一个新的人工智能工具预测脑肿瘤患者如何通过模拟实时肿瘤新陈代谢,提供个性化治疗选项,从而对饮食和药物作出反应。 A new AI tool predicts how brain tumor patients respond to diet and drugs by modeling real-time tumor metabolism, offering personalized treatment options.

密歇根大学开发的一个新的AI-动力数字双胞胎,利用机器学习实时模拟个体脑瘤新陈代谢,预测显微瘤患者如何应对饮食变化和药物。 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. 该系统通过分析血液测试、肿瘤组织和遗传数据,估计代谢通量-快速癌症细胞如何处理营养物质-让医生进行定制治疗。 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. 经人类数据和老鼠研究验证后,它准确预测了哪些病人受益于氨基酸限制,哪些肿瘤通过改变营养源来抵抗甲酚甲酸酯脂。 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. 这是第一个直接测量人类肿瘤代谢通量的人工智能方法,可以克服传统实验室测试的局限性。 This is the first AI method to directly measure metabolic flux in human tumors, overcoming limits of traditional lab tests. 该工具由国家卫生研究所资助,发表在细胞代谢中,为个人化脑癌护理提供了有希望的一步。 Funded by the NIH and published in Cell Metabolism, the tool offers a promising step toward personalized brain cancer care.