SynthesisNeurosurgical review2025
Artificial intelligence algorithms for differentiating pseudoprogression from true progression in high-grade gliomas: A systematic review and meta-analysis.
Synthesis in Neurosurgical review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- A spatiotemporal state-inference framework for adaptive immunotherapy in glioblastoma.Frontiers in oncology · 2026Review
- AI-driven radiomics and radiogenomics: supporting the assessment and differentiation of pseudoprogression in cellular immunotherapy for glioblastoma.Frontiers in immunology · 2026Review
- Now you see me; now you don't.Science translational medicine · 2025Review
- Review
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Differentiating pseudoprogression (PsP) from true progression (TP) in high-grade glioma (HGG) patients is still challenging and critical for effective treatment management. This meta-analysis evaluates the diagnostic accuracy of artificial intelligence (AI) algorithms in making this distinction. We aimed to assess the performance of AI algorithms in distinguishing between pseudoprogression and true progression in patients with high-grade glioma. We searched PubMed, Cochrane, and Embase databases for studies reporting on AI algorithms that differentiate pseudoprogression from true progression in high-grade gliomas. The analysis evaluated reported metrics such as accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. The meta-analysis included 26 articles involving 1,972 patients. In the high-grade glioma group, AI algorithms demonstrated a sensitivity of 88% (95% CI: 77%-100%) and a specificity of 75% (95% CI: 54%-97%). For the glioblastoma (GBM) group, accuracy was 77% (95% CI: 68%-86%), with sensitivity of 77% (95% CI: 67%-86%) and specificity of 63% (95% CI: 43%-82%). Overall, the algorithms achieved an accuracy of 80% (95% CI: 76%-85%), sensitivity of 85% (95% CI: 80%-91%), specificity of 69% (95% CI: 58%-80%), a PPV of 79% (95% CI: 58%-100%), a NPV of 97% (95% CI: 90%-100%), and an F1 score of 74% (95% CI: 67%-81%). AI algorithms show significant promise in accurately distinguishing between pseudoprogression and true progression in high-grade gliomas, suggesting their potential utility in clinical decision-making.
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Registered trials
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