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Radiomics: Images Are More Than Pictures, They Are Data

R. Gillies, P. Kinahan, H. Hricak
Published 2016 · Medicine

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This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
This paper references
Automatic classification of prostate cancer Gleason scores from multi - parametric magnetic resonance images . Proc Natl Acad Sci USA ( in press ) . Historical Perspective and Planned Goals
D Fehr (2014)
Consensus recommendations for the use of 18F-FDG PET as an indicator of therapeutic response in patients in National Cancer Institute Trials.
L. Shankar (2006)
10.1038/ncomms5006
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
H. Aerts (2014)
10.1080/10543400802527890
The Concordance Correlation Coefficient for Repeated Measures Estimated by Variance Components
J. Carrasco (2009)
10.1002/cncr.10318
Results of the Lynn Sage Second‐Opinion Program for local therapy in patients with breast carcinoma
J. Clauson (2002)
10.1158/1078-0432.CCR-14-0223
It's All About the Test: The Complexity of Companion Diagnostic Co-development in Personalized Medicine
S. Bates (2014)
10.1007/s00330-009-1685-y
RECIST revised: implications for the radiologist. A review article on the modified RECIST guideline
E. L. van Persijn van Meerten (2009)
10.1088/0031-9155/60/7/2685
Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness.
A. Vignati (2015)
10.1093/JNCI/88.20.1456
Tumor marker utility grading system: a framework to evaluate clinical utility of tumor markers.
D. Hayes (1996)
10.1016/J.JVIR.2007.04.031
Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma.
M. Kuo (2007)
10.1200/JCO.2007.14.8494
First-line gefitinib in patients with advanced non-small-cell lung cancer harboring somatic EGFR mutations.
L. Sequist (2008)
10.1016/j.radonc.2015.02.015
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.
T. Coroller (2015)
10.1016/S1078-1439(03)00236-9
The impact of pathology review on treatment recommendations for patients with adenocarcinoma of the prostate.
P. Nguyen (2004)
J Clin Oncol
Egfr Matic (2008)
10.1155/2014/431680
Standardization of Multiparametric Prostate MR Imaging Using PI-RADS
J. Bomers (2014)
Imaging in - tratumor heterogeneity : role in therapy response , resistance , and clinical outcome
JP O’Connor (2015)
Behavior of incidence lung cancers in the CT arm of the National Lung Screening Trial
Mb Schabath
10.1038/483531a
Drug development: Raise standards for preclinical cancer research
C. G. Begley (2012)
10.1016/j.compmedimag.2007.02.002
Computer-aided diagnosis in medical imaging: Historical review, current status and future potential
K. Doi (2007)
Fact sheet: President Obama's precision medicine initiative. The White House Web site
10.1016/j.jacr.2015.12.010
ACR CT Accreditation Program and the Lung Cancer Screening Program Designation.
E. Kazerooni (2016)
10.1056/NEJMoa1113205
Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.
M. Gerlinger (2012)
CT-guided biopsies of metabolically active bone lesions: applications and clinical impact
B Klaeser (2010)
10.1118/1.4925587
TU-CD-BRB-02: BEST IN PHYSICS (JOINT IMAGING-THERAPY): Identification of Molecular Phenotypes by Integrating Radiomics and Genomics
P. Grossmann (2015)
10.1016/j.jacr.2009.07.023
The ACR BI-RADS experience: learning from history.
E. Burnside (2009)
10.1007/s10278-013-9663-y
Mapping Institution-Specific Study Descriptions to RadLex Playbook Entries
T. Mabotuwana (2013)
10.1038/nbt1306
Decoding global gene expression programs in liver cancer by noninvasive imaging
E. Segal (2007)
10.1038/461160a
Data sharing: Empty archives
B. Nelson (2009)
Transl Oncol
(2014)
Journals unite for reproducibil
SC Landis
FDG-PET/CT as an Imaging Biomarker Measuring Response to Cancer Therapy, Quantitative Imaging Biomarkers Alliance, Version 1.05, Publicly Reviewed Version. RSNA Web site
Fdg-PetCt Technical Committee
Lung nodule classification using learnt texture features on a single patient population [abstr]
L Pickup (2015)
10.1016/j.radonc.2012.09.023
A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen.
