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 heterogeneity in the response to standard-of-care (SOC) radiotherapy contributes to …