M. Gerlinger, A. Rowan, S. Horswell, J. Larkin, D. Endesfelder et al., Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing, New England Journal of Medicine, vol.366, issue.10, pp.883-92, 2012.
DOI : 10.1056/NEJMoa1113205

F. Davnall, C. Yip, G. Ljungqvist, M. Selmi, F. Ng et al., Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging, pp.573-89, 2012.

S. Chicklore, V. Goh, M. Siddique, A. Roy, P. Marsden et al., Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis, European Journal of Nuclear Medicine and Molecular Imaging, vol.25, issue.1, pp.133-173, 2013.
DOI : 10.1007/s00259-012-2247-0

O. Connor, J. Rose, C. Waterton, J. Carano, R. Parker et al., Imaging Intratumor Heterogeneity: Role in Therapy Response, Resistance, and Clinical Outcome, Clinical Cancer Research, vol.21, issue.2, pp.249-57, 2015.
DOI : 10.1158/1078-0432.CCR-14-0990

J. Willaime, F. Turkheimer, L. Kenny, and E. Aboagye, Quantification of intra-tumour cell proliferation heterogeneity using imaging descriptors of 18F fluorothymidine-positron emission tomography, Physics in Medicine and Biology, vol.58, issue.2, p.187, 2013.
DOI : 10.1088/0031-9155/58/2/187

W. Weber, M. Schwaiger, and A. N. , Quantitative assessment of tumor metabolism using FDG-PET imaging, Nuclear Medicine and Biology, vol.27, issue.7, pp.683-690, 2000.
DOI : 10.1016/S0969-8051(00)00141-4

H. Aerts, E. Velazquez, R. Leijenaar, C. Parmar, P. Grossmann et al., Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach, Nat. Commun, vol.5, p.4006, 2014.

T. Win, K. Miles, S. Janes, B. Ganeshan, M. Shastry et al., Tumor Heterogeneity and Permeability as Measured on the CT Component of PET/CT Predict Survival in Patients with Non-Small Cell Lung Cancer, Clinical Cancer Research, vol.19, issue.13, pp.3591-3600, 2013.
DOI : 10.1158/1078-0432.CCR-12-1307

M. Asselin, O. Connor, J. Boellaard, R. Thacker, N. Jackson et al., Quantifying heterogeneity in human tumours using MRI and PET, European Journal of Cancer, vol.48, issue.4, pp.447-55, 1990.
DOI : 10.1016/j.ejca.2011.12.025

O. Connor, J. Rose, C. Jackson, A. Watson, Y. Cheung et al., DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6, British Journal of Cancer, vol.10, issue.1, pp.139-184, 2011.
DOI : 10.1002/jmri.1880070113

M. Nicolasjilwan, Y. Hu, C. Yan, D. Meerzaman, C. Holder et al., Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients, Journal of Neuroradiology, vol.42, issue.4, pp.212-233, 2015.
DOI : 10.1016/j.neurad.2014.02.006

S. Yoon, C. Park, S. Park, J. Yoon, S. Hahn et al., Tumor Heterogeneity in Lung Cancer: Assessment with Dynamic Contrast-enhanced MR Imaging, Radiology, vol.280, issue.3, 2016.
DOI : 10.1148/radiol.2016151367

F. Michallek and M. Dewey, Fractal analysis in radiological and nuclear medicine perfusion imaging: a systematic review, European Radiology, vol.9, issue.1, pp.60-69, 2014.
DOI : 10.1007/s00330-013-2977-9

O. Sullivan, F. Roy, S. Eary, and J. , A statistical measure of tissue heterogeneity with application to 3D PET sarcoma data, Biostatistics, vol.4, issue.3, pp.433-481, 2003.
DOI : 10.1093/biostatistics/4.3.433

E. Naqa, I. Grigsby, P. Apte, A. Kidd, E. Donnelly et al., Exploring feature-based approaches in PET images for predicting cancer treatment outcomes, Pattern Recognition, vol.42, issue.6, pp.1162-71, 2009.
DOI : 10.1016/j.patcog.2008.08.011

