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References (132)
-
Depeursinge (2014)
Three-dimensional solid texture analysis in biomedical imaging: review and opportunitiesMed Image Anal, 18
-
Choi (2012)
2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomasNat Med, 18
-
Kansagra (2016)
Big data and the future of radiology informaticsAcad Radiol, 23
-
Nardone (2016)
Prognostic value of MR imaging texture analysis in brain non-small cell lung cancer oligo-metastases undergoing stereotactic irradiationCureus, 8
-
Haralick (1973)
Textural features for image classificationIEEE Transact Syst Man Cybernet, SMC-3
-
(2015)
Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer, 5
-
Zacharaki (2009)
Classification of brain tumor type and grade using MRI texture and shape in a machine learning schemeMagn Reson Med, 62
-
Georgiadis (2011)
Quantitative combination of volumetric MR imaging and MR spectroscopy data for the discrimination of meningiomas from metastatic brain tumors by means of pattern recognitionMagn Reson Imaging, 29
-
Posner (1978)
Intracranial metastases from systemic cancerAdv Neurol, 19
-
Grove (2015)
Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinomaPLoS One, 10
-
Kassner (2010)
Texture analysis: a review of neurologic MR imaging applicationsAJNR Am J Neuroradiol, 31
-
(2006)
Machine learning: a review of classification and combining techniques -
Law (2008)
Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imagingRadiology, 247
-
(2009)
Implications for disease and therapy -
Eisenhauer (2009)
New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)Eur J Cancer, 45
-
Hassan (2016)
Radiomic texture analysis mapping predicts areas of true functional MRI activitySci Rep, 6
-
Tofts (1997)
Modeling tracer kinetics in dynamic Gd-DTPA MR imagingJ Magn Reson Imaging, 7
-
Butowski (2015)
Epidemiology and diagnosis of brain tumorsContinuum (Minneap Minn, 21
-
Chen (2005)
[Correlation between magnetic resonance diffusion weighted imaging and cell density in astrocytoma]Zhonghua Zhong Liu Za Zhi, 27
-
Warfield (2008)
Validation of image segmentation by estimating rater bias and variancePhilos Trans A Math Phys Eng Sci, 366
-
(2012)
1.5T versus 3.0T MRI texture analysis in the normal appearing white matter of multiple sclerosis patients, 20
-
Devos (2005)
The use of multivariate MR imaging intensities versus metabolic data from MR spectroscopic imaging for brain tumour classificationJ Magn Reson, 173
-
Despotovic (2015)
MRI segmentation of the human brain: challenges, methods, and applicationsComput Math Methods Med, 2015
-
Wen (2010)
Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working groupJ Clin Oncol, 28
-
Ryu (2014)
Glioma: application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneityPLoS One, 9
-
Weizman (2014)
Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studiesMed Phys, 41
-
Nelson (2009)
Data sharing: empty archivesNature, 461
-
Brynolfsson (2014)
ADC texture—an imaging biomarker for high-grade glioma?Med Phys, 41
-
Gillies (2016)
Radiomics: images are more than pictures, they are dataRadiology, 278
-
Neck radiotherapy, dynamic contrast-enhanced MRI detects acute radiotherapy-induced alterations in mandibular microvasculature: prospective assessment of imaging biomarkers of normal tissue injury.
