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Unravelling differences and hallmarks in suspected diffuse low-grade gliomas: a multicentre database study.

Francesco Latini1, Markus Fahlström2, Alice Neimantaite3

  • 1Department of Medical Sciences, Section of Neurosurgery Uppsala University Hospital, S-75185 Uppsala, Sweden.

Brain Communications
|October 13, 2025
PubMed
Summary

This study reveals distinct clinical and radiological phenotypes for diffuse low-grade glioma (DLGG) molecular subgroups. Integrating patient and tumor features aids in understanding DLGG progression and developing predictive models.

Keywords:
diffuse low-grade gliomasepilepsymolecular profileonco-functional trajectorywhite matter

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Area of Science:

  • Neuro-oncology
  • Radiology
  • Molecular Pathology

Background:

  • Diffuse low-grade gliomas (DLGG) natural history is influenced by molecular status.
  • Preoperative clinical and radiological data integration may enhance understanding of DLGG.
  • Identifying distinct phenotypes for DLGG molecular subgroups is crucial for prognosis.

Purpose of the Study:

  • To systematically analyze clinical and radiological phenotypes of DLGG molecular subgroups at diagnosis.
  • To identify differences in age, tumor location, onset symptoms, and cognitive status among DLGG molecular subgroups.
  • To explore the utility of integrated clinico-radiological approaches for DLGG prediction models.

Main Methods:

  • Analysis of 235 patients with World Health Organization (WHO) grade 2 gliomas from nine Scandinavian centers.
  • Inclusion of patients with known isocitrate dehydrogenase (IDH) status and 1p19q codeletion status.
  • Utilized MRI-based tumor volume segmentation, Brain-Grid (BG) system for invasiveness analysis, and regression analyses.

Main Results:

  • Three molecular subgroups (IDH-mutated astrocytomas, oligodendrogliomas, IDH-wildtype astrocytomas) showed significant differences in age, location, onset, and cognitive status.
  • Seizure onset correlated with BG voxel count and specific locations (A3C2S2).
  • Cognitive deficits related to age, gender, and tumor volume, with specific white matter tract infiltrations predicting subgroups.

Conclusions:

  • Integrated clinico-radiological analysis identified distinct phenotypes across DLGG molecular subgroups.
  • Patient-specific (age, onset) and tumor-specific (location, infiltration) features are relevant for preoperative DLGG understanding.
  • Combining clinical and radiological data offers potential for improved DLGG prediction models and understanding of onco-functional trajectory.