E. Rios Velazquez (2012)
10.1002/mrm.22003
Quantifying spatial heterogeneity in dynamic contrast‐enhanced MRI parameter maps
C. J. Rose (2009)
The QIBA profile for quantitative FDG-PET/CT oncology imaging
P. Kinahan (2014)
10.1038/nature11556
A call for transparent reporting to optimize the predictive value of preclinical research
S. Landis (2012)
10.1016/j.ejca.2011.11.036
Radiomics: extracting more information from medical images using advanced feature analysis.
P. Lambin (2012)
Institute of Medicine I. Evolution of Translational Omics. Lessons learned and the path forward
(2012)
10.1007/s10278-014-9716-x
Test–Retest Reproducibility Analysis of Lung CT Image Features
Yoganand Balagurunathan (2014)
10.1200/JCO.2010.33.2312
Dissecting therapeutic resistance to RAF inhibition in melanoma by tumor genomic profiling.
N. Wagle (2011)
Historical Perspective and Planned Goals
(2014)
10.1093/jnci/djp335
Use of archived specimens in evaluation of prognostic and predictive biomarkers.
R. Simon (2009)
matic EGFR mutations
(2008)
10.1371/journal.pone.0118261
Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma
O. Grove (2015)
10.1016/j.radonc.2010.05.003
Improving target delineation on 4-dimensional CT scans in stage I NSCLC using a deformable registration tool.
I. V. van Dam (2010)
10.1186/1741-7015-8-24
Transparent and accurate reporting increases reliability, utility, and impact of your research: reporting guidelines and the EQUATOR Network
I. Simera (2010)
10.1158/1078-0432.CCR-14-0990
Imaging Intratumor Heterogeneity: Role in Therapy Response, Resistance, and Clinical Outcome
J. O'Connor (2014)
10.1016/j.acra.2013.09.011
Evaluating RadLex and real world radiology reporting: are we there yet?
M. Heilbrun (2013)
10.1038/nature09515
Distant Metastasis Occurs Late during the Genetic Evolution of Pancreatic Cancer
S. Yachida (2010)
10.1016/S0140-6736(09)60329-9
Avoidable waste in the production and reporting of research evidence
I. Chalmers (2009)
10.1148/radiol.13122697
Quantitative imaging in cancer evolution and ecology.
R. Gatenby (2013)
10.1109/TSMC.1973.4309314
Textural Features for Image Classification
R. Haralick (1973)
10.2307/2532051
A concordance correlation coefficient to evaluate reproducibility.
L. Lin (1989)
10.1038/515007a
Journals unite for reproducibility
(2014)
10.1073/pnas.1219747110
Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics
A. Sottoriva (2013)
10.1200/JCO.2003.02.018
Trastuzumab and vinorelbine as first-line therapy for HER2-overexpressing metastatic breast cancer: multicenter phase II trial with clinical outcomes, analysis of serum tumor markers as predictive factors, and cardiac surveillance algorithm.
H. Burstein (2003)
10.1038/nrd3439-c1
Believe it or not: how much can we rely on published data on potential drug targets?
F. Prinz (2011)
10.2217/fon.09.154
Update on the potential of computer-aided diagnosis for breast cancer.
M. Giger (2010)
10.1593/TLO.13832
The Quantitative Imaging Network: NCI's Historical Perspective and Planned Goals.
L. Clarke (2014)
10.1093/carcin/bgv007
Radiogenomics helps to achieve personalized therapy by evaluating patient responses to radiation treatment.
Z. Guo (2015)
10.1007/s13244-012-0196-6
Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?
Fergus Davnall (2012)
10.3322/canjclin.39.6.399
Cancer statistics
N. Dubrawsky (1989)
10.1007/s00330-015-3701-8
Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores
A. Wibmer (2015)
10.1126/scitranslmed.3002003
Genotypic and Histological Evolution of Lung Cancers Acquiring Resistance to EGFR Inhibitors
L. Sequist (2011)
10.1148/radiol.10100799
A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging.
