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Abstract #2169

Monitoring glioma heterogeneity during tumor growth using clustering analysis of multiparametric MRI data

Benjamin Lemasson1,2, Nora Collomb1,2, Alexis Arnaud3,4, Florence Forbes3,4, and Emmanuel Luc Barbier1,2

1U836, Inserm, Grenoble, France, 2Université Grenoble Alpes, Grenoble Institut des Neurosciences, Grenoble, France, 3INRIA, Grenoble, France, 4LJK, Université Grenoble Alpes, Grenoble, France

Brain tumor heterogeneity plays a major role during gliomas growth and for the tumors resistance to therapies. The goal of this study was to demonstrate the ability of clustering analysis applied to multiparametric MRI (mpMRI) data to summarize and quantify intralesional heterogeneity during tumor growth. A mpMRI dataset of rats bearing glioma was acquired during the tumor growth (5 maps, 8 animals and 6 time points). After co-registration of every MR data over time, a clustering analysis was performed using a Gaussian mixture distribution model. Although preliminary, our results show that clustering analysis of mpMRI has a great potential to monitor quantitatively intralesional heterogeneity during the growth of tumors.

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