aceMRI-Based Machine Learning to Differentiate True Progression from Pseudoprogression in Glioblastoma: An Updated Systematic Review and Meta-Analysis
Background Differentiating true progression (TP) from pseudoprogression (PsP) after chemoradiation for glioblastoma remains clinically difficult. We evaluated externally validated diagnostic performance of magnetic resonance imaging (MRI)-based machine learning models and appraised study quality and transparency. Methods PubMed, Embase, MEDLINE, and Cochrane Library were searched through October 1, 2025 (PROSPERO registration CRD420251144894). Eligible adult glioblastoma studies used MRI-based machine learning t…
In external test cohorts, MRI-based machine learning models distinguished true glioblastoma progression from pseudoprogression with pooled sensitivity of 0.78 and specificity of 0.75.
The supporting evidence is weak: external cohorts were small, risk of bias was often high or unclear, and reporting was incomplete, so the models are not ready for routine clinical use.
The external evidence base is small and often biased or incompletely reported, so the pooled accuracy may not hold up in routine care; larger transparent multicenter validations are still needed.
Evidence
- Peer-reviewedNeuro-Oncology2026-10-19
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Truvace Impact Record TRV-2026-2075, v1: “MRI-Based Machine Learning to Differentiate True Progression from Pseudoprogression in Glioblastoma: An Updated Systematic Review and Meta-Analysis.” Truvace, 2026-10-21. /record/TRV-2026-2075 (accessed at citation time). sha256 397ce5b5386e037d…
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