Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients

Keyphrases

• Accessible Treatment 20%
• Area under the Receiver Operating Characteristic (AUROC) 20%
• Brain Malignancies 20%
• Counseling Outcome 20%
• Easily Accessible 20%
• Functional Magnetic Resonance Imaging 20%
• Glioblastoma multiforme 100%
• Hybrid Feature Selection Method 20%
• Improved Outcomes 20%
• Leave-one-subject-out 20%
• Long-term Survival 40%
• Machine Learning Analytics 100%
• Median Survival 20%
• Model Evaluation 20%
• Non-invasive Method 20%
• Novel Hybrids 20%
• Optimal Feature Selection 20%
• Patient Counseling 20%
• Poor Prognosis 20%
• Predictive Features 20%
• Pretreatment Planning 20%
• Radiomic Biomarker 20%
• Recursive Feature Elimination 20%
• Region of Interest 20%
• Resting-state Functional Connectivity 100%
• Specific Features 20%
• Study Support 20%
• Support Vector Machine 20%
• Survival Outcomes 100%

Neuroscience

• Biological Marker 20%
• Functional Magnetic Resonance Imaging 20%
• Glioblastoma Multiforme 100%
• Radiomics 20%
• Resting-State Functional Connectivity 100%
• Support Vector Machine 20%