Development of a circulating extracellular vesicle-based IDH mutant protein detection technology for diagnosis of IDH mutation status in glioma, enabling personalized resection strategies, precision medication, and disease dynamics monitoring in IDH-mutant glioma. We developed a tumor-classification strategy that discriminates tumor types by detecting the IDH1.R132H mutant protein in plasma extracellular vesicles using a bead-assisted, flow-cytometry–based assay. We assessed the differential expression’s capacity to distinguish tumor grades, analyzed pre-operative and post-operative expression variations, and verified consistency with histopathological immunohistochemistry. The method demonstrated a diagnostic sensitivity of 76.2% (95%CI: 54.9%–89.4%) and a specificity of 86.7% (95%CI: 62.1%–96.3%) for IDH-mutant glioma. Further analysis revealed that the expression level of the IDH mutant protein in plasma EVs was significantly associated with tumor grade in IDH-mutant glioma (P < 0.0001). Moreover, the expression level of the IDH mutant protein in plasma EVs significantly decreased in post-operative patients (P < 0.01) and showed a high degree of consistency with the pathological immunohistochemical results at different tumor grades. We propose a novel plasma extracellular-vesicle liquid biopsy with potential preoperative application in detecting IDH-mutant glioma, affording noninvasive, rapid, efficient, and low-cost assessment of IDH mutation status. The online version contains supplementary material available at 10.1186/s12885-026-15810-z.

Pituitary adenomas (PAs) can exert pressure on the optic apparatus, leading to visual impairment. A subset of patients may observe a swift improvement in their vision following surgery. Nevertheless, the alterations in the structural connectome during the early postoperative period remain largely unexplored. The research employed probabilistic tractography, graph theoretical analysis, and statistical methods on preoperative and postoperative structural magnetic resonance imaging and diffusion tensor images from 13 PA patients. Postoperative analysis revealed an increase in global and local efficiency, signifying improved network capacity for parallel information transfer and fault tolerance, respectively. Enhanced clustering coefficient and reduced shortest path length were also observed, suggesting a more regular network organization and shortened communication steps within the brain network. Furthermore, alterations in node graphical properties were detected, implying a restructuring of the network's control points, possibly contributing to more efficient visual processing. These findings propose that rapid vision recovery post-surgery may be associated with significant reorganization of the brain's structural connectome, enhancing the efficiency and adaptability of the network, thereby facilitating improved visual processing.