Robust association between vascular habitats and patient prognosis in glioblastoma: An international multicenter study
暂无分享,去创建一个
Carlos Sáez | Luaba Tshibanda | Girolamo Crisi | Didier Martin | Alex Rovira | Raquel Faubel | Sabina Asensio-Cuesta | Paulina Due-Tønnessen | Javier Juan-Albarracín | Juan M García-Gómez | Elies Fuster-Garcia | Kyrre E Emblem | Cristina Auger | Germán A. García-Ferrando | Fernando Aparici-Robles | Laura Oleaga | Torstein R Meling | Eduard Chelebian | À. Rovira | P. Due-Tønnessen | C. Botella | S. Filice | L. Tshibanda | Didier Martin | R. Faubel | C. Sáez | J. M. García-Gómez | K. Emblem | T. Meling | G. Crisi | A. Revert | C. Auger | J. Muñoz-Langa | J. Juan-Albarracín | E. Fuster-García | L. Oleaga | María Del Mar Álvarez-Torres | Fuensanta Bellvís-Bataller | David Lorente | Gaspar Reynés | Jaime Font de Mora | Carlos Botella | Jose Muñoz-Langa | Germán A García-Ferrando | Jose Pineda | Enrique Mollà-Olmos | Antonio J Revert | Silvano Filice | S. Asensio-Cuesta | Eduard Chelebian | J. Font de Mora | G. Reynés | F. Aparici-Robles | D. Lorente | M. D. M. Álvarez-Torres | Fuensanta Bellvís–Bataller | Jose Pineda | E. Mollá-Olmos
[1] Á. Alberich-Bayarri. Imaging Biomarkers , 2020 .
[2] Seok-Gu Kang,et al. Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction. , 2018, Radiology.
[3] Luis Martí-Bonmatí,et al. Improving the estimation of prognosis for glioblastoma patients by MR based hemodynamic tissue signatures , 2018, NMR in biomedicine.
[4] Luis Martí-Bonmatí,et al. Glioblastoma: Vascular Habitats Detected at Preoperative Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging Predict Survival. , 2018, Radiology.
[5] Baris Turkbey,et al. Delineation of Tumor Habitats based on Dynamic Contrast Enhanced MRI , 2017, Scientific Reports.
[6] T. Petrova,et al. Microenvironmental regulation of tumour angiogenesis , 2017, Nature Reviews Cancer.
[7] S. McLachlan,et al. Life beyond a diagnosis of glioblastoma: a systematic review of the literature , 2017, Journal of Cancer Survivorship.
[8] H. Urbach,et al. Mesoscopic imaging of glioblastomas: Are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype? , 2017, The neuroradiology journal.
[9] Steven D Chang,et al. Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment , 2016, Neuro-oncology.
[10] Stuart A. Taylor,et al. Imaging biomarker roadmap for cancer studies , 2016, Nature Reviews Clinical Oncology.
[11] Carole Dufouil,et al. Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease , 2016, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.
[12] G. Reifenberger,et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary , 2016, Acta Neuropathologica.
[13] A. Mishra,et al. Glioma Recurrence Versus Radiation Necrosis: Single-Session Multiparametric Approach Using Simultaneous O-(2-18F-Fluoroethyl)-L-Tyrosine PET/MRI , 2016, Clinical nuclear medicine.
[14] Lei Xing,et al. Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images. , 2016, Radiology.
[15] 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.
[16] G. Bergers,et al. Glioblastoma: Defining Tumor Niches. , 2015, Trends in cancer.
[17] Deric M. Park,et al. The Evidence of Glioblastoma Heterogeneity , 2015, Scientific Reports.
[18] Thomas E Yankeelov,et al. Methods and challenges in quantitative imaging biomarker development. , 2015, Academic radiology.
[19] Dafna Ben Bashat,et al. Differentiation between vasogenic-edema versus tumor-infiltrative area in patients with glioblastoma during bevacizumab therapy: a longitudinal MRI study. , 2014, European journal of radiology.
[20] Luke Macyszyn,et al. Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity. , 2014, Radiology.
[21] 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.
[22] Michael L Mumert,et al. Preoperative dynamic contrast-enhanced MRI correlates with molecular markers of hypoxia and vascularity in specific areas of intratumoral microenvironment and is predictive of patient outcome. , 2014, Neuro-oncology.
[23] D. Cheresh,et al. Tumor angiogenesis: molecular pathways and therapeutic targets , 2011, Nature Medicine.
[24] J. Raizer,et al. Glioblastoma: a method for predicting response to antiangiogenic chemotherapy by using MR perfusion imaging--pilot study. , 2010, Radiology.
[25] 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.
[26] T. Hirai,et al. Prognostic Value of Perfusion MR Imaging of High-Grade Astrocytomas: Long-Term Follow-Up Study , 2008, American Journal of Neuroradiology.
[27] B. Scheithauer,et al. The 2007 WHO classification of tumours of the central nervous system , 2007, Acta Neuropathologica.
[28] Martin J. van den Bent,et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. , 2005, The New England journal of medicine.
[29] Tyrone D. Cannon,et al. Reliability of brain volumes from multicenter MRI acquisition: A calibration study , 2004, Human brain mapping.
[30] Glyn Johnson,et al. Relative cerebral blood volume measurements in intracranial mass lesions: interobserver and intraobserver reproducibility study. , 2002, Radiology.