M. Gonzalez, K. Dinelle, N. Vafai, N. Heffernan, J. Mckenzie et al., Novel spatial analysis method for PET images using 3D moment invariants: Applications to Parkinson's disease, NeuroImage, vol.68, pp.11-21, 2013.
DOI : 10.1016/j.neuroimage.2012.11.055

F. Van-velden, P. Cheebsumon, M. Yaqub, E. Smit, O. Hoekstra et al., Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies, European Journal of Nuclear Medicine and Molecular Imaging, vol.72, issue.9, pp.1636-1683, 2011.
DOI : 10.1007/s00259-011-1845-6

B. Ganeshan and K. Miles, Quantifying tumour heterogeneity with CT, Cancer Imaging, vol.13, issue.1, pp.140-149, 2013.
DOI : 10.1102/1470-7330.2013.0015

P. Lambin, E. Rios-velazquez, R. Leijenaar, S. Carvalho, R. Van-stiphout et al., Radiomics: Extracting more information from medical images using advanced feature analysis, European Journal of Cancer, vol.48, issue.4, pp.441-447, 2012.
DOI : 10.1016/j.ejca.2011.11.036

R. Gillies, P. Kinahan, and H. Hricak, Radiomics: Images Are More than Pictures, They Are Data, Radiology, vol.278, issue.2, pp.563-77, 2016.
DOI : 10.1148/radiol.2015151169

A. Mir, M. Hanmandlu, and S. Tandon, Texture analysis of CT-images for early detection of liver malignancy, Biomed. Sci. Instrum, vol.31, pp.213-220, 1995.

L. Schad, S. Blüml, and I. Zuna, IX. MR tissue characterization of intracranial tumors by means of texture analysis, Magnetic Resonance Imaging, vol.11, issue.6, pp.889-96, 1993.
DOI : 10.1016/0730-725X(93)90206-S

P. Galavis, C. Hollensen, N. Jallow, B. Paliwal, and R. Jeraj, Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters, Acta Oncologica, vol.45, issue.9, pp.1012-1018, 2010.
DOI : 10.1016/j.patcog.2008.08.011

F. Tixier, L. Rest, C. Hatt, M. Albarghach, N. Pradier et al., Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer, Journal of Nuclear Medicine, vol.52, issue.3, pp.369-78, 2011.
DOI : 10.2967/jnumed.110.082404
URL : https://hal.archives-ouvertes.fr/inserm-00574272

M. Hatt, H. Hanzouli, C. Rest, and D. Visvikis, Comparison of edge-preserving filters for unbiased quantification in 18F-FDG PET imaging, J. Nucl. Med, vol.56, pp.1828-1828, 2015.

J. Vaquero and P. Kinahan, Positron Emission Tomography: Current Challenges and Opportunities for Technological Advances in Clinical and Preclinical Imaging Systems, Annual Review of Biomedical Engineering, vol.17, issue.1, pp.385-414, 2015.
DOI : 10.1146/annurev-bioeng-071114-040723

A. Chalkidou, O. Doherty, M. Marsden, and P. , False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review, PLOS ONE, vol.158, issue.Suppl 1, p.124165, 2015.
DOI : 10.1371/journal.pone.0124165.s004

K. Dwan, C. Gamble, P. Williamson, and J. Kirkham, Systematic Review of the Empirical Evidence of Study Publication Bias and Outcome Reporting Bias ??? An Updated Review, PLoS ONE, vol.31, issue.7, p.66844, 2013.
DOI : 10.1371/journal.pone.0066844.s003

J. Ioannidis, How to Make More Published Research True, PLoS Medicine, vol.51, issue.7145, p.1001747, 2014.
DOI : 10.1371/journal.pmed.1001747.t002

S. Basu, T. Kwee, R. Gatenby, B. Saboury, D. Torigian et al., Evolving role of molecular imaging with PET in detecting and characterizing heterogeneity of cancer tissue at the primary and metastatic sites, a plausible explanation for failed attempts to cure malignant disorders, European Journal of Nuclear Medicine and Molecular Imaging, vol.37, issue.9, pp.987-91, 2011.
DOI : 10.1007/s00259-011-1787-z

