Glioblastoma multiforme (GBM), the most malignant subtype of glioma, poses significant diagnostic challenges due to limitations in current methods, such as invasive histopathological examination and costly, lab-restricted biomarker detection technologies. Herein, we report a lanthanide (Tb3+)-doped organic framework-based sensor array for minimally invasive, sensitive, and accurate glioma diagnosis via cerebrospinal fluid (CSF) biopsy. The sensor array integrates three distinct Tb3+-doped frameworks, which exhibit unique topological structures, surface charges, and fluorescence responses, enabling differential recognition of glioma-related biomarkers. The sensor array demonstrated robust discriminatory capacity for eight CSF-relevant molecules via a machine learning algorithm. When applied to clinical CSF samples, it achieved satisfactory separation of glioma patient and normal control samples with 95.5% diagnostic accuracy. This sensor array, combined with advanced machine learning, offers great potential for clinical translation in early glioma diagnosis and molecular stratification.
Zhou et al. (2026) studied this question.