Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas

Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas

We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties in the underlying tumor biology in high-grade gliomas, where...

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Bibliographic Details
Main Authors: Anirban Chaudhuri, Graham Pash, David A. Hormuth, Guillermo Lorenzo, Michael Kapteyn, Chengyue Wu, Ernesto A. B. F. Lima, Thomas E. Yankeelov, Karen Willcox
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-10-01
Series:Frontiers in Artificial Intelligence
Subjects:
digital twin
risk-aware clinical decision-making
personalized tumor forecasts
uncertainty quantification
adaptive radiotherapy
mathematical oncology
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2023.1222612/full

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https://www.frontiersin.org/articles/10.3389/frai.2023.1222612/full

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