D. Visvikis, M. Hatt, and F. Tixier, Cheze Le Rest C. The age of reason for FDG PET image-derived indices

T. Watabe, M. Tatsumi, H. Watabe, K. Isohashi, H. Kato et al., Intratumoral heterogeneity of F-18 FDG uptake differentiates between gastrointestinal stromal tumors and abdominal malignant lymphomas on PET/CT, Annals of Nuclear Medicine, vol.4, issue.3, pp.222-229, 2012.
DOI : 10.1007/s12149-011-0562-3

D. Kim, J. Jung, S. Son, C. Kim, S. Jeong et al., Quantification of Intratumoral Metabolic Macroheterogeneity on 18F-FDG PET/CT and Its Prognostic Significance in Pathologic N0 Squamous Cell Lung Carcinoma, Clinical Nuclear Medicine, vol.41, issue.2, 2015.
DOI : 10.1097/RLU.0000000000000930

F. Tixier, M. Hatt, C. Valla, V. Fleury, C. Lamour et al., Visual Versus Quantitative Assessment of Intratumor 18F-FDG PET Uptake Heterogeneity: Prognostic Value in Non-Small Cell Lung Cancer, Journal of Nuclear Medicine, vol.55, issue.8, pp.1235-1276, 2014.
DOI : 10.2967/jnumed.113.133389
URL : https://hal.archives-ouvertes.fr/inserm-01074715

T. Pyka, J. Gempt, D. Hiob, F. Ringel, J. Schlegel et al., Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas, European Journal of Nuclear Medicine and Molecular Imaging, vol.53, issue.5, 2015.
DOI : 10.1007/s00259-015-3140-4

M. Majdoub, D. Visvikis, F. Tixier, B. Hoeben, E. Visser et al., Proliferative 18F-FLT PET tumor volumes characterization for prediction of locoregional recurrence and disease-free survival in head and neck cancer, Soc. Nucl. Med. Mol. Imaging Annu. Meet, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00936229

E. Segal, C. Sirlin, C. Ooi, A. Adler, J. Gollub et al., Decoding global gene expression programs in liver cancer by noninvasive imaging, Nature Biotechnology, vol.101, issue.6, pp.675-80, 2007.
DOI : 10.1038/nbt1306

O. Gevaert, L. Mitchell, A. Achrol, J. Xu, S. Echegaray et al., Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features, Radiology, vol.273, issue.1, pp.168-74, 2014.
DOI : 10.1148/radiol.14131731

T. Wan, B. Bloch, D. Plecha, C. Thompson, H. Gilmore et al., A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores, Scientific Reports, vol.10, issue.1, p.21394, 2016.
DOI : 10.1371/journal.pone.0117900

F. Tixier, A. Groves, V. Goh, M. Hatt, P. Ingrand et al., Correlation of Intra-Tumor 18F-FDG Uptake Heterogeneity Indices with Perfusion CT Derived Parameters in Colorectal Cancer, PLoS ONE, vol.19, issue.1, p.99567, 2014.
DOI : 10.1371/journal.pone.0099567.t003

F. Tixier, M. Hatt, C. Rest, B. Simon, S. Key et al., Signaling pathways alteration involved in head and neck cancer can be identified through textural features analysis in 18F-FDG PET images: a prospective study, J. Nucl. Med, vol.56, pp.449-449, 2015.

I. Klyuzhin, M. Gonzalez, E. Shahinfard, N. Vafai, and V. Sossi, Exploring the use of shape and texture descriptors of positron emission tomography tracer distribution in imaging studies of neurodegenerative disease, Journal of Cerebral Blood Flow & Metabolism, vol.8, issue.6, 2015.
DOI : 10.2105/AJPH.86.5.726

A. Rahmim, Y. Salimpour, S. Jain, S. Blinder, I. Klyuzhin et al., Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments, NeuroImage: Clinical, vol.12
DOI : 10.1016/j.nicl.2016.02.012

T. Carlier and C. Bailly, State-Of-The-Art and Recent Advances in Quantification for Therapeutic Follow-Up in Oncology Using PET, Frontiers in Medicine, vol.14, issue.Suppl 1, p.18, 2015.
DOI : 10.3348/kjr.2013.14.1.1