A. Buckler (2011)
First-line gefitinib in patients with advanced non-small-cell lung cancer harboring so-Radiomics Resources Readers may find the following resources helpful: QIN
L V Sequist
References 1. Institute of Medicine I. Evolution of Translational Omics. Lessons learned and the path forward
Drug Food (2012)
10.1593/TLO.13730
Radiologically defined ecological dynamics and clinical outcomes in glioblastoma multiforme: preliminary results.
M. Zhou (2014)
10.1148/radiol.2015154019
Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features.
O. Gevaert (2015)
10.1002/mrm.24644
Analysis of image heterogeneity using 2D Minkowski functionals detects tumor responses to treatment
T. Larkin (2014)
Sackler Colloquia of the National Academy of Sciences
M Arthur
Identification of molecular phenotypes in lung cancer by integrating radiomics and genomics
P Grossmann
Lung nodule classification using learnt texture features on a single patient population
L Pickup (2015)
10.1016/j.mri.2012.06.010
Radiomics: the process and the challenges.
Virendra Kumar (2012)
10.1016/j.acra.2013.11.007
Annotation of figures from the biomedical imaging literature: a comparative analysis of RadLex and other standardized vocabularies.
C. E. Kahn (2014)
10.3322/caac.21208
Cancer statistics, 2014
R. Siegel (2014)
10.1056/NEJMp1500523
A new initiative on precision medicine.
F. Collins (2015)
The Quantitative Imaging Network: NCI's SPECIAL REPORT: Radiomics Gillies et al
Lp Clarke
10.1002/nbm.3132
Dynamic contrast‐enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer
J. R. Teruel (2014)
10.1053/j.seminoncol.2009.12.007
The use of central laboratories and remote electronic data capture to risk-adjust therapy for pediatric acute lymphoblastic leukemia and neuroblastoma.
M. Devidas (2010)
10.1002/1097-0142(20000701)89:1<225::AID-CNCR36>3.0.CO;2-1
Mandatory second opinion surgical pathology at a large referral hospital
R. Sirota (2000)
ture features on T 2 - weighted magnetic resonance imaging : new potential biomarkers for prostate cancer aggressiveness
A Vignati (2015)
Dynamic contrast - enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadju - vant chemotherapy in patients with locally advanced breast cancer
Teruel JRHM (2014)
Radiogenomics helps to achieve personalized 41. Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification
Z Guo (1973)
10.1016/J.ACRA.2007.10.021
Imaging as a Biomarker: Standards for Change Measurements in Therapy workshop summary.
L. Clarke (2008)
10.1007/s10278-013-9622-7
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
K. Clark (2013)
10.1073/pnas.1608845113
Drawing causal inference from Big Data
R. Shiffrin (2016)
10.1371/journal.pone.0159880
Differences in Patient Outcomes of Prevalence, Interval, and Screen-Detected Lung Cancers in the CT Arm of the National Lung Screening Trial
M. Schabath (2016)
10.1007/s00259-010-1524-z
PET/CT-guided biopsies of metabolically active bone lesions: applications and clinical impact
B. Klaeser (2010)
10.1007/BF02574516
Changes in breast cancer therapy because of pathology second opinions
V. Staradub (2007)
10.1148/radiol.14131731
Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features.
O. Gevaert (2014)
10.1073/pnas.1505935112
Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images
D. Fehr (2015)
10.1148/radiol.10090931
Abdominal masses sampled at PET/CT-guided percutaneous biopsy: initial experience with registration of prior PET/CT images.
