Microvascular MRI and Unsupervised Clustering Yields Histology-Resembling Images in Two Rat Models of Glioma. Issue 8 (August 2014)

• Record Type: Journal Article
• Authors: Coquery, Nicolas; Francois, Olivier; Lemasson, Benjamin; Debacker, Clément; Farion, Régine; Rémy, Chantai; Barbier, Emmanuel Luc
• Is Part Of: Journal of cerebral blood flow & metabolism. Volume 34:Issue 8(2014)
• Page Start: 1354
• Page End: 1362
• Publication Date: 2014-08
• Subjects: brain -- glioma -- MRI perfusion -- pattern recognition -- radiomics -- unsupervised clustering; Cerebral circulation -- Periodicals; Brain -- Metabolism -- Periodicals; Brain -- Blood-vessels -- Periodicals; Cerebrovascular disease -- Periodicals; 612.824
• DOI: 10.1038/jcbfm.2014.90
• Languages: English
• ISSNs: 0271-678X

Abstract

Imaging heterogeneous cancer lesions is a real challenge. For diagnosis, histology often remains the reference, but it is widely acknowledged that biopsies are not reliable. There is thus a strong interest in establishing a link between clinical in vivo imaging and the biologic properties of tissues. In this study, we propose to construct histology-resembling images based on tissue microvascularization, a magnetic resonance imaging (MRI) accessible source of contrast. To integrate the large amount of information collected with microvascular MRI, we combined a manual delineation of a spatial region of interest with an unsupervised, model-based cluster analysis (Mclust). This approach was applied to two rat models of glioma (C6 and F98). Six MRI parameters were mapped: apparent diffusion coefficient, vessel wall permeability, cerebral blood volume fraction, cerebral blood flow, tissular oxygen saturation, and cerebral metabolic rate of oxygen. Five clusters, defined by their MRI features, were found to correspond to specific histologic features, and revealed intratumoral spatial structures. These results suggest that the presence of a cluster within a tumor can be used to assess the presence of a tissue type. In addition, the cluster composition, i.e., a signature of the intratumoral structure, could be used to characterize tumor models as histology does.