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Background <p>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.</p> Objective <p>To systematically evaluate and quantitatively synthesize the diagnostic accuracy of ML models trained on ^18F-FDG PET radiomics for glioma classification.</p> Methods <p>We conducted a PRISMA-compliant systematic review and meta-analysis registered on OSF (<a href="https://doi.org/10.17605/OSF.IO/XJG6P">https://doi.org/10.17605/OSF.IO/XJG6P</a>). 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.</p> Results <p>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 &gt;75%). Meta-regression identified ML model type and validation strategy as partial moderators. Models using CNNs or PET/MRI integration achieved superior performance.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月26日

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  • Ali Shahriari,
  • Sasan Ghazanafar Ahari,
  • Ali Mousavi,
  • Mahdie Sadeghi,
  • Marjan Abbasi,
  • Mahsa Hosseinpour,
  • Asal Mir,
  • Dorrin Zohouri Zanganeh,
  • Hossein Gharedaghi,
  • Saba Ezati,
  • Ali Sareminia,
  • Dina Seyedi,
  • Mahla Shokouhfar,
  • Ali Darzi,
  • Alireza Ghaedamini,
  • Sara Zamani,
  • Farbod Khosravi,
  • Mahsa Asadi Anar

摘要

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.