Can Innovative Imaging Better Predict the Aggressiveness of Benign Brain Tumors?
Low-grade brain tumors called oligodendrogliomas are generally considered less aggressive than their high-grade counterparts. However, some of them progress rapidly and recur early, complicating their management. A team of Chinese researchers explored the use of an advanced imaging technique to better identify these high-risk cases.
Positron emission tomography (PET) imaging using a specific tracer called [18F]FET allows visualization of the metabolic activity of tumors. Unlike conventional MRI, which does not always detect differences within grade 2 oligodendrogliomas, this method revealed two distinct subgroups. Half of the grade 2 tumors showed high tracer uptake similar to that observed in grade 3 tumors. These high-uptake tumors were associated with an increased risk of early recurrence.
Genetic analyses confirmed that high-uptake grade 2 oligodendrogliomas exhibited molecular characteristics similar to aggressive tumors. Their cells more strongly activate mechanisms related to energy production and the attraction of tumor cells. Conversely, those with low uptake showed increased activity of genes involved in the formation of the myelin sheath, a structure essential for proper neuron function.
This discovery suggests that [18F]FET imaging could refine patient prognosis by identifying the most at-risk grade 2 tumors. Protein markers such as AK4, CXCL5, and IL-33 have also been identified as potential indicators of aggressiveness. Their detection through additional tests could help tailor treatments in a more personalized way.
The combined use of this imaging technique and the analysis of biological markers thus opens a new avenue for better targeting therapeutic strategies and preventing recurrence in patients with these brain tumors.
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DOI: https://doi.org/10.1007/s11060-026-05528-0
Title: [18F]FET-PET in dissecting low-grade oligodendrogliomas
Journal: Journal of Neuro-Oncology
Publisher: Springer Science and Business Media LLC
Authors: Jiahui Liu; Qingqing Han; Wenbo Wu; Hai Wang; Xiaona Li; Kun Zheng; Dachun Zhao; Wenbin Ma; Xin Cheng; Xiaopeng Guo; Li Huo; Yu Wang