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. To systematically evaluate and quantitatively synthesize the diagnostic accuracy of ML models trained on ¹8F-FDG PET radiomics for glioma classification. We conducted a PRISMA-compliant systematic review and meta-analysis registered on OSF (https: //doi. org/10. 17605/OSF. IO/XJG6P). PubMed, Scopus, and Web of Science were searched up to January 2025. Studies were included if they applied ML algorithms to ¹8F-FDG PET radiomic features for glioma classification and reported at least one performance metric. Data extraction included demographics, imaging protocols, feature types, ML models, and validation design. Meta-analysis was performed using random-effects models with pooled estimates of accuracy, sensitivity, specificity, AUC, F1 score, and precision. Heterogeneity was explored via meta-regression and Galbraith plots. Twelve studies comprising 2, 321 patients were included. Pooled diagnostic metrics were: accuracy 92. 6% (95% CI: 91. 3–93. 9%), AUC 0. 95 (95% CI: 0. 94–0. 95), sensitivity 85. 4%, specificity 89. 7%, F1 score 0. 78, and precision 0. 90. Heterogeneity was high across all domains (I² >75%). Meta-regression identified ML model type and validation strategy as partial moderators. Models using CNNs or PET/MRI integration achieved superior performance. ML models based on ¹8F-FDG PET radiomics demonstrate strong and balanced diagnostic performance for glioma classification. However, methodological heterogeneity underscores the need for standardized pipelines, external validation, and transparent reporting before clinical integration.
Shahriari et al. (Tue,) studied this question.