S. Hess, B. Blomberg, H. Zhu, P. Høilund-carlsen, and A. Alavi, The Pivotal Role of FDG-PET/CT in Modern Medicine, Academic Radiology, vol.21, issue.2, pp.232-281, 2014.
DOI : 10.1016/j.acra.2013.11.002

K. Herrmann, M. Benz, B. Krause, K. Pomykala, A. Buck et al., 18)F-FDG-PET/CT in evaluating response to therapy in solid tumors: where we are and where we can go, Q J Nucl Med Mol Imaging, vol.55, pp.620-652, 2011.

G. Pereira, M. Traughber, and R. Muzic, The Role of Imaging in Radiation Therapy Planning: Past, Present, and Future, BioMed Research International, vol.38, issue.2, p.231090, 2014.
DOI : 10.1038/sj.onc.1206681

R. Wahl, H. Jacene, Y. Kasamon, and M. Lodge, From RECIST to PERCIST: Evolving Considerations for PET Response Criteria in Solid Tumors, Journal of Nuclear Medicine, vol.50, issue.Suppl_1, pp.122-50, 2009.
DOI : 10.2967/jnumed.108.057307

D. Singh and K. Miles, Multiparametric PET/CT in oncology, Cancer Imaging, vol.12, issue.2, pp.336-380, 2012.
DOI : 10.1102/1470-7330.2012.9007

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

D. Longo, Tumor Heterogeneity and Personalized Medicine, New England Journal of Medicine, vol.366, issue.10, pp.956-963, 2012.
DOI : 10.1056/NEJMe1200656

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

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

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

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.

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

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.

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

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

L. Alic, W. Niessen, and J. Veenland, Quantification of Heterogeneity as a Biomarker in Tumor Imaging: A Systematic Review, PLoS ONE, vol.23, issue.10, p.110300, 2014.
DOI : 10.1371/journal.pone.0110300.s006

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

K. Miwa, M. Inubushi, K. Wagatsuma, M. Nagao, T. Murata et al., FDG uptake heterogeneity evaluated by fractal analysis improves the differential diagnosis of pulmonary nodules, European Journal of Radiology, vol.83, issue.4, pp.715-724, 2014.
DOI : 10.1016/j.ejrad.2013.12.020

B. Bai, J. Bading, and P. Conti, Tumor Quantification in Clinical Positron Emission Tomography, Theranostics, vol.3, issue.10, pp.787-801, 2013.
DOI : 10.7150/thno.5629

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

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

O. Sullivan, F. Wolsztynski, E. , O. Sullivan, J. Richards et al., A Statistical Modeling Approach to the Analysis of Spatial Patterns of FDG-PET Uptake in Human Sarcoma, IEEE Trans Med Imaging [Internet], 2011.

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

I. Apostolova, I. Steffen, F. Wedel, A. Lougovski, S. Marnitz et al., Asphericity of pretherapeutic tumour FDG uptake provides independent prognostic value in head-and-neck cancer, European Radiology, vol.52, issue.9, pp.2077-87, 2014.
DOI : 10.1007/s00330-014-3269-8

F. Hofheinz, A. Lougovski, K. Zöphel, M. Hentschel, I. Steffen et al., Increased evidence for the prognostic value of primary tumor asphericity in pretherapeutic FDG PET for risk stratification in patients with head and neck cancer, European Journal of Nuclear Medicine and Molecular Imaging, vol.37, issue.1, 2014.
DOI : 10.1007/s00259-014-2953-x

I. Apostolova, J. Rogasch, R. Buchert, H. Wertzel, H. Achenbach et al., Quantitative assessment of the asphericity of pretherapeutic FDG uptake as an independent predictor of outcome in NSCLC, BMC Cancer, vol.97, issue.4, p.896, 2014.
DOI : 10.1016/j.athoracsur.2013.12.045

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.1391-1398, 2013.
DOI : 10.1080/10543400701329422

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

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

F. Van-velden, I. Nissen, F. Jongsma, L. Velasquez, W. Hayes et al., Test-Retest Variability of Various Quantitative Measures to Characterize Tracer Uptake and/or Tracer Uptake Heterogeneity in Metastasized Liver for Patients with Colorectal Carcinoma, Molecular Imaging and Biology, vol.45, issue.Suppl 1, pp.13-21, 2014.
DOI : 10.1007/s11307-013-0660-9

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

U. Tateishi, M. Tatsumi, T. Terauchi, K. Ando, N. Niitsu et al., Prognostic Significance of Metabolic Tumor Burden by PET/CT in Patients with Relapsed/Refractory Diffuse Large B-cell Lymphoma, Cancer Sci, 2014.

