Machine Learning-Driven radiomics on 18 F-FDG PET for glioma diagnosis: a systematic review and meta-analysis.

Background: Machine learning (ML) applied to radiomics has revolutionized neuro-oncological imaging, yet the diagnostic performance of ML models based specifically on ^18F-FDG PET features in glioma remains poorly characterized.

Objective: To systematically evaluate and quantitatively synthesize the diagnostic accuracy of ML models trained on ^18F-FDG PET radiomics for glioma classification.

Methods: We conducted a PRISMA-compliant systematic review and meta-analysis registered on OSF ( https://doi.