Speaker
Description
Tumor hypoxia drives radiotherapy failure, as reduced oxygenation lowers radiosensitivity and promotes resistant phenotypes. We propose a phenotype-structured partial differential equation model to study fractionation by varying dose–interval pairs under oxygen-limited conditions, extending \cite{chiari2023}. The framework couples tumor cell density and oxygen dynamics through a continuous trait encoding resistance to hypoxia and radiation.
Radiotherapy is described by a trait-dependent linear–quadratic model in which radiosensitivity is modulated by an oxygen enhancement ratio depending on the spatial oxygen field and evolving phenotypic distribution. Schedules are constrained by a normal-tissue biologically effective dose and evaluated by time to progression.
Exploration of the dose–interval space shows that oxygenation reshapes protocol ranking. Near normoxia, schedules yield comparable outcomes, whereas hypoxia induces distinct regimes. Severe hypoxia disrupts the classical dose–interval trade-off and may favor either hyperfractionated or hypofractionated strategies. Moreover, spatial heterogeneity in oxygen distribution, even at fixed total supply, alters protocol ranking relative to the spatially homogeneous case.
These results support spatially and phenotypically structured models for schedule selection in hypoxic tumors.
Bibliography
@article{chiari2023,
author = {Chiari, Giulia and Fiandaca, G. and Delitala, Marcello E.},
title = {Hypoxia-related radiotherapy resistance in tumors: treatment efficacy investigation in an eco-evolutionary perspective},
journal = {Frontiers in Applied Mathematics and Statistics},
year = {2023},
volume = {9},
pages = {1193191},
doi = {10.3389/fams.2023.1193191}
}