Sci Rep, 6
-
Santarosa (2016)
Dynamic contrast-enhanced and dynamic susceptibility contrast perfusion MR imaging for glioma grading: preliminary comparison of vessel compartment and permeability parameters using hotspot and histogram analysisEur J Radiol, 85
-
Eddy (2012)
Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7Med Decis Making, 32
-
Guzman-De-Villoria (2014)
Added value of advanced over conventional magnetic resonance imaging in grading gliomas and other primary brain tumorsCancer Imaging, 14
-
Guo (2011)
Enhanced CT images by the Wavelet transform improving diagnostic accuracy of chest nodulesJ Digit Imaging, 24
-
Lee (2016)
Texture feature ratios from rCBV maps of perfusion MRI are associated with patient survival in glioblastomaAJNR Am J Neuroradiol, 37
-
Zinn (2015)
Imaging genomics in gliomasCancer J, 21
-
Andronesi (2016)
Treatment response assessment in IDH-mutant glioma patients by noninvasive 3D functional spectroscopic mapping of 2-hydroxyglutarateClin Cancer Res, 22
-
Wangaryattawanich (2015)
Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survivalNeuro Oncol, 17
-
Kuo (2007)
Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinomaJ Vasc Interv Radiol, 18
-
Parmar (2015)
Machine learning methods for quantitative radiomic biomarkersSci Rep, 5
-
Wang (2007)
Classification of brain tumors using MRI and MRS dataProc of SPIE, 6514
-
Kono (2001)
The role of diffusion-weighted imaging in patients with brain tumorsAJNR Am J Neuroradiol, 22
-
Schad (1993)
MR tissue characterization of intracranial tumors by means of texture analysisMagn Reson Imaging, 11
-
Teruel (2014)
Dynamic contrast-enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancerNMR Biomed, 27
-
Raghupathi (2014)
Big data analytics in healthcare: promise and potentialHealth Inf Sci Syst, 2
-
Fetit (2015)
3D texture analysis of heterogeneous MRI data for diagnostic classification of childhood brain tumoursStud Health Technol Inform, 213
-
Mouthuy (2012)
Multiparametric magnetic resonance imaging to differentiate high-grade gliomas and brain metastasesJ Neuroradiol, 39
-
Nicolasjilwan (2015)
Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patientsJ Neuroradiol, 42
-
(2016)
Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging -
Gatenby (2013)
Quantitative imaging in cancer evolution and ecologyRadiology, 269
-
Choudhri (2015)
Intraoperative MRI in pediatric brain tumorsPediatr Radiol, 45
-
(2016)
Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas -
Rodriguez Gutierrez (2014)
Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumorsAm J Neuroradiol, 35
-
Kimura (2016)
Multiparametric MR imaging in the assessment of brain tumorsMagn Reson Imaging Clin N Am, 24
-
Li (2016)
Differentiating brain metastases from different pathological types of lung cancers using texture analysis of T1 postcontrast MRMagn Reson Med, 76
-
(1991)
Image Segmentation. Digital Image Processing: PIKS Inside -
Mayerhoefer (2009)
Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: an application-oriented studyMed Phys, 36
-
Olabarriaga (2001)
Interaction in the segmentation of medical images: a surveyMed Image Anal, 5
-
Moton (2015)
Imaging genomics of glioblastoma: biology, biomarkers, and breakthroughsTop Magn Reson Imaging, 24
-
Nash (2014)
Harnessing the power of big data in healthcareAm Health Drug Benefits, 7
-
Upadhyay (2011)
Conventional MRI evaluation of gliomasBrit J Radiol, 84
-
Gordillo (2013)
State of the art survey on MRI brain tumor segmentationMagn Reson Imaging, 31
-
Law (2003)
Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imagingAJNR Am J Neuroradiol, 24
-
Macdonald (1990)
Response criteria for phase II studies of supratentorial malignant gliomaJ Clin Oncol, 8
-
Atkins (1998)
Fully automatic segmentation of the brain in MRIIEEE Trans Med Imaging, 17
-
Hawkins (2014)
Predicting outcomes of nonsmall cell lung cancer using CT image featuresIEEE Access, 2
-
Zinn (2011)
Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiformePLoS One, 6
-
Wong (1998)
Correlation between dynamic MRI and outcome in patients with malignant gliomasNeurology, 50
-
Warren (2000)
