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

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 statis

SUBMITTER: Browning AP 

PROVIDER: S-EPMC10796515 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature