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Tumor classification and prediction using robust multivariate clustering of multiparametric MRI

Alexis Arnaud 1 Florence Forbes 1 Benjamin Lemasson 2 Emmanuel Luc Barbier 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
2 Equipe 5 : NeuroImagerie Fonctionnelle et Perfusion Cérébrale
INSERM - Institut National de la Santé et de la Recherche Médicale : U1216, GIN - [GIN] Grenoble Institut des Neurosciences, UGA - Université Grenoble Alpes
Abstract : In neuro-oncology, the use of multiparametric MRI may better characterize brain tumor heterogeneity. To fully exploit multiparametric MRI (e.g. tumor classification), appropriate analysis methods are yet to be developed. In this work, we show on small animals data that advanced statistical learning approaches can help 1) in organizing existing data by detecting and excluding outliers and 2) in building a dictionary of tumor fingerprints from a clustering analysis of their microvascular features.
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https://hal.archives-ouvertes.fr/hal-01253584
Contributor : Alexis Arnaud <>
Submitted on : Monday, January 11, 2016 - 9:27:38 AM
Last modification on : Thursday, May 6, 2021 - 3:07:48 AM
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Alexis Arnaud, Florence Forbes, Benjamin Lemasson, Emmanuel Luc Barbier. Tumor classification and prediction using robust multivariate clustering of multiparametric MRI. International Society for Magnetic Resonance in Medicine, May 2015, Toronto, Canada. ⟨hal-01253584⟩

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