S. Houshmand, A. Salavati, S. Hess, T. Werner, A. Alavi et al., An Update on Novel Quantitative Techniques in the Context of Evolving Whole-Body PET Imaging, PET Clinics, vol.10, issue.1, pp.45-58, 2015.
DOI : 10.1016/j.cpet.2014.09.004

M. Rahim, S. Kim, H. So, H. Kim, G. Cheon et al., Recent Trends in PET Image Interpretations Using Volumetric and Texture-based Quantification Methods in Nuclear Oncology, Nuclear Medicine and Molecular Imaging, vol.40, issue.Suppl 1, pp.1-15, 2014.
DOI : 10.1007/s13139-013-0260-2

N. Cheng, Y. Fang, and T. Yen, The promise and limits of PET texture analysis, Annals of Nuclear Medicine, vol.48, issue.9, pp.867-876, 2013.
DOI : 10.1007/s12149-013-0759-8

R. Bundschuh, J. Dinges, L. Neumann, M. Seyfried, N. Zsótér et al., Textural Parameters of Tumor Heterogeneity in 18 F-FDG PET/CT for Therapy Response Assessment and Prognosis in Patients with Locally Advanced Rectal Cancer, J. Nucl. Med. Off. Publ. Soc. Nucl. Med, vol.55, pp.891-898, 2014.

F. Tixier, M. Hatt, L. Rest, C. , L. Pogam et al., Reproducibility of Tumor Uptake Heterogeneity Characterization Through Textural Feature Analysis in 18F-FDG PET, Journal of Nuclear Medicine, vol.53, issue.5, pp.693-700, 2012.
DOI : 10.2967/jnumed.111.099127
URL : https://hal.archives-ouvertes.fr/inserm-00721377

F. Van-velden, G. Kramer, V. Frings, I. Nissen, E. Mulder et al., Repeatability of Radiomic Features in Non-Small-Cell Lung Cancer [(18)F]FDG-PET/CT Studies: Impact of Reconstruction and Delineation, Mol. Imaging Biol. MIB Off. Publ. Acad. Mol. Imaging, 2016.

L. Zhang, D. Fried, X. Fave, L. Hunter, Y. J. Court et al., : An open infrastructure software platform to facilitate collaborative work in radiomics, Medical Physics, vol.4, issue.3, pp.1341-53, 2015.
DOI : 10.1016/S0146-664X(75)80008-6

Y. Fang, C. Lin, M. Shih, H. Wang, T. Ho et al., Development and Evaluation of an Open-Source Software Package ???CGITA??? for Quantifying Tumor Heterogeneity with Molecular Images, BioMed Research International, vol.42, issue.4, p.248505, 2014.
DOI : 10.1007/978-3-642-22555-0_12

M. Vallières, C. Freeman, and S. Skamene, A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities, Physics in Medicine and Biology, vol.60, issue.14, pp.5471-96, 2015.
DOI : 10.1088/0031-9155/60/14/5471

R. Leijenaar, G. Nalbantov, S. Carvalho, W. Van-elmpt, E. Troost et al., The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis, Scientific Reports, vol.14, issue.1, p.11075, 2015.
DOI : 10.1038/srep11075

R. Leijenaar, S. Carvalho, E. Velazquez, W. Van-elmpt, C. Parmar et al., Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability, Acta Oncologica, vol.4, issue.7, pp.1391-1398, 2013.
DOI : 10.1080/10543400701329422

G. Doumou, M. Siddique, C. Tsoumpas, V. Goh, and G. Cook, The precision of textural analysis in 18F-FDG-PET scans of oesophageal cancer, European Radiology, vol.55, issue.9, pp.2805-2817, 2015.
DOI : 10.1007/s00330-015-3681-8

F. Orlhac, M. Soussan, J. Maisonobe, C. Garcia, B. Vanderlinden et al., Tumor Texture Analysis in 18F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis, Journal of Nuclear Medicine, vol.55, issue.3, pp.414-436, 2014.
DOI : 10.2967/jnumed.113.129858

