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Abstract #2779

Automatic Segmentation and Classification of Glioblastoma using DCE-MRI

Moran Artzi1, Gilad Liberman2,3, Deborah Blumenthal4, Felix Bokstein4, Orna Aizenstein1, and Dafna Ben Bashat1,5

1Functional Brain Center, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel, 2Functional Brain Centerasky Medical Cente, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel, 3Department of Chemical Physics, Weizmann Institute, Rehovot, Israel, 4Neuro-Oncology Service, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel, 5Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel-Aviv, Israel

Segmentation of lesion area in patients with glioblastoma (GB) into active tumor, tissue necrosis, vasogenic edema and infiltrative disease, is highly important for patient monitoring, yet is challenging using standard radiological assessment. The aim of this study was to segment the lesion area into these four tissue types in GB patients. Voxel-wise classification was performed using support-vector-machine based on anatomical and DCE-MRI parameters. Significant differences were detected between the tissue types for FLAIR, vp, and ktrans. Sensitivity and specificity of the training-set were measured based on 2-fold-cross-validation analysis, showing high sensitivities and specificities of 94-100% for the different tissue types.

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