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Abstract #5069

The Fuzzy MAD Glioma Conjecture, using Fuzzy C Means to classify Multimodal Apparent Diffusion for glioma stratification

Frederick C. Damen1, Changliang Su2, Thomas Anderson1, Jay Tsuruda3, Rifeng Jiang4, and Kejia Cai1
1Radiology, University of Illinois at Chicago, Chicago, IL, United States, 2State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China, 3Department of Radiology, USC Keck School of Medicine, Los Angeles, CA, United States, 4Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, China

Synopsis

Keywords: Microstructure, Brain

Motivation: Gliomas are a very heterogeneous primary brain tumor. Their heterogeneity lies not only at the microscopic (genetic / biochemical) level, but at the mesoscopic (cellular morphology) level and macroscopic (tissue pathology) level.

Goal(s): To better understand the underpinnings of the glioma heterogeneity.

Approach: We applied the Multimodal Apparent Diffusion (MAD) method and Fuzzy C Means to multi b‑value diffusion weighted magnetic resonance imaging, up to b‑value of 10K s/mm2, on 54 glioma patients.

Results: We discerned 15 normal appearing tissue types and 19 lesion types, including 3 voracious solid tumor, 5 recruiting solid tumor, 3 edema, and 7 b0 attenuated types.

Impact: Each stage in the glioma’s progression manifests in unique changes in MAD parameter signatures. The ability to to more precisely understand the heterogeneous microstructure involved can be used to improved diagnosis and prognosis of gliomas.

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