Non-invasive MRI-based survival prediction for glioblastoma patients is potentially valuable for informing prognostic and treatment counseling. In this study, we analyzed the relationships between overall survival and several automatic segmentation-based MR imaging features. Simple logistic regression models to classify 1-year survival with clinical factors and selected imaging features were trained and tested. Results showed that combining imaging features with clinical factors improved the survival prediction.
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