A Systematic Review and Meta-analysis of Survival Prediction in Glioblastoma Patients using Advanced MRI Techniques
Abstract
Introduction: Glioblastoma (GBM) is an aggressive brain tumor with a dismal prognosis. Recent advances in radiomics and machine learning (ML) applied to magnetic resonance imaging (MRI) have demonstrated promising potential in enhancing clinical decision-making and prognostic accuracy. This systematic review and meta-analysis aimed to evaluate the predictive performance of radiomics and ML techniques applied to pre-treatment MRI data in glioblastoma prognosis.
Methods: A comprehensive literature search was conducted across MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials up to March 2024 for studies using radiomics or ML techniques applied to pre-treatment MRI scans to predict progression-free survival (PFS) and overall survival (OS) in glioblastoma patients. The primary outcome was the area under the receiver operating characteristic curve (AUC). Study quality was assessed using the QUADAS-2 tool, meta-analysis employed a random-effects model, and heterogeneity was evaluated using the I2 statistic.
Results: Sixteen studies comprising a total of 2,342 patients were included. MRI-based machine learning models demonstrated high predictive performance for glioblastoma prognosis (AUC: 0.71-0.92), with a tendency to outperform radiomics-based approaches (AUC: 0.68-0.88). A meta-analysis of 12 studies yielded a pooled AUC of 0.78 (95% CI: 0.74-0.82; p < 0.001) for PFS prediction with moderate heterogeneity (I2 = 59%). Four studies focused on OS prediction,…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 2,342 participants or records.
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