Predicting Radiotherapy Patient Outcomes with Real-Time Clinical Data Using Mathematical Modelling

Authors

Alexander P Browning
Thomas D Lewin
Ruth E Baker
Philip K Maini
Eduardo G Moros
Jimmy Caudell
Helen M Byrne
Heiko Enderling

Publication Date

1-18-2024

Journal

Bulletin of Mathematical Biology

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.

Keywords

Humans, Bayes Theorem, Models, Biological, Mathematical Concepts, Models, Theoretical, Neoplasms

Comments

This article has been corrected. See Bull Math Biol. 2024 February 28; 86(4): 35.

Supplementary Materials

PMID: 38238433