M. Hatt, M. Majdoub, M. Vallières, F. Tixier, L. Rest et al., 18F-FDG PET Uptake Characterization Through Texture Analysis: Investigating the Complementary Nature of Heterogeneity and Functional Tumor Volume in a Multi-Cancer Site Patient Cohort, Journal of Nuclear Medicine, vol.56, issue.1, pp.38-44, 2015.
DOI : 10.2967/jnumed.114.144055

M. Hatt, C. Cheze-le-rest, A. Van-baardwijk, P. Lambin, O. Pradier et al., Impact of Tumor Size and Tracer Uptake Heterogeneity in 18F-FDG PET and CT Non-Small Cell Lung Cancer Tumor Delineation, Journal of Nuclear Medicine, vol.52, issue.11, pp.1690-1697, 2011.
DOI : 10.2967/jnumed.111.092767
URL : https://hal.archives-ouvertes.fr/hal-00703670

X. Dong, P. Wu, X. Sun, W. Li, H. Wan et al., F-FDG uptake heterogeneity decreases the reliability on target volume definition with positron emission tomography/computed tomography imaging, Journal of Medical Imaging and Radiation Oncology, vol.46, issue.12 Suppl, pp.338-383, 2015.
DOI : 10.1111/1754-9485.12289

X. Geets, J. Lee, A. Bol, M. Lonneux, and V. Gregoire, A gradient-based method for segmenting FDG-PET images: methodology and validation, European Journal of Nuclear Medicine and Molecular Imaging, vol.10, issue.Suppl 2, pp.1427-1465, 2007.
DOI : 10.1007/s00259-006-0363-4

A. Nelson, K. Brockway, A. Nelson, and J. Piper, PET Tumor Segmentation: Multi-observer Validation of a Gradient-Based Method using a NSCLC PET Phantom, International Journal of Radiation Oncology*Biology*Physics, vol.75, issue.3, 2009.
DOI : 10.1016/j.ijrobp.2009.07.1432

F. Hofheinz, J. Langner, J. Petr, B. Beuthien-baumann, J. Steinbach et al., An automatic method for accurate volume delineation of heterogeneous tumors in PET, Medical Physics, vol.58, issue.5, p.82503, 2013.
DOI : 10.1016/j.ijrobp.2003.12.008

M. Hatt, C. Le-rest, C. Descourt, P. Dekker, A. et al., Accurate Automatic Delineation of Heterogeneous Functional Volumes in Positron Emission Tomography for Oncology Applications, International Journal of Radiation Oncology*Biology*Physics, vol.77, issue.1, pp.301-309, 2010.
DOI : 10.1016/j.ijrobp.2009.08.018
URL : https://hal.archives-ouvertes.fr/inserm-00537776

F. Brooks and P. Grigsby, Current measures of metabolic heterogeneity within cervical cancer do not predict disease outcome, Radiation Oncology, vol.6, issue.1, p.69, 2011.
DOI : 10.2214/AJR.05.0039

D. Groheux, M. Majdoub, F. Tixier, L. Rest, C. Martineau et al., Do clinical, histological or immunohistochemical primary tumour characteristics translate into different 18F-FDG PET/CT volumetric and heterogeneity features in stage II/III breast cancer?, European Journal of Nuclear Medicine and Molecular Imaging, vol.43, issue.11, pp.1682-91, 2015.
DOI : 10.1007/s00259-015-3110-x
URL : https://hal.archives-ouvertes.fr/inserm-01171706

F. Brooks and P. Grigsby, FDG uptake heterogeneity in FIGO IIb cervical carcinoma does not predict pelvic lymph node involvement, Radiation Oncology, vol.8, issue.1, p.294, 2013.
DOI : 10.1086/623964

E. Kidd and P. Grigsby, Intratumoral Metabolic Heterogeneity of Cervical Cancer, Clinical Cancer Research, vol.14, issue.16, pp.5236-5277, 2008.
DOI : 10.1158/1078-0432.CCR-07-5252

F. Yang, M. Thomas, F. Dehdashti, and P. Grigsby, Temporal analysis of intratumoral metabolic heterogeneity characterized by textural features in cervical cancer, European Journal of Nuclear Medicine and Molecular Imaging, vol.19, issue.8, pp.716-743, 2013.
DOI : 10.1007/s00259-012-2332-4

