Faculty, Staff and Student Publications
Language
English
Publication Date
10-1-2024
Journal
Journal of Computational Science
DOI
10.1016/j.jocs.2024.102400
PMID
40303598
PMCID
PMC12037169
PubMedCentral® Posted Date
10-1-2025
PubMedCentral® Full Text Version
Author MSS
Abstract
Constructing digital twins for predictive tumor treatment response models can have a high computational demand that presents a practical barrier for their clinical adoption. In this work, we demonstrate that proper orthogonal decomposition, by which a low-dimensional representation of the full model is constructed, can be used to dramatically reduce the computational time required to calibrate a partial differential equation model to magnetic resonance imaging (MRI) data for rapid predictions of tumor growth and response to chemotherapy. In the proposed formulation, the reduction basis is based on each patient's own MRI data and controls the overall size of the "reduced order model". Using the full model as the reference, we validate that the reduced order mathematical model can accurately predict response in 50 triple negative breast cancer patients receiving standard of care neoadjuvant chemotherapy. The concordance correlation coefficient between the full and reduced order models was 0.986 ± 0.012 (mean ± standard deviation) for predicting changes in both tumor volume and cellularity across the entire model family, with a corresponding median local error (inter-quartile range) of 4.36% (1.22%, 15.04%). The total time to estimate parameters and to predict response dramatically improves with the reduced framework. Specifically, the reduced order model accelerates our calibration by a factor of (mean ± standard deviation) 378.4 ± 279.8 when compared to the full order model for a non-mechanically coupled model. This enormous reduction in computational time can directly help realize the practical construction of digital twins when the access to computational resources is limited.
Keywords
mathematical model, computational oncology, reduced order model, digital twins, reaction-diffusion
Published Open-Access
yes
Recommended Citation
Christenson, Chase; Wu, Chengyue; Hormuth, David A; et al., "Fast Model Calibration for Predicting the Response of Breast Cancer to Chemotherapy Using Proper Orthogonal Decomposition" (2024). Faculty, Staff and Student Publications. 6226.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6226
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