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Martin J. van den Bent et al.
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WHO 2016 Classification of gliomas
P. Wesseling et al.
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Glioblastoma and primary central nervous system lymphoma: Preoperative differentiation by using MRI-based 3D texture analysis
Dong-Dong Xiao et al.
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Jill M. Abrigo et al.
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Alberto Dellacasa Bellingegni et al.
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Adrian Ion-Margineanu et al.
FRONTIERS IN NEUROSCIENCE (2017)
The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary
David N. Louis et al.
ACTA NEUROPATHOLOGICA (2016)
A Very Simple Safe-Bayesian Random Forest
Novi Quadrianto et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2015)
Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma
Leland S. Hu et al.
PLOS ONE (2015)
A random forest classifier for lymph diseases
Ahmad Taher Azar et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2014)
CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2007-2011
Quinn T. Ostrom et al.
NEURO-ONCOLOGY (2014)
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Erik Scheme et al.
JOURNAL OF REHABILITATION RESEARCH AND DEVELOPMENT (2011)
Volumes and growth rates of untreated adult low-grade gliomas indicate risk of early malignant transformation
Jeremy Rees et al.
EUROPEAN JOURNAL OF RADIOLOGY (2009)
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Joseph M. Piepmeier
JOURNAL OF NEURO-ONCOLOGY (2009)
Prognostic significance of imaging contrast enhancement for WHO grade II gliomas
Johan Pallud et al.
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Non-enhancing de novo glioblastoma: report of two cases
AA Cohen-Gadol et al.
NEUROSURGICAL REVIEW (2004)
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