Abstract
Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient’s course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations.
Original language | English (US) |
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Article number | 19 |
Journal | Bulletin of Mathematical Biology |
Volume | 86 |
Issue number | 2 |
DOIs | |
State | Published - Feb 2024 |
Keywords
- Head-and-neck cancer
- Heterogeneity
- Patient variability
- Predictive model
- Radiotherapy
- Uncertainty
ASJC Scopus subject areas
- General Neuroscience
- Immunology
- General Mathematics
- General Biochemistry, Genetics and Molecular Biology
- General Environmental Science
- Pharmacology
- General Agricultural and Biological Sciences
- Computational Theory and Mathematics