Higher vascularity at infiltrated peripheral edema differentiates proneural glioblastoma subtype
暂无分享,去创建一个
J. M. García-Gómez | J. Juan-Albarracín | E. Fuster-García | Eduard Chelebian | María del Mar Álvarez-Torres
[1] Elies Fuster-García,et al. Non-local spatially varying finite mixture models for image segmentation , 2021, Statistics and Computing.
[2] Carlos Sáez,et al. Robust association between vascular habitats and patient prognosis in glioblastoma: An international multicenter study , 2020, Journal of magnetic resonance imaging : JMRI.
[3] Elies Fuster-García,et al. ONCOhabitats Glioma Segmentation Model , 2019, BrainLes@MICCAI.
[4] Juan Miguel García-Gómez,et al. ONCOhabitats: A system for glioblastoma heterogeneity assessment through MRI , 2019, Int. J. Medical Informatics.
[5] L. Cerchia,et al. Proneural-Mesenchymal Transition: Phenotypic Plasticity to Acquire Multitherapy Resistance in Glioblastoma , 2019, International journal of molecular sciences.
[6] G. Finocchiaro,et al. The landscape of the mesenchymal signature in brain tumours , 2019, Brain : a journal of neurology.
[7] U. Klose,et al. In Vivo Molecular Profiling of Human Glioma , 2019, Clinical Neuroradiology.
[8] Luis Martí-Bonmatí,et al. Improving the estimation of prognosis for glioblastoma patients by MR based hemodynamic tissue signatures , 2018, NMR in biomedicine.
[9] Mary Goldman,et al. The UCSC Xena platform for public and private cancer genomics data visualization and interpretation , 2018, bioRxiv.
[10] A. Bozzao,et al. Prediction of survival in patients affected by glioblastoma: histogram analysis of perfusion MRI , 2018, Journal of Neuro-Oncology.
[11] Luis Martí-Bonmatí,et al. Glioblastoma: Vascular Habitats Detected at Preoperative Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging Predict Survival. , 2018, Radiology.
[12] Songhua Zhan,et al. Noninvasively detecting Isocitrate dehydrogenase 1 gene status in astrocytoma by dynamic susceptibility contrast MRI , 2017, Journal of magnetic resonance imaging : JMRI.
[13] Gang Wang,et al. Diffusion and perfusion weighted magnetic resonance imaging for tumor volume definition in radiotherapy of brain tumors , 2016, Radiation Oncology.
[14] G. Reifenberger,et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary , 2016, Acta Neuropathologica.
[15] Yu Yao,et al. Isocitrate Dehydrogenase (IDH)1/2 Mutations as Prognostic Markers in Patients With Glioblastomas , 2016, Medicine.
[16] M. McLean,et al. Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas , 2015, Journal of magnetic resonance imaging : JMRI.
[17] A. Vortmeyer,et al. Integrated genomic characterization of IDH1-mutant glioma malignant progression , 2015, Nature Genetics.
[18] X. Bian,et al. Lower MGMT expression predicts better prognosis in proneural-like glioblastoma. , 2015, International journal of clinical and experimental medicine.
[19] R. Bourgon,et al. Patients With Proneural Glioblastoma May Derive Overall Survival Benefit From the Addition of Bevacizumab to First-Line Radiotherapy and Temozolomide: Retrospective Analysis of the AVAglio Trial. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[20] José V. Manjón,et al. Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification , 2015, PloS one.
[21] Peter Canoll,et al. MRI-localized biopsies reveal subtype-specific differences in molecular and cellular composition at the margins of glioblastoma , 2014, Proceedings of the National Academy of Sciences.
[22] Scott N. Hwang,et al. Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. , 2014, Radiology.
[23] K. Aldape,et al. Using the molecular classification of glioblastoma to inform personalized treatment , 2014, The Journal of pathology.
[24] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[25] D. Haussler,et al. The Somatic Genomic Landscape of Glioblastoma , 2013, Cell.
[26] S. Choi,et al. Cerebral Blood Volume Calculated by Dynamic Susceptibility Contrast-Enhanced Perfusion MR Imaging: Preliminary Correlation Study with Glioblastoma Genetic Profiles , 2013, PloS one.
[27] Benjamin M Ellingson,et al. Identifying the mesenchymal molecular subtype of glioblastoma using quantitative volumetric analysis of anatomic magnetic resonance images. , 2013, Neuro-oncology.
[28] David Gutman,et al. Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers. , 2013, Radiology.
[29] David T. W. Jones,et al. Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. , 2012, Cancer cell.
[30] T. Cloughesy,et al. Relationship between Tumor Enhancement, Edema, IDH1 Mutational Status, MGMT Promoter Methylation, and Survival in Glioblastoma , 2012, American Journal of Neuroradiology.
[31] A. Viale,et al. IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype , 2012, Nature.
[32] F. Jolesz,et al. Correction: Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme , 2012, PLoS ONE.
[33] J. Uhm. IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype , 2012 .
[34] Ferenc A. Jolesz,et al. Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme , 2011, PloS one.
[35] Joel H. Saltz,et al. The Proneural Molecular Signature Is Enriched in Oligodendrogliomas and Predicts Improved Survival among Diffuse Gliomas , 2010, PloS one.
[36] R. Wilson,et al. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. , 2010, Cancer cell.
[37] S. Gabriel,et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. , 2010, Cancer cell.
[38] Ru-Fang Yeh,et al. Glioblastoma multiforme regional genetic and cellular expression patterns: influence on anatomic and physiologic MR imaging. , 2010, Radiology.
[39] R. Arceci. Identification of a CpG Island Methylator Phenotype that Defines a Distinct Subgroup of Glioma , 2010 .
[40] A. Falini,et al. Tumours , 2008, Neurological Sciences.
[41] T. Hirai,et al. Prognostic Value of Perfusion MR Imaging of High-Grade Astrocytomas: Long-Term Follow-Up Study , 2008, American Journal of Neuroradiology.
[42] Douglas C. Miller,et al. Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. , 2008, Radiology.
[43] B. Scheithauer,et al. The 2007 WHO classification of tumours of the central nervous system , 2007, Acta Neuropathologica.
[44] D. Louis. WHO classification of tumours of the central nervous system , 2007 .
[45] R M Weisskoff,et al. Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. , 2006, AJNR. American journal of neuroradiology.
[46] A. Norman,et al. in patients with , 1995 .