Machine Learning–Driven Radiomics on 18 F-Fdg Pet for Glioma Diagnosis: A Systematic Review and Meta-Analysis Publisher



A Shahriari ALI ; S Ghazanafar Ahari SASAN ; A Mousavi ALI ; M Sadeghi MAHDIE ; M Abbasi MARJAN ; M Hosseinpour MAHSA ; A Mir ASAL ; D Zohouri Zanganeh DORRIN ; H Gharedaghi HOSSEIN ; S Ezati SABA
Source: Cancer Imaging Published:2025

Abstract

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.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. Results: 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. Conclusion: 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. © 2025 Elsevier B.V., All rights reserved.