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Abstract #2898

Copula Transform Characterizing the Inter-dependence of Multi-parametric Magnetic Resonance Imaging for Glioblastoma Patients

Chao Li1,2,3, Shuo Wang3,4, Pan Liu3, Bart RJ van Dijken5, Carola-Bibiane Schönlieb3, and Stephen John Price1

1Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom, 2Department of Neurosurgery, Shanghai Jiao Tong University School of Medicine, Shanghai, United Kingdom, 3The Centre for Mathematical Imaging in Healthcare, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, United Kingdom, 4Department of Radiology, University of Cambridge, Cambridge, United Kingdom, 5Radiology, Medical Imaging Center, University Medical Center Groningen, Groningen, Netherlands

The purpose of this study was to interrogate the inter-dependence of perfusion and diffusion imaging, and further investigate the clinical relevance of the inter-dependence of perfusion and diffusion, using a glioblastoma cohort containing 115 patients. A statistical method, the empirical copula transform, was applied obtain the joint distribution of perfusion and diffusion imaging, which was then discretized to extract second-order features for hierarchical patient clustering. Three patient subgroups were identified which showed significantly different overall survival and progression-free survival. The results showed that the inter-dependence between perfusion and diffusion imaging may be useful in stratifying patients and evaluating tumor invasiveness.

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