S. Tatli (2010)
10.1186/gm268
Genetics and genomics of radiotherapy toxicity: towards prediction
C. West (2011)
10.1073/pnas.0801279105
Identification of noninvasive imaging surrogates for brain tumor gene-expression modules
M. Diehn (2008)
Behavior of incidence lung cancers in the CT arm of the National Lung Screening Trial
S Stalin



This paper is referenced by
Image biomarker standardisation initiative
A. Zwanenburg (2016)
Unsupervised Histopathology Image Synthesis
Le Hou (2017)
10.21147/j.issn.1000-9604.2018.04.02
Radiomics approach for preoperative identification of stages I−II and III−IV of esophageal cancer
L. Wu (2018)
10.1038/srep25295
Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity
Islam A. Hassan (2016)
10.1097/MNM.0000000000000937
Artificial intelligence and nuclear medicine
Margaret L Hall (2019)
10.2967/jnumed.117.200758
Prediction of 90Y Radioembolization Outcome from Pretherapeutic Factors with Random Survival Forests
M. Ingrisch (2018)
10.26717/BJSTR.2018.02.000678
Images Are More than Documentation: They Are ResearchData
Faten Dhawi (2018)
10.14288/1.0380226
Personalized dosimetry protocol for the optimization of lutetium-177 DOTATATE radionuclide therapy
Wei Zhao (2019)
Prognostic Prediction of Lung Cancer Patients Using Random Survival Forest
T. Yoshioka (2019)
10.1002/mp.13150
The utility of quantitative CT radiomics features for improved prediction of radiation pneumonitis
S. Krafft (2018)
10.17077/ETD.0XWCEK81
Longitudinal medical imaging approaches for characterization of porcine cancer models
E. Hammond (2017)
10.1016/j.cllc.2017.05.014
Radiomics‐based Assessment of Radiation‐induced Lung Injury After Stereotactic Body Radiotherapy
Angel Moran (2017)
10.1186/s12880-019-0321-9
Radiomics-based classification of hepatocellular carcinoma and hepatic haemangioma on precontrast magnetic resonance images
J. Wu (2019)
10.1136/bmjopen-2018-023157
Prediction of adverse motor outcome for neonates with punctate white matter lesions by MRI images using radiomics strategy: protocol for a prospective cohort multicentre study
M. Wang (2019)
10.1186/s13014-017-0885-x
Development and clinical application of radiomics in lung cancer
Bojiang Chen (2017)
10.1007/s11548-017-1691-5
MRI radiomics analysis of molecular alterations in low-grade gliomas
B. Shofty (2017)
10.1038/s41598-017-05848-2
Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma
Zeju Li (2017)
10.2967/jnumed.118.222893
Introduction to Radiomics
M. Mayerhoefer (2020)
10.1002/jmrs.369
Artificial Intelligence in medical imaging practice: looking to the future
S. Lewis (2019)
10.1016/j.ijrobp.2018.05.022
Radiomics in Nuclear Medicine Applied to Radiation Therapy: Methods, Pitfalls, and Challenges.
S. Reuzé (2018)
10.2214/AJR.16.16435
The Top Three Health Care Developments Impacting the Practice of Interventional Radiology in the Next Decade.
S. Kwan (2016)
10.3748/wjg.v26.i19.2388
Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer
Zheng-yan Li (2020)
10.2147/CMAR.S217887
Radiomic signature: a non-invasive biomarker for discriminating invasive and non-invasive cases of lung adenocarcinoma
Bin Yang (2019)
About New Health Technologies An Overview of Clinical Applications of Artificial Intelligence
J. Mason (2018)
10.1007/s00330-020-07064-5
Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features
Simon Bernatz (2020)
10.1007/s00330-020-06993-5
MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes
LuoDan Qian (2020)
10.1007/s11307-019-01423-5
Contrast-Enhanced Mammography and Radiomics Analysis for Noninvasive Breast Cancer Characterization: Initial Results
M. A. Marino (2019)
10.1007/s00261-020-02576-6
DTI-based radiomics signature for the detection of early diabetic kidney damage
Yi Deng (2020)
10.1002/jmri.26852
Machine learning in breast MRI
B. Reig (2019)
10.1109/JBHI.2020.2991043
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Andreas S. Panayides (2020)
10.3389/fnins.2020.00144
Amide Proton Transfer Imaging in Predicting Isocitrate Dehydrogenase 1 Mutation Status of Grade II/III Gliomas Based on Support Vector Machine
Y. Han (2020)
10.1177/0284185120922822
Assessment of liver metastases radiomic feature reproducibility with deep-learning-based semi-automatic segmentation software.
L. Wang (2020)
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