40823 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:43:15Z |
| publishDate | 2017 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-408232020-05-04T19:57:55Z https://eprints.nottingham.ac.uk/40823/ 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. 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. Springer 2017-04 Article PeerReviewed Collis, Joe, Connor, Anthony J., Paczkowski, Marcin, Kannan, Pavitra, Pitt-Francis, Joe, 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 Bayesian Calibration Tumour Growth Model Validation https://link.springer.com/article/10.1007%2Fs11538-017-0258-5 doi:10.1007/s11538-017-0258-5 doi:10.1007/s11538-017-0258-5 |
| spellingShingle | Bayesian Calibration Tumour Growth Model Validation Collis, Joe Connor, Anthony J. Paczkowski, Marcin Kannan, Pavitra Pitt-Francis, Joe Byrne, Helen M. Hubbard, Matthew E. Bayesian calibration, validation and uncertainty quantification for predictive modelling of tumour growth: a tutorial |
| title | Bayesian calibration, validation and uncertainty quantification for predictive modelling of tumour growth: a tutorial |
| title_full | Bayesian calibration, validation and uncertainty quantification for predictive modelling of tumour growth: a tutorial |
| title_fullStr | Bayesian calibration, validation and uncertainty quantification for predictive modelling of tumour growth: a tutorial |
| title_full_unstemmed | Bayesian calibration, validation and uncertainty quantification for predictive modelling of tumour growth: a tutorial |
| title_short | Bayesian calibration, validation and uncertainty quantification for predictive modelling of tumour growth: a tutorial |
| title_sort | bayesian calibration, validation and uncertainty quantification for predictive modelling of tumour growth: a tutorial |
| topic | Bayesian Calibration Tumour Growth Model Validation |
| url | https://eprints.nottingham.ac.uk/40823/ https://eprints.nottingham.ac.uk/40823/ https://eprints.nottingham.ac.uk/40823/ |