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Abstract #3350

Multiparametric Tumor Clustering for Predicting Recurrent Glioblastoma: Comparison with Single Parametric Diffusion and Perfusion Analyses

Ra Gyoung Yoon 1 , Ho Sung Kim 2 , Choong Gon Choi 2 , and Sang Jun Kim 2

1 Radiology, Asan medical center, Seoul, Seoul, Korea, 2 Asan medical center, Seoul, Korea

We performed this study to determine if enlarging contrast-enhancing lesion (CEL) with similar tumor microenvironment (TM) in patients with posttreatment glioblastoma, can be labeled by clustering methods to differentiate between recurrent glioblastoma (RGM) and radiation necrosis (RN). The tumor clustering method including four distinct clusters (tumor cluster, radiation change cluster, necrosis cluster, edema cluster) was performed on DSC, DCE, and DW MR imagings of 84 patients with pathologically proven RGM or RN. We have demonstrated that tumor clustering of clinical MR imaging data is feasible. Moreover, the volume fraction of tumor cluster was associated with the possibility of RGM.

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