Speaker
Description
Radiotherapy (RT) efficacy in solid tumours is critically shaped by the microvascular environment (MVE); hypoxic niches confer radioresistance, while RT-induced phenotypic selection enriches residual tumours in cancer stem cells (CSCs), driving recurrence. We present a multiscale computational framework integrating three components: (i) a phenotype-structured PDE model for tumour cell dynamics with oxygen-dependent proliferation, necrosis, plasticity, and radiobiological response \cite{celora_spatio-temporal_2023}; (ii) a 3D–1D coupled model for microvascular oxygen transport \cite{possenti_mesoscale_2021}; and (iii) patient-specific vascular networks generated from capillary density data of head and neck cancer cohort \cite{materne_patient-specific_2025}, subjected to vessel damage to simulate RT-induced MVE degradation. A reduced order model based on proper orthogonal decomposition and mesh-informed neural networks enables real-time oxygen field evaluation \vite{vitullo_nonlinear_2024}. Simulating fractionated (FRT) and ultrahypofractionated (UHFRT) protocols on healthy and 50%-pruned networks reveals that vascular architecture—not dose alone—is the primary determinant of outcome. Although hypoxia-induced dedifferentiation shifts the residual tumor composition to radioresistant stem-like states, vascular geometry compromises the efficacy of RT, rendering dose escalation essentially pointless. UHFRT outperforms FRT in well-perfused settings, but both fail under severe MVE damage. These patient-informed results provide mechanistic insights to guide RT planning, paving the way for patient-specific digital twins.
Bibliography
@article{celora_spatio-temporal_2023,
title = {Spatio-temporal modelling of phenotypic heterogeneity in tumour tissues and its impact on radiotherapy treatment},
volume = {556},
issn = {00225193},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0022519322002417},
doi = {10.1016/j.jtbi.2022.111248},
language = {en},
urldate = {2026-03-23},
journal = {Journal of Theoretical Biology},
author = {Celora, Giulia L. and Byrne, Helen M. and Kevrekidis, P.G.},
month = jan,
year = {2023},
pages = {111248},}
@article{possenti_mesoscale_2021,
title = {A {Mesoscale} {Computational} {Model} for {Microvascular} {Oxygen} {Transfer}},
volume = {49},
issn = {0090-6964, 1573-9686},
url = {https://link.springer.com/10.1007/s10439-021-02807-x},
doi = {10.1007/s10439-021-02807-x},
language = {en},
number = {12},
urldate = {2026-03-23},
journal = {Annals of Biomedical Engineering},
author = {Possenti, Luca and Cicchetti, Alessandro and Rosati, Riccardo and Cerroni, Daniele and Costantino, Maria Laura and Rancati, Tiziana and Zunino, Paolo},
month = dec,
year = {2021},
pages = {3356--3373},}
@article{vitullo_nonlinear_2024,
title = {Nonlinear model order reduction for problems with microstructure using mesh informed neural networks},
volume = {229},
issn = {0168874X},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0168874X23001610},
doi = {10.1016/j.finel.2023.104068},
language = {en},
urldate = {2026-03-23},
journal = {Finite Elements in Analysis and Design},
author = {Vitullo, Piermario and Colombo, Alessio and Franco, Nicola Rares and Manzoni, Andrea and Zunino, Paolo},
month = feb,
year = {2024},
pages = {104068},}
@article{materne_patient-specific_2025,
title = {Patient-specific microvascular computational modeling for estimating radiotherapy outcomes},
volume = {190},
issn = {00104825},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482525003658},
doi = {10.1016/j.compbiomed.2025.110014},
language = {en},
urldate = {2026-03-23},
journal = {Computers in Biology and Medicine},
author = {Materne, Sophie and Possenti, Luca and Pisani, Francesco and Vitullo, Piermario and Catalano, Alessandra and Iacovelli, Nicola Alessandro and Franceschini, Marzia and Cavallo, Anna and Cicchetti, Alessandro and Zunino, Paolo and Rancati, Tiziana},
month = may,
year = {2025},
pages = {110014},}
