M. Galloway, Texture analysis using gray level run lengths, Computer Graphics and Image Processing, vol.4, issue.2, pp.172-179, 1975.
DOI : 10.1016/S0146-664X(75)80008-6

G. Thibault, J. Angulo, and F. Meyer, Advanced Statistical Matrices for Texture Characterization: Application to Cell Classification, IEEE Transactions on Biomedical Engineering, vol.61, issue.3, pp.630-637, 2014.
DOI : 10.1109/TBME.2013.2284600

C. Sun and W. Wee, Neighboring gray level dependence matrix for texture classification, Computer Vision, Graphics, and Image Processing, vol.23, issue.3, pp.341-352, 1983.
DOI : 10.1016/0734-189X(83)90032-4

S. Narang, M. Lehrer, and D. Yang, Radiomics in glioblastoma: current status, challenges and potential opportunities, Translational Cancer Research, vol.5, issue.4, pp.383-397, 2016.
DOI : 10.21037/tcr.2016.06.31

Y. Zheng, S. Englander, and S. Baloch, STEP: Spatiotemporal enhancement pattern for MR-based breast tumor diagnosis, Medical Physics, vol.9, issue.7, pp.3192-3204, 2009.
DOI : 10.1016/j.media.2005.01.001
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2852449/pdf

J. Wang, H. Bensmail, and X. Gao, Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification, Pattern Recognition, vol.46, issue.12, pp.3249-3255, 2013.
DOI : 10.1016/j.patcog.2013.05.001

S. Cataldo and E. Ficarra, Mining textural knowledge in biological images: Applications, methods and trends, Computational and Structural Biotechnology Journal, vol.15, 2016.
DOI : 10.1016/j.csbj.2016.11.002

S. Armato, C. Meyer, and M. Mcnitt-gray, The Reference Image Database to Evaluate Response to Therapy in Lung Cancer (RIDER) Project: A Resource for the Development of Change-Analysis Software, Clinical Pharmacology & Therapeutics, vol.9, issue.4, pp.448-456, 2008.
DOI : 10.1016/j.acra.2006.07.012

W. Shen, M. Zhou, and F. Yang, Multi-scale Convolutional Neural Networks for Lung Nodule Classification, Information Processing in Medical Imaging, pp.588-599, 2015.
DOI : 10.1007/978-3-319-19992-4_46

E. Zacharaki, N. Morita, and P. Bhatt, Survival Analysis of Patients with High-Grade Gliomas Based on Data Mining of Imaging Variables, American Journal of Neuroradiology, vol.33, issue.6, p.1065, 2012.
DOI : 10.3174/ajnr.A2939

E. Zacharaki, V. Kanas, and C. Davatzikos, Investigating machine learning techniques for MRI-based classification of brain neoplasms, International Journal of Computer Assisted Radiology and Surgery, vol.25, issue.2, pp.821-828, 2011.
DOI : 10.1148/radiol.2432060493

Y. Liu, Y. Balagurunathan, and T. Atwater, Radiological Image Traits Predictive of Cancer Status in Pulmonary Nodules, Clinical Cancer Research, vol.23, issue.6, p.3102, 2016.
DOI : 10.1158/1078-0432.CCR-15-3102

E. Zacharaki, S. Wang, and S. Chawla, Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme, Magnetic Resonance in Medicine, vol.17, issue.6, pp.1609-1618, 2009.
DOI : 10.1109/34.689305

J. Wang, X. Liu, and D. Dong, Prediction of malignant and benign of lung tumor using a quantitative radiomic method, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.1272-1275, 2016.
DOI : 10.1109/EMBC.2016.7590938

K. Hayano, F. Tian, and A. Kambadakone, Texture Analysis of Non???Contrast-Enhanced Computed Tomography for Assessing Angiogenesis and Survival of Soft Tissue Sarcoma, Journal of Computer Assisted Tomography, vol.39, issue.4, pp.607-612, 2015.
DOI : 10.1097/RCT.0000000000000239

H. Zhu, H. Chen, and Z. Lin, Identifying molecular genetic features and oncogenic pathways of clear cell renal cell carcinoma through the anatomical (PADUA) scoring system, Oncotarget, vol.7, issue.9, pp.10006-10014, 2016.
DOI : 10.18632/oncotarget.7129

D. Hanahan and R. Weinberg, The Hallmarks of Cancer, Cell, vol.100, issue.1, pp.57-70, 2000.
DOI : 10.1016/S0092-8674(00)81683-9

D. Hanahan and R. Weinberg, Hallmarks of Cancer: The Next Generation, Cell, vol.144, issue.5, pp.646-674, 2011.
DOI : 10.1016/j.cell.2011.02.013

