Digital twins for in vivo metabolic flux estimations in patients with brain cancer (2026) Baharan Meghdadi

Description

This article describes a digital twin framework (DTF) that uses machine learning to quantify metabolic fluxes in tissues from patients with glioma/brain cancer. The framework is designed to advance personalized cancer targeting by identifying which patients may benefit from specific targeted metabolic therapies, such as specialized diets (e.g., serine/glycine-free) or pharmacological nucleotide targeting.