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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Detalles Bibliográficos
Autores principales: Chaudhuri, Anirban, Pash, Graham, Hormuth, David A., Lorenzo, Guillermo, Kapteyn, Michael, Wu, Chengyue, Lima, Ernesto A. B. F., Yankeelov, Thomas E., Willcox, Karen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598726/
https://www.ncbi.nlm.nih.gov/pubmed/37886348
http://dx.doi.org/10.3389/frai.2023.1222612

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598726/
https://www.ncbi.nlm.nih.gov/pubmed/37886348
http://dx.doi.org/10.3389/frai.2023.1222612

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