B. Ganeshan, S. Abaleke, and R. Young, Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage, Cancer Imaging, vol.10, issue.1, pp.137-143, 2010.
DOI : 10.1102/1470-7330.2010.0021

K. Panth, R. Leijenaar, and S. Carvalho, Is there a causal relationship between genetic changes and radiomics-based image features? An in vivo preclinical experiment with doxycycline inducible GADD34 tumor cells, Radiotherapy and Oncology, vol.116, issue.3, pp.462-466, 2015.
DOI : 10.1016/j.radonc.2015.06.013

Y. Zhu, H. Li, and W. Guo, Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma, Scientific Reports, vol.11, issue.1, p.17787, 2015.
DOI : 10.1038/nmeth.2956

P. Zinn, B. Mahajan, and B. Majadan, Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme, PLoS ONE, vol.7, issue.10, p.25451, 2011.
DOI : 10.1371/journal.pone.0025451.s002

A. Basavanhally, S. Ganesan, and S. Agner, Computerized Image-Based Detection and Grading of Lymphocytic Infiltration in HER2+ Breast Cancer Histopathology, IEEE Transactions on Biomedical Engineering, vol.57, issue.3, pp.642-653, 2010.
DOI : 10.1109/TBME.2009.2035305

E. Segal, C. Sirlin, and C. Ooi, Decoding global gene expression programs in liver cancer by noninvasive imaging, Nature Biotechnology, vol.101, issue.6, pp.675-680, 2007.
DOI : 10.3748/wjg.v8.i3.385

D. Zinovev, J. Feigenbaum, J. Furst, and D. Raicu, Probabilistic lung nodule classification with belief decision trees, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.4493-4498, 2011.
DOI : 10.1109/IEMBS.2011.6091114

W. Wu, C. Parmar, and P. Grossmann, Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology, Frontiers in Oncology, vol.44, issue.Suppl 2
DOI : 10.1038/ng.2415
URL : http://journal.frontiersin.org/article/10.3389/fonc.2016.00071/pdf

F. Maldonado, J. Boland, and S. Raghunath, Noninvasive Characterization of the Histopathologic Features of Pulmonary Nodules of the Lung Adenocarcinoma Spectrum using Computer-Aided Nodule Assessment and Risk Yield (CANARY)???A Pilot Study, Journal of Thoracic Oncology, vol.8, issue.4, pp.452-460, 2013.
DOI : 10.1097/JTO.0b013e3182843721

B. Ganeshan, K. Miles, R. Young, and C. Chatwin, Texture analysis in non-contrast enhanced CT: Impact of malignancy on texture in apparently disease-free areas of the liver, European Journal of Radiology, vol.70, issue.1, pp.101-110, 2009.
DOI : 10.1016/j.ejrad.2007.12.005

T. Coroller, P. Grossmann, and Y. Hou, CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma, Radiotherapy and Oncology, vol.114, issue.3, pp.345-350, 2015.
DOI : 10.1016/j.radonc.2015.02.015
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4400248/pdf

Y. Huang, C. Liang, and L. He, Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer, Journal of Clinical Oncology, vol.34, issue.18, 2016.
DOI : 10.1200/JCO.2015.65.9128

C. Liang, Y. Huan, and L. He, The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer, Oncotarget, vol.7, issue.21
DOI : 10.18632/oncotarget.8919

C. Parmar, R. Leijenaar, and P. Grossmann, Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer, Scientific Reports, vol.7, issue.1, p.11044, 2015.
DOI : 10.1593/tlo.13844
URL : http://www.nature.com/articles/srep11044.pdf

R. Leijenaar, S. Carvalho, and F. Hoebers, External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma, Acta Oncologica, vol.5, issue.9, pp.1423-1429, 2015.
DOI : 10.1038/srep11044

J. Lee, S. Narang, and J. Martinez, Spatial Habitat Features Derived from Multiparametric Magnetic Resonance Imaging Data Are Associated with Molecular Subtype and 12-Month Survival Status in Glioblastoma Multiforme, PLOS ONE, vol.10, issue.3, p.136557, 2015.
DOI : 10.1371/journal.pone.0136557.s001

P. Kickingereder, S. Burth, and A. Wick, Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models, Radiology, vol.280, issue.3, pp.880-889, 2016.
DOI : 10.1148/radiol.2016160845

Y. Liu, J. Kim, and F. Qu, CT Features Associated with Epidermal Growth Factor Receptor Mutation Status in Patients with Lung Adenocarcinoma, Radiology, vol.280, issue.1, pp.271-80, 2016.
DOI : 10.1148/radiol.2016151455
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4934516/pdf

