Identifiability and Model Selection Frameworks for Models of High-Grade Glioma Response to Chemoradiation

Authors

Khushi C Hiremath
Kenan Atakishi
Ernesto A B F Lima
Maguy Farhat
Bikash Panthi
Holly Langshaw
Mihir D Shanker
Wasif Talpur
Sara Thrower
Jodi Goldman
Caroline Chung
Thomas E Yankeelov
David A Hormuth Ii

Publication Date

4-2-2025

Journal

Philosophical Transactions of the Royal Society A

DOI

10.1098/rsta.2024.0212

PMID

40172557

PMCID

PMC12105797

PubMedCentral® Posted Date

4-2-2025

PubMedCentral® Full Text Version

Post-print

Abstract

We have developed a family of biology-based mathematical models of high-grade glioma (HGG), capturing the key features of tumour growth and response to chemoradiation. We now seek to quantify the accuracy of parameter estimation and determine, when given a virtual patient cohort, which model was used to generate the tumours. In this way, we systematically test both the parameter and model identifiability. Virtual patients are generated from unique growth parameters whose growth dynamics are determined by the model family. We then assessed the ability to recover model parameters and select the model used to generate the tumour. We then evaluated the accuracy of predictions using the selected model at four weeks post-chemoradiation. We observed median parameter errors from 0.04% to 72.96%. Our model selection framework selected the model that was used to generate the data in 82% of the cases. Finally, we predicted the growth of the virtual tumours using the selected model resulting in low error at the voxel-level (concordance correlation coefficient (CCC) ranged from 0.66 to 0.99) and global level (percentage error in total tumour cellularity ranged from -12.35% to 0.07%). These results demonstrate the reliability of our framework to identify the most appropriate model under noisy conditions expected in the clinical setting.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.

Keywords

Humans, Glioma, Chemoradiotherapy, Brain Neoplasms, Models, Biological, Treatment Outcome, Neoplasm Grading, Computer Simulation, mathematical oncology, reaction–diffusion, model selection, image-based modelling

Published Open-Access

yes

Recommended Citation

Hiremath, Khushi C; Atakishi, Kenan; Lima, Ernesto A B F; et al., "Identifiability and Model Selection Frameworks for Models of High-Grade Glioma Response to Chemoradiation" (2025). Faculty, Staff and Student Publications. 6586.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6586