NIMG-79. SPATIALLY MAPPED PREDICTIONS OF EVOLVING TUMOR RESPONSE OF HIGH-GRADE GLIOMA VIA IMAGE-DRIVEN MATHEMATICAL MODELING

Timely treatment response assessment of high-grade gliomas (HGG), crucial for driving therapeutic decisions, remains a challenge; as HGGs exhibit variable response to treatment within different sub-regions. Current assessment using multi-parametric MRI (mpMRI) depends largely upon follow-up (FU) ima...

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Published in:Neuro-oncology (Charlottesville, Va.) Va.), 2022-11, Vol.24 (Suppl 7), p.vii183-vii183
Main Authors: Farhat, Maguy, Hormuth, David, Langshaw, Holly, Bronk, Juliana, Curl, Brandon, Yadav, Divya, Upadhyay, Rituraj, Elliot, Andrew, Goldman, Jodi, Erickson, Lily, Talpur, Wasif, Lee, Maggie, Yankeelov, Thomas, Chung, Caroline
Format: Article
Language:English
Subjects:
Neuro-Imaging
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Summary:Timely treatment response assessment of high-grade gliomas (HGG), crucial for driving therapeutic decisions, remains a challenge; as HGGs exhibit variable response to treatment within different sub-regions. Current assessment using multi-parametric MRI (mpMRI) depends largely upon follow-up (FU) imaging timepoints for achieving diagnostic certainty, which delays therapeutic interventions. Mathematical modeling (MM) of tumor growth and treatment response can provide spatiotemporal information of HGG evolution in response to treatment, thus allowing for prospective early identification of resilient tumor subregions. AIMS: We aim to initialize and calibrate an image-driven MM framework to forecast HGG response, both at the end of chemoradiotherapy (CRT) and at 3-month FU.
ISSN:1522-8517
1523-5866
DOI:10.1093/neuonc/noac209.697