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Abstract #3116

Towards Non-Invasive Characterization of Intravoxel Tumor Heterogeneity: Correlation between Non-Gaussian Diffusion MRI and Histology Using Machine Learning

Muge Karaman1, Lingdao Sha2, Tingqi Shi1, Weiguo Li3,4, Dan Schonfeld2,5,6, Tibor Valyi-Nagy7, and Xiaohong Joe Zhou1,6,8

1Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States, 2Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, United States, 3Research Resource Center, University of Illinois at Chicago, Chicago, IL, United States, 4Department of Radiology, Northwestern University, Chicago, IL, United States, 5Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States, 6Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States, 7Department of Pathology, University of Illinois at Chicago, Chicago, IL, United States, 8Departments of Radiology, and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States

Tissue heterogeneity is an important consideration for diagnosing many diseases. Recently, a novel non-Gaussian diffusion model – continuous-time random-walk model (CTRW) – provided promising evidence indicating a possible link between voxel-level spatiotemporal diffusion heterogeneity and microscopic intravoxel tissue heterogeneity. Establishing a correlation between imaging-based and histology-based measurements, however, has been challenging because of the lack of efficient and subjective evaluation of tissue heterogeneity histologically. In this study, we applied a machine-learning algorithm to quantitatively determine microscopic tissue heterogeneity, enabling a correlation between intravoxel diffusion heterogeneity based on CTRW parameters and structural heterogeneity from histopathology.

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