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A new AI tool predicts how brain tumor patients respond to diet and drugs by modeling real-time tumor metabolism, offering personalized treatment options.
A new AI-powered digital twin developed at the University of Michigan uses machine learning to model individual brain tumor metabolism in real time, predicting how glioma patients will respond to dietary changes and drugs.
By analyzing blood tests, tumor tissue, and genetic data, the system estimates metabolic flux—how fast cancer cells process nutrients—allowing doctors to tailor treatments.
Validated in human data and mouse studies, it accurately predicted which patients benefited from amino acid restriction and which tumors resisted mycophenolate mofetil by switching nutrient sources.
This is the first AI method to directly measure metabolic flux in human tumors, overcoming limits of traditional lab tests.
Funded by the NIH and published in Cell Metabolism, the tool offers a promising step toward personalized brain cancer care.
Una nueva herramienta de IA predice cómo los pacientes con tumor cerebral responden a la dieta y los medicamentos modelando el metabolismo tumoral en tiempo real, ofreciendo opciones de tratamiento personalizadas.