M. Soussan, F. Orlhac, M. Boubaya, L. Zelek, M. Ziol et al., Relationship between Tumor Heterogeneity Measured on FDG-PET/CT and Pathological Prognostic Factors in Invasive Breast Cancer, PLoS ONE, vol.112, issue.4, p.94017, 2014.
DOI : 10.1371/journal.pone.0094017.s001

S. Larson, Y. Erdi, T. Akhurst, M. Mazumdar, H. Macapinlac et al., Tumor Treatment Response Based on Visual and Quantitative Changes in Global Tumor Glycolysis Using PET-FDG Imaging The Visual Response Score and the Change in Total Lesion Glycolysis, Clinical Positron Imaging, vol.2, issue.3, pp.159-71, 1999.
DOI : 10.1016/S1095-0397(99)00016-3

J. Yan, J. Lim, H. Loi, L. Khor, A. Sinha et al., Impact of Image Reconstruction Settings on Texture Features in 18F-FDG PET, Journal of Nuclear Medicine, vol.56, issue.11, 2015.
DOI : 10.2967/jnumed.115.156927

M. Hatt, F. Tixier, C. Le-rest, C. Pradier, O. Visvikis et al., Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma, European Journal of Nuclear Medicine and Molecular Imaging, vol.44, issue.Spec No 1, pp.1662-71, 2013.
DOI : 10.1007/s00259-013-2486-8

F. Brooks and P. Grigsby, The Effect of Small Tumor Volumes on Studies of Intratumoral Heterogeneity of Tracer Uptake, Journal of Nuclear Medicine, vol.55, issue.1, pp.37-42, 2014.
DOI : 10.2967/jnumed.112.116715

Q. Fdg-pet, FDG-PET/CT as an Imaging Biomarker Measuring Response to Cancer Therapy, Quantitative Imaging Biomarkers Alliance

S. Yip, K. Mccall, M. Aristophanous, A. Chen, H. Aerts et al., Comparison of Texture Features Derived from Static and Respiratory-Gated PET Images in Non-Small Cell Lung Cancer, PLoS ONE, vol.53, issue.12, p.115510, 2014.
DOI : 10.1371/journal.pone.0115510.t003

J. Oliver, M. Budzevich, G. Zhang, T. Dilling, K. Latifi et al., Variability of Image Features Computed from Conventional and Respiratory-Gated PET/CT Images of Lung Cancer, Translational Oncology, vol.8, issue.6, pp.524-558, 2015.
DOI : 10.1016/j.tranon.2015.11.013

N. Cheng, Y. Fang, D. Tsan, C. Hsu, and T. Yen, Respiration-Averaged CT for Attenuation Correction of PET Images ??? Impact on PET Texture Features in Non-Small Cell Lung Cancer Patients, PLOS ONE, vol.21, issue.24, p.150509, 2016.
DOI : 10.1371/journal.pone.0150509.s006

F. Tixier, D. Vriens, L. Rest, C. Hatt, M. Disselhorst et al., Comparison of Tumor Uptake Heterogeneity Characterization Between Static and Parametric 18F-FDG PET Images in Non-Small Cell Lung Cancer, Journal of Nuclear Medicine, vol.57, issue.7, 2016.
DOI : 10.2967/jnumed.115.166918

M. Nyflot, F. Yang, D. Byrd, S. Bowen, G. Sandison et al., Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards, Journal of Medical Imaging, vol.2, issue.4, p.41002, 2015.
DOI : 10.1117/1.JMI.2.4.041002

T. Pyka, R. Bundschuh, N. Andratschke, B. Mayer, H. Specht et al., Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy, Radiation Oncology, vol.25, issue.2, p.100, 2015.
DOI : 10.1186/s13014-015-0407-7

G. Cook, O. Brien, M. Siddique, M. Chicklore, S. Loi et al., F-FDG Uptake at PET???Association with Treatment Response and Prognosis, Radiology, vol.276, issue.3, pp.883-93, 2015.
DOI : 10.1148/radiol.2015141309

