Cbtrus statistical report: primary brain and central nervous system tumors diagnosed in the united states in, Neuro-oncology, vol.14, pp.1-49, 2012. ,
The 2007 who classi-fication of tumours of the central nervous system, Acta neuropathologica, vol.114, issue.2, p.97109, 2007. ,
Long-term survival with glioblastoma multiforme, Brain, vol.130, issue.10, p.25962606, 2007. ,
DOI : 10.1093/brain/awm204
Current concepts and management of glioblastoma, Annals of Neurology, vol.91, issue.2, p.921, 2011. ,
DOI : 10.1002/ana.22425
Mr imaging predictors of molecular profile and survival: multi-institutional study of the tcga glioblastoma data set, Radiology, vol.267, issue.2, p.560569, 2013. ,
multiforme: Exploratory radio-genomic analysis by using quantitative image features, Radiology, 2014. ,
Computer-extracted mr imaging features are associated with survival in glioblastoma pa-tients, Journal of neuro-oncology, p.16, 2014. ,
A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival, Journal of Neurosurgery, vol.95, issue.2, 2001. ,
DOI : 10.3171/jns.2001.95.2.0190
Quantification of Heterogeneity as a Biomarker in Tumor Imaging: A Systematic Review, PLoS ONE, vol.23, issue.10, pp.10-110300, 2014. ,
DOI : 10.1371/journal.pone.0110300.s006
Dimitris Visvikis, and Catherine Cheze Le Rest, Visual versus quantitative assessment of intratumor 18f-fdg pet uptake heterogeneity: Prognostic value in nonsmall cell lung cancer, Journal of Nuclear Medicine, p.113, 2014. ,
Jean-Philippe Met-ges, Laurent Corcos, and Dimitris Visvikis, Intratumor heterogeneity characterized by textural features on base-line 18f-fdg pet images predicts response to concomitant radiochemotherapy in esophageal cancer, Journal of Nuclear Medicine, vol.52, issue.3, p.369378, 2011. ,
Classification of brain tumor type and grade using mri texture and shape in a machine learning scheme, Magnetic Resonance in Medicine, vol.62, issue.6, p.16091618, 2009. ,
Gene selection for cancer classification using support vector machines, Machine learning, p.389422, 2002. ,
The ITK Software Guide, Kitware, Inc, 2005. ,
Mutual-information-based registration of medical images: a survey, Medical Imaging, IEEE Transactions on, vol.22, issue.8, p.9861004, 2003. ,
Multi-Modal Glioblastoma Segmentation: Man versus Machine, PLoS ONE, vol.119, issue.5, p.96873, 2014. ,
DOI : 10.1371/journal.pone.0096873.s001
Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regulariza-tion, Medical Image Computing and Computer-Assisted Intervention MICCAI 2011, p.354361, 2011. ,
Karthikeyan Shanmugam, and Its Hak Dinstein, Textural features for image classification, Systems, Man and Cybernetics, IEEE Transactions on, issue.6, p.610621, 1973. ,
The analysis of natural textures using run length features, Industrial Electronics, IEEE Transactions on, vol.35, issue.2, p.323328, 1988. ,
Texture indexes and grey level size zone matrix application to cell nuclei classification, Pattern Recognition and Information Processing, 2009. ,
Metrics and textural features of mri diffusion to improve classification of pediatric posterior fossa tumors, American Journal of Neuroradiology, vol.355, pp.1009-1015, 2014. ,
An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM, Expert Systems with Applications, vol.37, issue.10, pp.6737-6741, 2010. ,
DOI : 10.1016/j.eswa.2010.02.067
A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities, Physics in Medicine and Biology, vol.60, issue.14, pp.14-5471, 2015. ,
DOI : 10.1088/0031-9155/60/14/5471
Sparse kernel methods for high-dimensional survival data, Bioinformatics, vol.24, issue.14, pp.1632-1638, 2008. ,
DOI : 10.1093/bioinformatics/btn253