A Multi-parametric MRI-Based Radiomics Signature and a Practical ML Model for Stratifying Glioblastoma Patients Based on Survival Toward Precision Oncology

Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme

(2019) Xin Chen et al.   ACADEMIC RADIOLOGY

Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation

(2019) Ahmad Chaddad et al.   Frontiers in Oncology

The effect of glioblastoma heterogeneity on survival stratification: a multimodal MR imaging texture analysis

(2018) Yang Liu et al.   ACTA RADIOLOGICA

Variable selection - A review and recommendations for the practicing statistician

(2018) Georg Heinze et al.   BIOMETRICAL JOURNAL

Prediction of survival with multi-scale radiomic analysis in glioblastoma patients

(2018) Ahmad Chaddad et al.   MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING

Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1

(2018) Saima Rathore et al.   Scientific Reports

Novel Radiomic Features based on Joint Intensity Matrices for Predicting Glioblastoma Patient Survival Time

(2018) Ahmad Chaddad et al.   IEEE Journal of Biomedical and Health Informatics

Overall survival prediction in glioblastoma multiforme patients from volumetric, shape and texture features using machine learning

(2018) Parita Sanghani et al.   SURGICAL ONCOLOGY-OXFORD

Radiomic Analysis Reveals Prognostic Information in T1-Weighted Baseline Magnetic Resonance Imaging in Patients With Glioblastoma

(2017) Michael Ingrisch et al.   INVESTIGATIVE RADIOLOGY

Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma

(2017) Philipp Kickingereder et al.   NEURO-ONCOLOGY

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

(2017) Spyridon Bakas et al.   Scientific Data

A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme

(2017) Jiangwei Lao et al.   Scientific Reports

A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome

(2016) Ahmad Chaddad et al.   BRITISH JOURNAL OF RADIOLOGY

Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings

(2016) Prateek Prasanna et al.   EUROPEAN RADIOLOGY

Radiomics: Images Are More than Pictures, They Are Data

(2016) Robert J. Gillies et al.   RADIOLOGY

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

(2015) Bjoern H. Menze et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma

(2015) Dalu Yang et al.   MEDICAL PHYSICS

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques

(2015) Luke Macyszyn et al.   NEURO-ONCOLOGY

Molecular and cellular heterogeneity: the hallmark of glioblastoma

(2014) Diane J. Aum et al.   Neurosurgical Focus

Exciting New Advances in Neuro-Oncology: The Avenue to a Cure for Malignant Glioma

(2010) E. G. Van Meir et al.   CA-A CANCER JOURNAL FOR CLINICIANS