W. Mu, Z. Chen, Y. Liang, W. Shen, F. Yang et al., F-FDG PET images, Physics in Medicine and Biology, vol.60, issue.13, pp.5123-5162, 2015.
DOI : 10.1088/0031-9155/60/13/5123

J. Oh, B. Kang, J. Roh, J. Kim, K. Cho et al., Intratumor Textural Heterogeneity on Pretreatment 18F-FDG PET Images Predicts Response and Survival After Chemoradiotherapy for Hypopharyngeal Cancer, Annals of Surgical Oncology, vol.266, issue.Suppl 3, pp.2746-54, 2015.
DOI : 10.1245/s10434-014-4284-3

N. Cheng, Y. Fang, J. Chang, C. Huang, D. Tsan et al., Textural Features of Pretreatment 18F-FDG PET/CT Images: Prognostic Significance in Patients with Advanced T-Stage Oropharyngeal Squamous Cell Carcinoma, Journal of Nuclear Medicine, vol.54, issue.10, pp.1703-1712, 2013.
DOI : 10.2967/jnumed.112.119289

P. Lovinfosse, Z. Janvary, P. Coucke, S. Jodogne, C. Bernard et al., FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy, European Journal of Nuclear Medicine and Molecular Imaging, vol.102, issue.2, 2016.
DOI : 10.1007/s00259-016-3314-8

S. Yip, T. Coroller, N. Sanford, H. Mamon, H. Aerts et al., Relationship between the Temporal Changes in Positron-Emission-Tomography-Imaging-Based Textural Features and Pathologic Response and Survival in Esophageal Cancer Patients. Front, Oncol, vol.6, p.72, 2016.

N. Cheng, Y. Fang, L. Lee, J. Chang, D. Tsan et al., Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer, European Journal of Nuclear Medicine and Molecular Imaging, vol.41, issue.3, pp.419-447, 2015.
DOI : 10.1007/s00259-014-2933-1

R. Xu, S. Kido, K. Suga, Y. Hirano, R. Tachibana et al., Texture analysis on 18F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions, Annals of Nuclear Medicine, vol.55, issue.379???423, pp.926-961, 2014.
DOI : 10.1007/s12149-014-0895-9

J. Wu, T. Aguilera, D. Shultz, M. Gudur, D. Rubin et al., F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis, Radiology, vol.281, issue.1, 2016.
DOI : 10.1148/radiol.2016151829

P. Ypsilantis, M. Siddique, H. Sohn, A. Davies, G. Cook et al., Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks, PLOS ONE, vol.102, issue.2, p.137036, 2015.
DOI : 10.1371/journal.pone.0137036.t002

P. Van-rossum, D. Fried, L. Zhang, W. Hofstetter, M. Van-vulpen et al., The Incremental Value of Subjective and Quantitative Assessment of 18F-FDG PET for the Prediction of Pathologic Complete Response to Preoperative Chemoradiotherapy in Esophageal Cancer, Journal of Nuclear Medicine, vol.57, issue.5, 2016.
DOI : 10.2967/jnumed.115.163766

X. Gao, C. Chu, Y. Li, P. Lu, W. Wang et al., The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from 18F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer, European Journal of Radiology, vol.84, issue.2, pp.312-319, 2015.
DOI : 10.1016/j.ejrad.2014.11.006

M. Desseroit, D. Visvikis, F. Tixier, M. Majdoub, R. Guillevin et al., Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in Non-Small Cell Lung Cancer stage I-III, Eur. J. Nucl. Med. Mol. Imaging, 2016.
URL : https://hal.archives-ouvertes.fr/inserm-01285687

D. Fried, O. Mawlawi, L. Zhang, X. Fave, S. Zhou et al., Stage III Non???Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors, Radiology, vol.278, issue.1, 2015.
DOI : 10.1148/radiol.2015142920

N. Ohri, F. Duan, B. Snyder, B. Wei, M. Machtay et al., Pretreatment 18FDG-PET Textural Features in Locally Advanced Non-Small Cell Lung Cancer: Secondary Analysis of ACRIN 6668/RTOG 0235, J. Nucl. Med, 2016.

