title: Morphometric model for discrimination between glioblastoma multiforme and solitary metastasis using three-dimensional shape analysis.
abstract: Glioblastoma multiforme (GBM) and brain metastasis (MET) are the most common intra-axial brain neoplasms in adults and often pose a diagnostic dilemma using standard clinical MRI. These tumor types require different oncological and surgical management, which subsequently influence prognosis and clinical outcome.
  
  
  
  Here, we hypothesize that GBM and MET possess different three-dimensional (3D) morphological attributes based on their physical characteristics. A 3D morphological analysis was applied on the tumor surface defined by our diffusion tensor imaging (DTI) segmentation technique. It segments the DTI data into clusters representing different isotropic and anisotropic water diffusion characteristics, from which a distinct surface boundary between healthy and pathological tissue was identified. Morphometric features of shape index and curvedness were then computed for each tumor surface and used to build a morphometric model of GBM and MET pathology with the goal of developing a tumor classification method based on shape characteristics.
  
  
  
  Our 3D morphometric method was applied on 48 untreated brain tumor patients. Cross-validation resulted in a 95.8% accuracy classification with only two shape features needed and that can be objectively derived from quantitative imaging methods.
  
  
  
  The proposed 3D morphometric analysis framework can be applied to distinguish GBMs from solitary METs. Magn Reson Med 75:2505-2516, 2016. © 2015 Wiley Periodicals, Inc.
authors.affiliation: Neuroscience Research Centre, Cardiovascular and Cell Sciences Institute, St. George's, University of London, London, United Kingdom.
authors.forename: Guang
authors.initials: G
authors.lastname: Yang
authors.affiliation: Neuroscience Research Centre, Cardiovascular and Cell Sciences Institute, St. George's, University of London, London, United Kingdom.
authors.forename: Timothy L
authors.initials: TL
authors.lastname: Jones
authors.affiliation: Neuroscience Research Centre, Cardiovascular and Cell Sciences Institute, St. George's, University of London, London, United Kingdom.
authors.forename: Franklyn A
authors.initials: FA
authors.lastname: Howe
authors.affiliation: Neuroscience Research Centre, Cardiovascular and Cell Sciences Institute, St. George's, University of London, London, United Kingdom.
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Barrick
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging / Q000473:pathology; D001932:Brain Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005909:Glioblastoma / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated
keywords: brain tumor classification; computer-aided diagnosis; diffusion tensor imaging segmentation; magnetic resonance imaging; pattern recognition and classification; shape analysis