H. Aerts, P. Grossmann, and Y. Tan, Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC, Scientific Reports, vol.258, issue.1
DOI : 10.1148/radiol.10100799

N. Michoux, S. Van-den-broeck, and L. Lacoste, Texture analysis on MR images helps predicting non-response to NAC in breast cancer, BMC Cancer, vol.17, issue.1, p.574, 2015.
DOI : 10.1093/annonc/mdl114

A. Ahmed, P. Gibbs, M. Pickles, and L. Turnbull, Texture analysis in assessment and prediction of chemotherapy response in breast cancer, Journal of Magnetic Resonance Imaging, vol.25, issue.1, pp.89-101, 2013.
DOI : 10.1016/0734-189X(84)90197-X

G. Cook, O. Brien, M. Siddique, and M. , F-FDG Uptake at PET???Association with Treatment Response and Prognosis, Radiology, vol.276, issue.3, pp.883-893, 2015.
DOI : 10.1148/radiol.2015141309

V. Goh, B. Ganeshan, and P. Nathan, Assessment of Response to Tyrosine Kinase Inhibitors in Metastatic Renal Cell Cancer: CT Texture as a Predictive Biomarker, Radiology, vol.261, issue.1, pp.165-171, 2011.
DOI : 10.1148/radiol.11110264

R. Bundschuh, J. Dinges, and L. Neumann, Textural Parameters of Tumor Heterogeneity in 18F-FDG PET/CT for Therapy Response Assessment and Prognosis in Patients with Locally Advanced Rectal Cancer, Journal of Nuclear Medicine, vol.55, issue.6, pp.891-897, 2014.
DOI : 10.2967/jnumed.113.127340

V. Chiou and M. Burotto, Pseudoprogression and Immune-Related Response in Solid Tumors, Journal of Clinical Oncology, vol.33, issue.31, pp.3541-3543, 2015.
DOI : 10.1200/JCO.2015.61.6870
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4622096/pdf

P. Tumeh, C. Harview, and J. Yearley, PD-1 blockade induces responses by inhibiting adaptive immune resistance, Nature, vol.15, issue.7528, pp.568-571, 2014.
DOI : 10.1158/1078-0432.CCR-09-1624
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4246418/pdf

C. Ferté, Dynamic quantitative imaging approaches to identify pseudoprogression in cancer patients treated by immune checkpoints blockers. AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics, 2015.

M. Infante, R. Lutman, and S. Imparato, Differential diagnosis and management of focal ground-glass opacities, European Respiratory Journal, vol.33, issue.4, pp.821-827, 2009.
DOI : 10.1183/09031936.00047908
URL : http://erj.ersjournals.com/content/erj/33/4/821.full.pdf

S. Mattonen, S. Tetar, and D. Palma, Automated Texture Analysis for Prediction of Recurrence After Stereotactic Ablative Radiation Therapy for Lung Cancer, International Journal of Radiation Oncology*Biology*Physics, vol.93, issue.3, pp.5-6, 2015.
DOI : 10.1016/j.ijrobp.2015.07.019

J. Antunes, S. Viswanath, and M. Rusu, Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study, Translational Oncology, vol.9, issue.2, pp.155-162, 2016.
DOI : 10.1016/j.tranon.2016.01.008

E. Drucker and K. Krapfenbauer, Pitfalls and limitations in translation from biomarker discovery to clinical utility in predictive and personalised medicine, EPMA Journal, vol.1, issue.1, p.7, 2013.
DOI : 10.1007/s13167-010-0005-6
URL : https://link.springer.com/content/pdf/10.1186%2F1878-5085-4-7.pdf

J. Wagner, M. Prince, and E. Wright, The Biomarkers Consortium: Practice and Pitfalls of Open-Source Precompetitive Collaboration, Clinical Pharmacology & Therapeutics, vol.87, issue.5, pp.539-542, 2010.
DOI : 10.1038/nrd2939

D. Altman, L. Mcshane, W. Sauerbrei, and S. Taube, Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration, BMC Medicine, vol.362, issue.1, p.51, 2012.
DOI : 10.1016/S0140-6736(03)14686-7
URL : https://doi.org/10.1371/journal.pmed.1001216

A. Brazma, P. Hingamp, and J. Quackenbush, Minimum information about a microarray experiment (MIAME)-toward standards for microarray data, Nature Genetics, vol.29, issue.4, pp.365-371, 2001.
DOI : 10.1038/ng1201-365
URL : https://doi.org/10.1100/tsw.2009.57