AI and Cancer Research ── Brain ── 2026-10-22
Brain tumours: treatment, trials and AI
- Abstract
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 to distinguish TP from PsP against histopathology and/or clinicoradiologic follow-up. The primary meta-analysis included external test cohorts with extractable 2 × 2 data; internal and combined internal and external test analyses were secondary. Diagnostic accuracy was synthesized with bivariate random-effects models. RESULTS: Thirty-one studies including 2,344 patients undergoing imaging assessment were included. Seven external test cohorts, including 219 patient observations, entered the primary meta-analysis. Pooled sensitivity was 0.78 (95% confidence interval [CI], 0.63-0.88), specificity was 0.75 (95% CI, 0.63-0.84), and summary area under the receiver operating characteristic curve (AUC) was 0.81 (bootstrap 95% CI, 0.69-0.91). Internal test cohorts showed higher apparent performance than external cohorts (AUC, 0.91 vs 0.81), but the difference was not statistically significant (difference, -0.09; 95% CI, -0.22 to 0.02; P = 0.12). Risk of bias was frequently high or unclear, and reporting transparency was heterogeneous. CONCLUSIONS: MRI-based machine learning showed moderate-to-good external validation performance for TP-versus-PsP discrimination, but evidence remains limited by small external test cohorts, bias, and incomplete reporting. Larger transparent multicenter validations are needed before routine clinical implementation. Following chemoradiation for glioblastoma, new or worsening MRI abnormalities may represent either true tumor progression or pseudoprogression. This review evaluated machine learning studies for distinguishing true progression from pseudoprogression, emphasizing models tested at external centers. External validation showed moderate-to-good diagnostic performance, but the evidence was limited by small test cohorts, inconsistent reference standards, and incomplete reporting. Larger multicenter studies using current WHO 2021 molecular definitions, standardized diagnostic criteria, and complete reporting are needed before these models can be used routinely in clinical practice.
Journal IF-equivalent: 1.2 (OpenAlex 2-year mean citedness, value as of 2026-10-20, retrieved 2026-10-21; not the official Clarivate IF)Reference: Zhang X, Guo X, Wang Y, Wagstyl K, Booth TC. MRI-Based Machine Learning to Differentiate True Progression from Pseudoprogression in Glioblastoma: An Updated Systematic Review and Meta-Analysis. Neuro-Oncology. 2026 Oct 19 [Epub ahead of print]. doi:10.1093/neuonc/noag259.Checked: Abstract only