Related references
Note: Only part of the references are listed.A radiomics nomogram based on multiparametric MRI might stratify glioblastoma patients according to survival
Xi Zhang et al.
EUROPEAN RADIOLOGY (2019)
Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Veronika Cheplygina et al.
MEDICAL IMAGE ANALYSIS (2019)
Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation
Ahmad Chaddad et al.
FRONTIERS IN ONCOLOGY (2019)
A sparse representation-based radiomics for outcome prediction of higher grade gliomas
Guoqing Wu et al.
MEDICAL PHYSICS (2019)
Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma
Philipp Kickingereder et al.
NEURO-ONCOLOGY (2018)
The effect of glioblastoma heterogeneity on survival stratification: a multimodal MR imaging texture analysis
Yang Liu et al.
ACTA RADIOLOGICA (2018)
Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma
Niha Beig et al.
SCIENTIFIC REPORTS (2018)
Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches
M. Zhou et al.
AMERICAN JOURNAL OF NEURORADIOLOGY (2018)
Baseline pretreatment contrast enhancing tumor volume including central necrosis is a prognostic factor in recurrent glioblastoma: evidence from single- and multicenter trials
Benjamin M. Ellingson et al.
NEURO-ONCOLOGY (2017)
Radiogenomics to characterize regional genetic heterogeneity in glioblastoma
Leland S. Hu et al.
NEURO-ONCOLOGY (2017)
Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma
Yi Cui et al.
EUROPEAN RADIOLOGY (2017)
Multiparametric MR Imaging of Diffusion and Perfusion in Contrast-enhancing and Nonenhancing Components in Patients with Glioblastoma
Natalie R. Boonzaier et al.
RADIOLOGY (2017)
Data Descriptor: Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
Spyridon Bakas et al.
SCIENTIFIC DATA (2017)
Relationship between necrotic patterns in glioblastoma and patient survival: fractal dimension and lacunarity analyses using magnetic resonance imaging
Shuai Liu et al.
SCIENTIFIC REPORTS (2017)
A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
Shaoxu Wu et al.
CLINICAL CANCER RESEARCH (2017)
Relationship between Glioblastoma Heterogeneity and Survival Time: An MR Imaging Texture Analysis
Y. Liu et al.
AMERICAN JOURNAL OF NEURORADIOLOGY (2017)
Radiological features combined with IDH1 status for predicting the survival outcome of glioblastoma patients
Kai Wang et al.
NEURO-ONCOLOGY (2016)
Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models
Philipp Kickingereder et al.
RADIOLOGY (2016)
Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images
Yi Cui et al.
RADIOLOGY (2016)
Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor
Rajan Jain et al.
RADIOLOGY (2014)
Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features
Olivier Gevaert et al.
RADIOLOGY (2014)
Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics
Andrea Sottoriva et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2013)
Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
Marco Gerlinger et al.
NEW ENGLAND JOURNAL OF MEDICINE (2012)
Exciting New Advances in Neuro-Oncology The Avenue to a Cure for Malignant Glioma
Erwin G. Van Meir et al.
CA-A CANCER JOURNAL FOR CLINICIANS (2010)
Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma
R Stupp et al.
NEW ENGLAND JOURNAL OF MEDICINE (2005)
An efficient k-means clustering algorithm:: Analysis and implementation
T Kanungo et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2002)