Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization

Assessment of Heterogeneity Difference Between Edge and Core by Using Texture Analysis

(2016) Shiteng Suo et al.   ACADEMIC RADIOLOGY

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival

(2016) David Molina et al.   BRITISH JOURNAL OF RADIOLOGY

Influence of gray level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images

(2016) David Molina et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma

(2016) Jia Liu et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Applications and limitations of radiomics

(2016) Stephen S F Yip et al.   PHYSICS IN MEDICINE AND BIOLOGY

Texture Feature Ratios from Relative CBV Maps of Perfusion MRI Are Associated with Patient Survival in Glioblastoma

(2015) J. Lee et al.   AMERICAN JOURNAL OF NEURORADIOLOGY

Magnetic resonance imaging texture analysis classification of primary breast cancer

(2015) S. A. Waugh et al.   EUROPEAN RADIOLOGY

Prognostic Value of Computed Tomography Texture Features in Non–Small Cell Lung Cancers Treated With Definitive Concomitant Chemoradiotherapy

(2015) Su Yeon Ahn et al.   INVESTIGATIVE RADIOLOGY

Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI

(2015) Andrés Larroza et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Tumor Texture Analysis in PET: Where Do We Stand?

(2015) I. Buvat et al.   JOURNAL OF NUCLEAR MEDICINE

The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis

(2015) Ralph T.H. Leijenaar et al.   Scientific Reports

Improving tumour heterogeneity MRI assessment with histograms

(2014) N Just   BRITISH JOURNAL OF CANCER

Radiogenomics and Imaging Phenotypes in Glioblastoma: Novel Observations and Correlation with Molecular Characteristics

(2014) Benjamin M. Ellingson   Current Neurology and Neuroscience Reports

Tumor Texture Analysis in 18F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis

(2014) F. Orlhac et al.   JOURNAL OF NUCLEAR MEDICINE

Quantification of Heterogeneity as a Biomarker in Tumor Imaging: A Systematic Review

(2014) Lejla Alic et al.   PLoS One

On some misconceptions about tumor heterogeneity quantification

(2013) Frank J. Brooks   EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

(2013) Adrien Depeursinge et al.   MEDICAL IMAGE ANALYSIS

Reproducibility of Tumor Uptake Heterogeneity Characterization Through Textural Feature Analysis in 18F-FDG PET

(2012) F. Tixier et al.   JOURNAL OF NUCLEAR MEDICINE

Radiomics: the process and the challenges

(2012) Virendra Kumar et al.   MAGNETIC RESONANCE IMAGING

The influence of field strength and different clinical breast MRI protocols on the outcome of texture analysis using foam phantoms

(2011) Shelley A. Waugh et al.   MEDICAL PHYSICS

Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage

(2011) Balaji Ganeshan et al.   CANCER IMAGING

Texture Analysis: A Review of Neurologic MR Imaging Applications

(2010) A. Kassner et al.   AMERICAN JOURNAL OF NEURORADIOLOGY

Robust texture features for response monitoring of glioblastoma multiforme on T1-weighted and T2-FLAIR MR images: A preliminary investigation in terms of identification and segmentation

(2010) Dawit Assefa et al.   MEDICAL PHYSICS

Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: An application-oriented study

(2009) Marius E. Mayerhoefer et al.   MEDICAL PHYSICS

Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods

(2008) Pantelis Georgiadis et al.   MAGNETIC RESONANCE IMAGING