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.883-892, 2012.
DOI : 10.1056/NEJMoa1113205

A. Pugachev, S. Ruan, S. Carlin, S. Larson, J. Campa et al., Dependence of FDG uptake on tumor microenvironment, International Journal of Radiation Oncology*Biology*Physics, vol.62, issue.2, pp.545-553, 2005.
DOI : 10.1016/j.ijrobp.2005.02.009

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.249-257, 2015.
DOI : 10.1158/1078-0432.CCR-14-0990

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.

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.441-446, 2012.
DOI : 10.1016/j.ejca.2011.11.036
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4533986

D. Visvikis, M. Hatt, F. Tixier, C. Le-rest, and C. , The age of reason for FDG PET image-derived indices, European Journal of Nuclear Medicine and Molecular Imaging, vol.25, issue.3, pp.1670-1672, 2012.
DOI : 10.1007/s00259-012-2239-0
URL : https://hal.archives-ouvertes.fr/inserm-00733622

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.133-140, 2013.
DOI : 10.1007/s00259-012-2247-0

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, pp.4006-4016, 2014.
DOI : 10.1038/ncomms5006

G. Cook, M. Siddique, B. Taylor, C. Yip, S. Chicklore et al., Radiomics in PET: principles and applications, Clinical and Translational Imaging, vol.264, issue.3, pp.269-276, 2014.
DOI : 10.1007/s40336-014-0064-0

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

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.24722644, 2014.
DOI : 10.1371/journal.pone.0094017.s001

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.369-378, 2011.
DOI : 10.2967/jnumed.110.082404
URL : https://hal.archives-ouvertes.fr/inserm-00574272

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

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.2746-2754, 2014.
DOI : 10.1245/s10434-014-4284-3

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.419-428, 2015.
DOI : 10.1007/s00259-014-2933-1

W. Mu, Z. Chen, Y. Liang, W. Shen, F. Yang et al., Staging of cervical cancer based on tumor heterogeneity characterized by texture features on 18 F-FDG PET images, Phys Med Biol, vol.6060135123, pp.5123-5139, 2015.

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.1235-1241, 2014.
DOI : 10.2967/jnumed.113.133389
URL : https://hal.archives-ouvertes.fr/inserm-01074715

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, pp.100-25900186, 2015.
DOI : 10.1186/s13014-015-0407-7

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

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.1391-1397, 2013.
DOI : 10.1080/10543400701329422

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.414-422, 2014.
DOI : 10.2967/jnumed.113.129858

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.26242464, 2015.
DOI : 10.1038/srep11075

M. Hatt, F. Tixier, C. Le-rest, C. Pradier, O. Visvikis et al., Robustness of intratumour 18 F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma, Eur J Nucl Med Mol Imaging, p.23857457

T. Carlier, C. Bailly, M. Hatt, F. Kraeber-bodéré, D. Visvikis et al., Quantification of intratumor heterogeneity derived from baseline FDG PET/CT in untreated mantle cell lymphoma patients enrolled in a prospective phase III trial of the LYSA group: preliminary results, J Nucl Med Meeting Abstracts, vol.56, p.429, 2015.

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.1012-1016, 2010.
DOI : 10.1016/j.patcog.2008.08.011

J. Yan, J. Chu-shern, 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, pp.1667-1673, 2015.
DOI : 10.2967/jnumed.115.156927

J. Scheuermann, J. Saffer, J. Karp, A. Levering, and B. Siegel, Qualification of PET Scanners for Use in Multicenter Cancer Clinical Trials: The American College of Radiology Imaging Network Experience, Journal of Nuclear Medicine, vol.50, issue.7, pp.1187-1193, 2009.
DOI : 10.2967/jnumed.108.057455

R. Boellaard, R. Delgado-bolton, W. Oyen, F. Giammarile, K. Tatsch et al., FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0, European Journal of Nuclear Medicine and Molecular Imaging, vol.28, issue.10, pp.328-354, 2015.
DOI : 10.1007/s00259-014-2961-x
URL : http://doi.org/10.1007/s00259-014-2961-x

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.25938522, 2015.
DOI : 10.1371/journal.pone.0124165.s004

S. Vauclin, K. Doyeux, S. Hapdey, A. Edet-sanson, P. Vera et al., Development of a generic thresholding algorithm for the delineation of 18FDG-PET-positive tissue: application to the comparison of three thresholding models, Phys Med Biol, vol.545422, pp.6901-6916, 2009.
URL : https://hal.archives-ouvertes.fr/inserm-00467195

I. Buvat, F. Orlhac, and M. Soussan, Tumor Texture Analysis in PET: Where Do We Stand?, Journal of Nuclear Medicine, vol.56, issue.11, pp.1642-1644, 2015.
DOI : 10.2967/jnumed.115.163469

E. Naqa, I. Grisby, 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.1162-1171, 2009.
DOI : 10.1016/j.patcog.2008.08.011

C. Bodet-milin, C. Bailly, M. Meignan, A. Beriollo-riedinger, A. Devillers et al., Prognosis value of quantitative indices derived from initial FDG PET/CT in untreated mantle cell lymphoma patients enrolled in the Lyma trial, a LYSA study. Preliminary results, J Nucl Med Meeting Abstracts, vol.56, p.659, 2015.

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.946-955, 2014.
DOI : 10.1109/JBHI.2013.2283658
URL : https://hal.archives-ouvertes.fr/hal-00830260

M. Adams, T. Turkington, J. Wilson, and T. Wong, A Systematic Review of the Factors Affecting Accuracy of SUV Measurements, American Journal of Roentgenology, vol.195, issue.2, pp.310-320, 2010.
DOI : 10.2214/AJR.10.4923