E. Bibliographie1-]-limkin, R. Sun, L. Dercle, E. Zacharaki, C. Robert et al., Promises and challenges for the implementation of computational medical imaging (radiomics)

A. Oncol-off, J. Eur-soc-med-oncol-aerts, H. Velazquez, E. Leijenaar, R. Parmar et al., Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach, Hricak H. Radiomics: Images Are More than Pictures, 2014.

I. Heterogeneity-reuzé, S. Orlhac, F. Chargari, C. Nioche, C. Limkin et al., Prediction of cervical cancer recurrence using textural features extracted from 18 F-FDG PET images acquired with different scanners, Therapy Response, Resistance, and Clinical Outcome, pp.249-571544, 2015.

C. Robert, G. Long, B. Brady, C. Dutriaux, M. Maio et al., Mutation, New England Journal of Medicine, vol.372, issue.4, pp.320-350, 2015.
DOI : 10.1056/NEJMoa1412082
URL : https://hal.archives-ouvertes.fr/hal-00872683

S. Ansell and . Hodgkin-lymphoma, Hodgkin Lymphoma: Diagnosis and Treatment, Mayo Clinic Proceedings, vol.90, issue.11, pp.1574-83, 2015.
DOI : 10.1016/j.mayocp.2015.07.005
URL : http://onlinelibrary.wiley.com/doi/10.1002/ajh.23750/pdf

D. Chen and I. Mellman, Elements of cancer immunity and the cancer???immune set point, Nature, vol.121, issue.7637, pp.321-351, 2017.
DOI : 10.1172/JCI57834

J. Bushberg and J. Boone, The Essential Physics of Medical Imaging, Medical Physics, vol.30, issue.7, 2011.
DOI : 10.1118/1.1585033

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

B. Zhao, Y. Tan, W. Tsai, J. Qi, C. Xie et al., Reproducibility of radiomics for deciphering tumor phenotype with imaging, Scientific Reports, vol.9, issue.1, pp.23428-23438, 2016.
DOI : 10.1371/journal.pone.0102107

Y. Balagurunathan, V. Kumar, Y. Gu, J. Kim, H. Wang et al., Test???Retest Reproducibility Analysis of Lung CT Image Features, Journal of Digital Imaging, vol.16, issue.18, pp.805-828, 2014.
DOI : 10.1158/1078-0432.CCR-10-0125
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4391075/pdf

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
URL : http://jnm.snmjournals.org/content/55/3/414.full.pdf

E. Velazquez, C. Parmar, M. Jermoumi, R. Mak, A. Van-baardwijk et al., Volumetric CT-based segmentation of NSCLC using 3D-Slicer, Scientific Reports, vol.91, issue.1, 2013.
DOI : 10.1016/j.radonc.2009.03.006

S. Gorthi, M. Bach-cuadra, and J. Thiran, Exporting Contours to DICOM-RT Structure Set, Insight J, 2009.

W. Wells, Efficient Synthesis of Gaussian Filters by Cascaded Uniform Filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.8, issue.2, 1986.
DOI : 10.1109/TPAMI.1986.4767776

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.10, 1038.
DOI : 10.1186/1471-2407-14-130

F. Orlhac, M. Soussan, K. Chouahnia, E. Martinod, and I. Buvat, 18F-FDG PET-Derived Textural Indices Reflect Tissue-Specific Uptake Pattern in Non-Small Cell Lung Cancer, PLOS ONE, vol.30, issue.9, 2015.
DOI : 10.1371/journal.pone.0145063.s005
URL : https://doi.org/10.1371/journal.pone.0145063

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. 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

R. Haralick, K. Shanmugam, and I. Dinstein, Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, vol.3, issue.6
DOI : 10.1109/TSMC.1973.4309314
URL : http://www.cis.rit.edu/~cnspci/references/dip/segmentation/haralick1973.pdf

M. Galloway, Texture analysis using gray level run lengths, Computer Graphics and Image Processing, vol.4, issue.2, pp.172-181, 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
DOI : 10.1016/0146-664x(82)90093-4

S. Narang, M. Lehrer, D. Yang, J. Lee, and A. Rao, Radiomics in glioblastoma: current status, challenges and potential opportunities, Translational Cancer Research, vol.5, issue.4, pp.383-97, 2016.
DOI : 10.21037/tcr.2016.06.31

R. Bellman, Adaptive control processes: a guided tour. Princeton: Princeton university press, 1961.

R. Clarke, H. Ressom, A. Wang, J. Xuan, M. Liu et al., The properties of high-dimensional data spaces: implications for exploring gene and protein expression data, Nature Reviews Cancer, vol.29, issue.1
DOI : 10.1002/sim.1263

C. Ferte, A. Trister, E. Huang, B. Bot, J. Guinney et al., Impact of Bioinformatic Procedures in the Development and Translation of High-Throughput Molecular Classifiers in Oncology, Clinical Cancer Research, vol.19, issue.16, pp.4315-4340, 2013.
DOI : 10.1158/1078-0432.CCR-12-3937

S. A. Holm, Simple Sequentially Rejective Multiple Test Procedure, Scand J Stat, vol.6, pp.65-70, 1979.

Y. Hy and B. , More powerful procedures for multiple significance testing. -PubMed - NCBI n.d. https://vpn.igr.fr, p.2218183, 2017.

