Machine Learning Models for Predicting Pseudoprogression in Glioblastoma: A Systematic Review and Diagnostic Meta-Analysis

Abstract Background Artificial intelligence (AI) and machine learning (ML) are increasingly being used in medical practice, particularly in neuroradiology. Glioblastoma, a highly aggressive brain tumor, poses significant diagnostic challenges, especially in differentiating actual tumor progression (TP) from pseudoprogression (PsP) using conventional MRI. ML and AI techniques offer a promising approach, but their diagnostic accuracy remains debated. Methods A systematic review and diagnostic meta-analysis were performed to assess the diagnostic efficacy of ML algorithms applied in MRI of glioblastoma patients to differentiate between TP and PsP. We evaluated classical machine learning and deep learning models using diffusion-weighted, perfusion-weighted, and structural MRI inputs to differentiate pseudoprogression from true progression in glioblastoma. PubMed, Scopus, and Cochrane databases were searched. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), accuracy, and area under the curve (AUC) were calculated using a random-effects model. Results Eleven studies involving a total of 676 patients were included. ML models demonstrated a pooled sensitivity of 0.899 (95% CI: 0.843–0.936; I² = 16.3%) and a specificity of 0.766 (95% CI: 0.647–0.854; I² = 38.8%). The diagnostic odds ratio was 27.059 (95% CI: 11.094–65.999; I² =61.9%), and the overall accuracy was 0.85 (95% CI: 0.788–0.897; I² = 66%). The AUC was estimated at 0.88 (95% CI: 0.727–0.896). Deek’s funnel plot showed no significant publication bias (p = 0.283). Conclusion ML-based models demonstrate high diagnostic accuracy in distinguishing TP from PsP in glioblastoma patients, offering a promising tool to enhance radiological assessment and support clinical decision-making. However, further standardization across algorithms and datasets is needed to facilitate their integration into routine clinical practice.