Predicting radiotherapy patient outcomes with real-time clinical data using mathematical modelling

TL;DR

This study develops a simple yet effective mathematical model combined with a Bayesian approach to predict tumour response in head-and-neck cancer radiotherapy, accounting for patient variability and data sparsity.

Contribution

The paper introduces a novel compartment model and statistical methodology that improve prediction of tumour progression using limited clinical data.

Findings

Model accurately predicts tumour volume progression.

Incorporates patient-specific variability.

Demonstrates predictive limitations with unseen data.

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.