S. Hyun, H. Kim, S. Choi, D. Choi, J. Lee et al., Intratumoral heterogeneity of 18F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma, European Journal of Nuclear Medicine and Molecular Imaging, vol.56, issue.8, pp.1-8, 2016.
DOI : 10.1007/s00259-016-3316-6

C. Lartizien, M. Rogez, E. Niaf, and R. F. , Computer-Aided Staging of Lymphoma Patients With FDG PET/CT Imaging Based on Textural Information, IEEE Journal of Biomedical and Health Informatics, vol.18, issue.3, pp.946-55, 2014.
DOI : 10.1109/JBHI.2013.2283658
URL : https://hal.archives-ouvertes.fr/hal-00830260

J. Bang, S. Ha, S. Kang, K. Lee, H. Lee et al., Prediction of neoadjuvant radiation chemotherapy response and survival using pretreatment [(18)F]FDG PET/CT scans in locally advanced rectal cancer, Eur. J. Nucl. Med. Mol. Imaging, 2015.

M. Vaidya, K. Creach, J. Frye, F. Dehdashti, and J. Bradley, Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer, Radiotherapy and Oncology, vol.102, issue.2, pp.239-284, 2012.
DOI : 10.1016/j.radonc.2011.10.014

J. Antunes, S. Viswanath, M. Rusu, L. Valls, C. Hoimes et al., 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.155-62, 2016.
DOI : 10.1016/j.tranon.2016.01.008

H. Wang, N. Cheng, L. Lee, Y. Fang, J. Chang et al., F-FDG PET combined with expression of EGFR may improve the prognostic stratification of advanced oropharyngeal carcinoma, International Journal of Cancer, vol.11, issue.3, pp.731-739, 2016.
DOI : 10.1002/ijc.29811

C. Parmar, P. Grossmann, D. Rietveld, M. Rietbergen, P. Lambin et al., Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer, Frontiers in Oncology, vol.5, issue.7, p.272, 2015.
DOI : 10.1038/srep11075

H. Yoon, I. Sohn, J. Cho, H. Lee, J. Kim et al., Decoding Tumor Phenotypes for ALK, ROS1, and RET Fusions in Lung Adenocarcinoma Using a Radiomics Approach, Medicine, vol.94, issue.41, p.1753, 2015.
DOI : 10.1097/MD.0000000000001753

T. Upadhaya, Y. Morvan, E. Stindel, L. Reste, P. Hatt et al., A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in Glioblastoma Multiforme, IRBM, vol.36, issue.6, 2015.
DOI : 10.1016/j.irbm.2015.08.001
URL : https://hal.archives-ouvertes.fr/inserm-01314340

J. Wang, F. Kato, N. Oyama-manabe, R. Li, Y. Cui et al., Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study, PLOS ONE, vol.44, issue.7, p.143308, 2015.
DOI : 10.1371/journal.pone.0143308.s002

A. Cameron, F. Khalvati, M. Haider, and A. Wong, MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection, IEEE Transactions on Biomedical Engineering, vol.63, issue.6, 2015.
DOI : 10.1109/TBME.2015.2485779

F. Khalvati, A. Wong, and M. Haider, Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models, BMC Medical Imaging, vol.266, issue.3, p.27, 2015.
DOI : 10.1186/s12880-015-0069-9

R. Sharma and P. Marikkannu, Hybrid RGSA and Support Vector Machine Framework for Three- Dimensional Magnetic Resonance Brain Tumor Classification, ScientificWorldJournal, vol.2015, p.184350, 2015.

H. Mi, C. Petitjean, B. Dubray, P. Vera, and S. Ruan, Robust feature selection to predict tumor treatment outcome, Artificial Intelligence in Medicine, vol.64, issue.3, pp.195-204, 2015.
DOI : 10.1016/j.artmed.2015.07.002
URL : https://hal.archives-ouvertes.fr/hal-01153463

N. Pandis and Z. Fedorowicz, The international EQUATOR network: enhancing the quality and transparency of health care research, Journal of Applied Oral Science, vol.19, issue.5, 2011.
DOI : 10.1590/S1678-77572011000500001