PURPOSE: @entity463 ( @entity463 ) and @entity75 (MET) are the most common intra-axial @entity1313 in adults and often pose a diagnostic dilemma using standard clinical MRI. These @entity5 types require different oncological and surgical management, which subsequently influence prognosis and clinical outcome. METHODS: Here, we hypothesize that @entity463 and MET possess different three-dimensional (3D) morphological attributes based on their physical characteristics. A 3D morphological analysis was applied on the @entity5 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 @entity5 surface and used to build a morphometric model of @entity463 and MET pathology with the goal of developing a @entity5 classification method based on shape characteristics. RESULTS: Our 3D morphometric method was applied on 48 untreated @entity1313 @entity1 . 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. CONCLUSION: The proposed 3D morphometric analysis framework can be applied to distinguish GBMs from solitary METs. Magn Reson Med, 2015.    2015 Wiley Periodicals, Inc.

Morphometric model for discrimination between XXXX multiforme and solitary metastasis using three-dimensional shape analysis.

@entity1 :: ('9606', 'Species') :: ['patients'] @entity1313 :: ('MESH:D001932', 'Disease') :: ['brain tumor', 'brain neoplasms'] @entity75 :: ('MESH:D001927', 'Disease') :: ['brain metastasis'] @entity5 :: ('MESH:D009369', 'Disease') :: ['tumor'] @entity463 :: ('MESH:D005909', 'Disease') :: ['GBM', 'Glioblastoma multiforme']
@entity463 :: (MESH:D005909,Disease) :: ['glioblastoma']
