A data assimilation framework for predicting the spatiotemporal response of high-grade gliomas to chemoradiation

Abstract Background High-grade gliomas are highly invasive and respond variably to chemoradiation. Accurate, patient-specific predictions of tumor response could enhance treatment planning. We present a novel computational platform that assimilates MRI data to continually predict spatiotemporal tumo...

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Bibliographic Details
Published in:BMC Cancer
Main Authors: Hugo J. M. Miniere, David A. Hormuth, Ernesto A. B. F. Lima, Maguy Farhat, Bikash Panthi, Holly Langshaw, Mihir D. Shanker, Wasif Talpur, Sara Thrower, Jodi Goldman, Sophia Ty, Caroline Chung, Thomas E. Yankeelov
Format: Article
Language:English
Published: BMC 2025-07-01
Subjects:
Personalized medicine
Mathematical modeling
Glioma
Imaging
Computational oncology
Online Access:https://doi.org/10.1186/s12885-025-14557-3

Internet

https://doi.org/10.1186/s12885-025-14557-3

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