Gaussian mixture model-based classification of dynamic contrast enhanced MRI data for identifying diverse tumor microenvironments: Preliminary results Journal Article

   

Authors:	Han, S. H.; Ackerstaff, E.; Stoyanova, R.; Carlin, S.; Huang, W.; Koutcher, J. A.; Kim, J. K.; Cho, G.; Jang, G.; Cho, H.
Article Title:	Gaussian mixture model-based classification of dynamic contrast enhanced MRI data for identifying diverse tumor microenvironments: Preliminary results
Abstract:	Tumor hypoxia develops heterogeneously, affects radiation sensitivity and the development of metastases. Prognostic information derived from the in vivo characterization of the spatial distribution of hypoxic areas in solid tumors can be of value for radiation therapy planning and for monitoring the early treatment response. Tumor hypoxia is caused by an imbalance between the supply and consumption of oxygen. The tumor oxygen supply is inherently linked to its vasculature and perfusion which can be evaluated by dynamic contrast enhanced (DCE-) MRI using the contrast agent Gd-DTPA. Thus, we hypothesize that DCE-MRI data may provide surrogate information regarding tumor hypoxia. In this study, DCE-MRI data from a rat prostate tumor model were analysed with a Gaussian mixture model (GMM)-based classification to identify perfused, hypoxic and necrotic areas for a total of ten tumor slices from six rats, of which one slice was used as training data for GMM classifications. The results of pattern recognition analyzes were validated by comparison to corresponding Akep maps defining the perfused area (0.84±0.09 overlap), hematoxylin and eosin (H&E)-stained tissue sections defining necrosis (0.64±0.15 overlap) and pimonidazole-stained sections defining hypoxia (0.72±0.17 overlap), respectively. Our preliminary data indicate the feasibility of a GMM-based classification to identify tumor hypoxia, necrosis and perfusion/permeability from non-invasively acquired, in vivo DCE-MRI data alone, possibly obviating the need for invasive procedures, such as biopsies, or exposure to radioactivity, such as positron emission tomography (PET) exams. © 2013 John Wiley & Sons, Ltd.
Keywords:	immunohistochemistry; controlled study; histopathology; nonhuman; nuclear magnetic resonance imaging; positron emission tomography; magnetic resonance imaging; animal cell; animals; animal tissue; cell death; classification; image analysis; tumor volume; animal experiment; animal model; cell line, tumor; necrosis; radiation exposure; hypoxia; prostatic neoplasms; drug uptake; image enhancement; tumors; contrast enhancement; pattern recognition, automated; prostate tumor; rat; prostate biopsy; radioactivity; urology; contrast media; pimonidazole; cell hypoxia; rats; rattus; gaussian mixture model; gadolinium pentetate; statistical model; normal distribution; dce-mri; oxygen supply; positron emission tomography (pet); cell membrane permeability; tumor microenvironment; gadolinium pentetate meglumine; pattern recognition; classification (of information); biological monitoring; preclinical prostate model; tumor microenvironments; dynamic contrast enhanced; dynamic contrast enhanced mri; model-based classifications; dynamic contrast enhancement
Journal Title:	NMR in Biomedicine
Volume:	26
Issue:	5
ISSN:	0952-3480
Publisher:	John Wiley & Sons
Date Published:	2013-05-01
Start Page:	519
End Page:	532
Language:	English
DOI:	10.1002/nbm.2888
PROVIDER:	scopus
PUBMED:	23440683
PMCID:	PMC3706205
DOI/URL:	http://www.scopus.com/inward/record.url?eid=2-s2.0-84876802376&partnerID=40&md5=b9bb71f0426ca07b16dd8ab38219e48f
Notes:	--- - "Export Date: 3 June 2013" - "CODEN: NMRBE" - "Source: Scopus"

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