S. Kang, H. Song, B. Byun, J. Oh, H. Kim et al., Intratumoral Metabolic Heterogeneity for Prediction of Disease Progression After Concurrent Chemoradiotherapy in Patients with Inoperable Stage III Non-Small-Cell Lung Cancer, Nuclear Medicine and Molecular Imaging, vol.270, issue.1, pp.16-25, 2014.
DOI : 10.1007/s13139-013-0231-7

F. Brooks, Area under the cumulative SUV-volume histogram is not a viable metric of intratumoral metabolic heterogeneity: further comments, European Journal of Nuclear Medicine and Molecular Imaging, vol.38, issue.9, 2013.
DOI : 10.1007/s00259-013-2572-y

F. Van-velden and R. Boellaard, Reply to: Area under the cumulative SUV-volume histogram is not a viable metric of intratumoral metabolic heterogeneity, European Journal of Nuclear Medicine and Molecular Imaging, vol.48, issue.Suppl 1, pp.1469-70, 2013.
DOI : 10.1007/s00259-013-2474-z

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

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

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

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

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, pp.187-203, 2013.
DOI : 10.1088/0031-9155/58/2/187

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

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

S. Son, D. Kim, C. Hong, C. Kim, S. Jeong et al., Prognostic implication of intratumoral metabolic heterogeneity in invasive ductal carcinoma of the breast, BMC Cancer, vol.27, issue.1, p.585, 2014.
DOI : 10.1200/JCO.2007.13.7083

B. Koolen, S. Vidal-sicart, B. Baviera, J. , V. Olmos et al., Evaluating heterogeneity of primary tumor 18F-FDG uptake in breast cancer with a dedicated breast PET (MAMMI), Nuclear Medicine Communications, vol.35, issue.5, pp.446-52, 2014.
DOI : 10.1097/MNM.0000000000000072

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

S. Tan, S. Kligerman, W. Chen, M. Lu, G. Kim et al., Spatial-Temporal [18F]FDG-PET Features for Predicting Pathologic Response of Esophageal Cancer to Neoadjuvant Chemoradiation Therapy, International Journal of Radiation Oncology*Biology*Physics, vol.85, issue.5, pp.1375-82, 2013.
DOI : 10.1016/j.ijrobp.2012.10.017

N. Cheng, Y. Fang, L. Lee, J. Chang, D. Tsan et al., Zone-size nonuniformity of (18)F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer

J. Oh, B. Kang, J. Roh, J. Kim, K. Cho et al., Intratumor Textural Heterogeneity on Pretreatment (18)F-FDG PET Images Predicts Response and Survival After Chemoradiotherapy for Hypopharyngeal Cancer, Ann. Surg. Oncol, 2014.

C. Lapa, R. Werner, J. Schmid, L. Papp, N. Zsótér et al., Prognostic value of positron emission tomography-assessed tumor heterogeneity in patients with thyroid cancer undergoing treatment with radiopeptide therapy, Nuclear Medicine and Biology, vol.42, issue.4, 2014.
DOI : 10.1016/j.nucmedbio.2014.12.006

G. Cook, C. Yip, M. Siddique, V. Goh, S. Chicklore et al., Are Pretreatment 18F-FDG PET Tumor Textural Features in Non-Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy?, Journal of Nuclear Medicine, vol.54, issue.1, pp.19-26, 2013.
DOI : 10.2967/jnumed.112.107375

S. Koyasu, Y. Nakamoto, M. Kikuchi, K. Suzuki, K. Hayashida et al., F-FDG PET/CT Parameters Including Visual Evaluation in Patients With Head and Neck Squamous Cell Carcinoma, American Journal of Roentgenology, vol.202, issue.4, pp.851-859, 2014.
DOI : 10.2214/AJR.13.11013

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

V. Nair, O. Gevaert, G. Davidzon, S. Napel, E. Graves et al., Prognostic PET 18F-FDG Uptake Imaging Features Are Associated with Major Oncogenomic Alterations in Patients with Resected Non-Small Cell Lung Cancer, Cancer Research, vol.72, issue.15, pp.3725-3759, 2012.
DOI : 10.1158/0008-5472.CAN-11-3943

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

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4725327

F. Tixier, M. Hatt, L. Chezeaud, C. Corcos, C. Le-rest et al., 18F-FDG PET derived quantitative heterogeneity features reflect gene expression profiles in head and neck cancer, Soc. Nucl. Med. Mol. Imaging Annu. Meet, 2014.

B. Hoeben, M. Starmans, R. Leijenaar, L. Dubois, A. Van-der-kogel et al., Systematic analysis of 18F-FDG PET and metabolism, proliferation and hypoxia markers for classification of head and neck tumors, BMC Cancer, vol.52, issue.1, p.130, 2014.
DOI : 10.3109/0284186X.2013.812799

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

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.

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

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

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

T. Upadhaya, Y. Morvan, E. Stindel, P. Reste, and M. Hatt, Prognosis in Glioblastoma Multiforme patients using textural features analysis of multimodal MRI sequences, IEEE Int. Symp. Biomed. Imaging Nano Macro, 2015.

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, 2014.
DOI : 10.1016/j.neurad.2014.02.006