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Abstract #4253

Voxel-based Multiparametric Analysis of Magnetic Resonance Imaging Data for Differentiating Recurrent Glioblastoma from Delayed Radiation Necrosis

Ra Gyoung Yoon1, Ho Sung Kim2, Myeong Ju Koh3, and Sang Joon Kim2

1Radiology, Catholic Kwandong University International St. Mary’s Hospital, Incheon, Korea, Republic of, 2Radiology and Research Institute of Radiology, Asan Medical Center, Seoul, Korea, Republic of, 3Jeju National University Hospital

We evaluated a volume-weighted voxel-based multiparametric (MP) clustering method as an imaging biomarker for differentiating recurrent glioblastoma from delayed radiation necrosis, comparing to the single imaging parameters of DWI, DSC and DCE perfusion MR. In an area under the receiver operating characteristic curve analysis, volume-weighted voxel-based MP clustering demonstrated better diagnostic accuracy for discriminating these two conditions than single imaging parameters. When performed with use of an optimal cutoff, volume-weighted voxel-based MP clustering improved the overall sensitivity. Therefore, quantitative analysis using volume-weighted voxel-based MP clustering is superior to single imaging parameter measurements for differentiating recurrent glioblastoma from delayed radiation necrosis.

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