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Abstract #3259

Prognostic value of MR imaging features derived from automatic segmentation in glioblastoma

Quan Dou1, Xue Feng1, Sohil Patel2, and Craig H. Meyer1
1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, 2Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, United States

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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