Proton magnetic resonance spectroscopic imaging in children with recurrent primary brain tumorsJ Clin Oncol, 18
-
Korfiatis (2016)
MRI texture features as biomarkers to predict MGMT methylation status in glioblastomasMed Phys, 43
-
Khalilzadeh (2013)
Automatic segmentation of brain MRI in high-dimensional local and non-local feature space based on sparse representationMagn Reson Imaging, 31
-
Marx (2013)
Biology: the big challenges of big dataNature, 498
-
Gutman (2013)
MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data setRadiology, 267
-
Wong (2008)
Quality of life in brain metastases radiation trials: a literature reviewCurr Oncol, 15
-
(2011)
Big Data: The Next Frontier for Innovation, Competition and Productivity -
Kondziolka (1993)
Unreliability of contemporary neurodiagnostic imaging in evaluating suspected adult supratentorial (low-grade) astrocytomaJ Neurosurg, 79
-
Pope (2012)
Non-invasive detection of 2-hydroxyglutarate and other metabolites in IDH1 mutant glioma patients using magnetic resonance spectroscopyJ Neurooncol, 107
-
Brant-Zawadzki (1984)
Primary intracranial tumor imaging: a comparison of magnetic resonance and CTRadiology, 150
-
Colen (2014)
NCI Workshop Report: clinical and computational requirements for correlating imaging phenotypes with genomics signaturesTransl Oncol, 7
-
Helms (2016)
Segmentation of human brain using structural MRIMAGMA, 29
-
Wong (2000)
Perfusion MR imaging of brain neoplasmsAJR Am J Roentgenol, 174
-
Soh (1999)
Texture analysis of SAR sea ice imagery using gray level co-occurrence matricesIEEE Transact Geosci Remote Sensing, 37
-
(2014)
Why Health Care May Finally Be Ready for Big Data -
Aghili (2009)
Hydroxyglutaric aciduria and malignant brain tumor: a case report and literature reviewJ Neurooncol, 91
-
Bibault (2016)
Big data and machine learning in radiation oncology: state of the art and future prospectsCancer Lett, 382
-
Duffaud (2000)
New guidelines to evaluate the response to treatment in solid tumorsBull Cancer, 87
-
Dangouloff-Ros (2016)
Arterial spin labeling to predict brain tumor grading in children: correlations between histopathologic vascular density and perfusion MR imagingRadiology, 281
-
(1996)
From data mining to knowledge discovery: an overview -
Lam (1996)
Texture feature extraction using gray level gradient based co-occurence matrices. Systems, Man, and Cybernetics, 1996IEEE International Conference on, 1
-
Cha (2002)
Intracranial mass lesions: dynamic contrast-enhanced susceptibility-weighted echo-planar perfusion MR imagingRadiology, 223
-
Gerlinger (2012)
Intratumor heterogeneity and branched evolution revealed by multiregion sequencingN Engl J Med, 366
-
in Systems, Man, and Cybernetics (SMC), 2011 -
Sourbron (2009)
Quantification of cerebral blood flow, cerebral blood volume, and blood-brain-barrier leakage with DCE-MRIMagn Reson Med, 62
-
Levner (2009)
Predicting MGMT methylation status of glioblastomas from MRI textureMed Image Comput Comput Assist Interv, 12
-
Rao (2016)
A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastomaJ Neurosurg, 124
-
Jenkinson (2007)
Advanced MRI in the management of adult gliomasBr J Neurosurg, 21
-
Pallavi (2014)
Texture descriptors to distinguish radiation necrosis from recurrent brain tumors on multi-parametric MRIProc SPIE Int Soc Opt Eng, 9035
-
Ellingson (2015)
Radiogenomics and imaging phenotypes in glioblastoma: novel observations and correlation with molecular characteristicsCurr Neurol Neurosci Rep, 15
-
Wintermark (2005)
Comparative overview of brain perfusion imaging techniquesJ Neuroradiol, 32
-
Miles (2013)
CT texture analysis using the filtration-histogram method: what do the measurements mean?Cancer Imaging, 13
-
Hu (2015)
Multi-parametric MRI and texture analysis to visualize spatial histologic heterogeneity and tumor extent in glioblastomaPLoS One, 10
-
Brown (2008)
The use of magnetic resonance imaging to noninvasively detect genetic signatures in oligodendrogliomaClin Cancer Res, 14
-
Ross (1994)
Clinical applications of magnetic resonance spectroscopyMagn Reson Q, 10
-
Bulik (2013)
Potential of MR spectroscopy for assessment of glioma gradingClin Neurol Neurosurg, 115
