Reference : Imaging-informed BIOmechanical brain tumor forecast MOdelling

	Document type :	Scientific congresses, symposiums and conference proceedings : Unpublished conference
	Discipline(s) :	Engineering, computing & technology : Multidisciplinary, general & others
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/52636

Title :	Imaging-informed BIOmechanical brain tumor forecast MOdelling
Language :	English
Author, co-author :	Abbad Andaloussi, Meryem [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Interventional Neuroscience >]
	Husch, Andreas [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Interventional Neuroscience >]
	Urcun, Stephane [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
	Bordas, Stéphane [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Publication date :	6-Jun-2022
Peer reviewed :	No
Audience :	International
Event name :	European Congres on COmputational Methods in Applied Sciences and engineering (ECCOMAS)
Event date :	from 05-06-2022 to 09-06-2022
Event organizer :	NTNU, SINTEF, NOACM, ECCOMAS, Visit Oslo
Event place (city) :	Oslo
Event country :	Norway
Keywords :	[en] Malignant transformation ; Low Grade Glioma ; Low Grade Astrocytoma ; Medical Resonance Imaging ; Patient-specific Tumor Growth Model
Abstract :	[en] Grade 3 and 4 Astrocytomas are high grade gliomas (HGG) that usually result from initially less aggressive low grade gliomas (LGG) through malignant transformation (MT). This process has various definitions in the literature, clinical and histopathological, depending on the scale of the study and researchers' interest. We introduce an overview of different aspects of MT: molecular, clinical and the role of the microenvironment in acquiring the malignant phenotype. Furthermore, we introduce a new hypothesis that could explain the spatial progression of low grade astrocytoma (LGA) during MT. The former hypothesis will next be tested on LGA patients through tumor segmentation from Medical Resonance Images (MRI) and a mechanistic growth model.
Funders :	Institute of Advanced Studies
Name of the research project :	IBIOMO
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/52636

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