A Systematic Review and Meta-Analysis of Survival Prediction in Glioblastoma Patients Using Advanced MRI Techniques.
Authors
Affiliations (10)
Affiliations (10)
- Department of Internal Medicine, Faculty of Medicine, King Abdulaziz University (Rabigh), Jeddah, Saudi Arabia.
- Department of Internal Medicine/Diagnostic and Interventional Radiology, Faculty of Medicine, Al Baha University, Al Baha, Saudi Arabia.
- Department of Radiology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.
- Department of Medicine, Majmaah University, Majmaah, Saudi Arabia.
- Department of Medicine, King Khalid University, Abha, Saudi Arabia.
- Faculty of Medicine, Jazan University, Jazan, Saudi Arabia.
- Faculty of Medicine, Batterjee Medical College, Jeddah, Saudi Arabia.
- Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.
- Faculty of Medicine, King Saud University, Riyadh, Saudi Arabia.
- Department of Surgery, Medical College, King Faisal University, Hofuf, Al-Ahsa, Saudi Arabia.
Abstract
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. 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. 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, showing no heterogeneity (I2 = 0%) and a pooled AUC of 0.81 (95% CI: 0.77-0.85; P < 0.001). Subgroup analysis revealed that ML models (AUC: 0.83 [95% CI: 0.78-0.87]) statistically outperformed radiomics-based models (AUC: 0.76 [95% CI: 0.71-0.80]) for PFS prediction (P = 0.02). Radiomics and ML approaches based on pre-treatment MRI are promising tools for predicting survival outcomes in glioblastoma patients, with ML models demonstrating a slight edge over radiomics for PFS prediction. Standardized protocols and larger multi-center studies are warranted to facilitate clinical adoption.