-
Parker (2016)
Intratumoral heterogeneity identified at the epigenetic, genetic and transcriptional level in glioblastomaSci Rep, 6
-
Siegel (2014)
Cancer statistics, 2014CA Cancer J Clin, 64
-
Zinn (2013)
Imaging genomic mapping in glioblastomaNeurosurgery, 60
-
Kotrotsou (2016)
Radiomics in brain tumors: an emerging technique for characterization of tumor environmentMagn Reson Imaging Clin N Am, 24
-
Zinn (2016)
139 clinically applicable and biologically validated MRI radiomic test method predicts glioblastoma genomic landscape and survivalNeurosurgery, 63
-
Kang (2011)
Gliomas: histogram analysis of apparent diffusion coefficient maps with standard- or high-b-value diffusion-weighted MR imaging—correlation with tumor gradeRadiology, 261
-
Eliat (2012)
Can dynamic contrast-enhanced magnetic resonance imaging combined with texture analysis differentiate malignant glioneuronal tumors from other glioblastoma?Neurol Res Int, 2012
-
Kumar (2012)
Radiomics: the process and the challengesMagn Reson Imaging, 30
-
Filice (2016)
Dynamic contrast-enhanced perfusion MRI of high grade brain gliomas obtained with arterial or venous waveform input functionJ Neuroimaging, 26
-
Aerts (2014)
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approachNat Commun, 5
-
Kjaer (1995)
Texture analysis in quantitative MR imaging. Tissue characterisation of normal brain and intracranial tumours at 1.5 TActa Radiol, 36
-
Korfiatis (2014)
The basics of diffusion and perfusion imaging in brain tumorsAppl Radiol, 43
-
Drabycz (2010)
An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imagingNeuroimage, 49
-
Oz (2014)
Clinical proton MR spectroscopy in central nervous system disordersRadiology, 270
-
Kao (2013)
Advanced MR imaging of gliomas: an updateBiomed Res Int, 2013
-
(2006)
Texture Analysis for Magnetic Resonance Imaging -
Lin (2015)
Response assessment criteria for brain metastases: proposal from the RANO groupLancet Oncol, 16
-
Nachimuthu (2014)
Multidimensional texture characterization: on analysis for brain tumor tissues using MRS and MRIJ Digit Imaging, 27
-
(2013)
Brain MR image normalization in texture analysis of multiple sclerosis, 3
-
Pierpaoli (2010)
Quantitative brain MRITopics Magn Reson Imaging, 21
-
(1990)
Random Variables, and Stochastic Processes -
(2016)
Radiogenomics to characterize regional genetic heterogeneity in glioblastoma -
(2012)
Big data in healthcare hype and hope -
Tofts (1999)
Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbolsJ Magn Reson Imaging, 10
-
Big Data: 20 Mind-Boggling Facts Everyone Must Read -
Hatt (2017)
Characterization of PET/CT images using texture analysis: the past, the present... any future?Eur J Nucl Med Mol Imaging, 44
-
Arnold (2001)
Diagnosis and management of brain metastasesHematol Oncol Clin North Am, 15
-
Peduzzi (1996)
A simulation study of the number of events per variable in logistic regression analysisJ Clin Epidemiol, 49
- Publisher
- Wolters Kluwer Health
- Copyright
- Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.
- ISSN
- 0899-3459
- eISSN
- 1536-1004
- DOI
- 10.1097/RMR.0000000000000117
- pmid
- 28079714
- Publisher site
- See Article on Publisher Site
Abstract
AbstractRadiomics is a new area of research in the field of imaging with tremendous potential to unravel the hidden information in digital images. The scope of radiology has grown exponentially over the last two decades; since the advent of radiomics, many quantitative imaging features can now be extracted from medical images through high-throughput computing, and these can be converted into mineable data that can help in linking imaging phenotypes with clinical data, genomics, proteomics, and other “omics” information. In cancer, radiomic imaging analysis aims at extracting imaging features embedded in the imaging data, which can act as a guide in the disease or cancer diagnosis, staging and planning interventions for treating patients, monitor patients on therapy, predict treatment response, and determine patient outcomes.
Journal
Topics in Magnetic Resonance Imaging – Wolters Kluwer Health
Published: Feb 1, 2017