H. Peng, F. Long, and C. Ding, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.8, pp.1226-1264, 2005.
DOI : 10.1109/TPAMI.2005.159

I. Kononenko, Estimating attributes: Analysis and extensions of RELIEF, Mach. Learn
DOI : 10.1007/3-540-57868-4_57
URL : http://ai.fri.uni-lj.si/papers/kononenko94-ecml.ps.gz

R. Kohavi and G. John, Wrappers for feature subset selection, Artificial Intelligence, vol.97, issue.1-2, pp.273-324, 1997.
DOI : 10.1016/S0004-3702(97)00043-X
URL : https://doi.org/10.1016/s0004-3702(97)00043-x

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.5, issue.2, pp.301-321, 2005.
DOI : 10.1073/pnas.201162998

R. Fisher, . The, . Use, . Multiple, . In et al., THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS, Annals of Eugenics, vol.59, issue.2, pp.179-88, 1936.
DOI : 10.1111/j.1469-1809.1936.tb02137.x

J. Tenenbaum, V. De-silva, and J. Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, vol.290, issue.5500, pp.2319-2342, 2000.
DOI : 10.1126/science.290.5500.2319

S. Roweis and L. Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, vol.290, issue.5500
DOI : 10.1126/science.290.5500.2323

G. Hinton, Reducing the Dimensionality of Data with Neural Networks, Science, vol.313, issue.5786, pp.504-511, 2006.
DOI : 10.1126/science.1127647

J. Shao, Linear Model Selection by Cross-validation, Journal of the American Statistical Association, vol.39, issue.422, pp.486-94, 1993.
DOI : 10.1080/03610927508827223

D. Kumar, M. Shafiee, A. Chung, F. Khalvati, M. Haider et al., Discovery Radiomics for Computed Tomography Cancer Detection, 2015.
DOI : 10.1007/978-3-319-59876-5_7

S. Hawkins, H. Wang, Y. Liu, A. Garcia, O. Stringfield et al., Predicting Malignant Nodules from Screening CT Scans, Journal of Thoracic Oncology, vol.11, issue.12, 2016.
DOI : 10.1016/j.jtho.2016.07.002

W. Shen, M. Zhou, F. Yang, C. Yang, J. Tian et al., Multi-scale Convolutional Neural Networks for Lung Nodule Classification
DOI : 10.1007/978-3-319-19992-4_46

E. Zacharaki, N. Morita, P. Bhatt, O. Rourke, D. Melhem et al., Survival Analysis of Patients with High-Grade Gliomas Based on Data Mining of Imaging Variables, American Journal of Neuroradiology, vol.90, issue.19
DOI : 10.1093/jnci/90.19.1473

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.821-829, 2011.
DOI : 10.1148/radiol.2432060493

L. Al, Radiological Image traits Predictive of Cancer Status in Pulmonary Nodules. - PubMed - NCBI n.d. https://vpn.igr.fr, p.27663588, 2016.

J. Wang, X. Liu, D. Dong, J. Song, M. Xu et al., 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.1272-1277, 2016.
DOI : 10.1109/EMBC.2016.7590938

K. Nie, L. Shi, Q. Chen, X. Hu, S. Jabbour et al., Rectal Cancer: Assessment of Neoadjuvant Chemo-Radiation Outcome Based on Radiomics of Multi-Parametric MRI, Clin Cancer Res, 2016.

T. Hastie, R. Tibshirani, and J. Friedman, Unsupervised learning, Elem. Stat. Learn, pp.485-585, 2009.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.9, issue.7553, pp.436-480, 2015.
DOI : 10.1007/s10994-013-5335-x

Q. Zhang, Y. Xiao, W. Dai, J. Suo, C. Wang et al., Deep learning based classification of breast tumors with shear-wave elastography, Ultrasonics, vol.72, pp.150-157, 2016.
DOI : 10.1016/j.ultras.2016.08.004

J. Cheng, D. Ni, Y. Chou, J. Qin, C. Tiu et al., Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans, Scientific Reports, vol.38, issue.1, p.24454, 2016.
DOI : 10.1118/1.3528204

K. Sohn, G. Zhou, C. Lee, and H. Lee, Learning and Selecting Features Jointly with Pointwise Gated Boltzmann Machines, pp.217-242, 2013.

G. Carneiro, L. Oakden-rayner, A. Bradley, J. Nascimento, and L. Palmer, Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2016.
DOI : 10.1109/ISBI.2017.7950485

D. Hanahan and R. Weinberg, Hallmarks of Cancer: The Next Generation, Cell, vol.144, issue.5, pp.646-74, 2011.
DOI : 10.1016/j.cell.2011.02.013

J. Couzin-frankel, Cancer Immunotherapy, Science, vol.342, issue.6165, pp.1432-1435, 2013.
DOI : 10.1126/science.342.6165.1432

C. Robert, J. Schachter, G. Long, A. Arance, J. Grob et al., Pembrolizumab versus Ipilimumab in Advanced Melanoma, New England Journal of Medicine, vol.372, issue.26, pp.2521-2553, 2015.
DOI : 10.1056/NEJMoa1503093

W. Zou, J. Wolchok, and L. Chen, PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: Mechanisms, response biomarkers, and combinations, Science Translational Medicine, vol.120, issue.2, 2016.
DOI : 10.1172/JCI39397
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4859220/pdf

R. Herbst, J. Soria, M. Kowanetz, G. Fine, O. Hamid et al., Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients, Nature, vol.9, issue.7528, pp.563-570, 2014.
DOI : 10.1371/journal.pone.0088401

J. Kim and D. Chen, Immune escape to PD-L1/PD-1 blockade: seven steps to success (or failure), Annals of Oncology, vol.27, issue.8, pp.1492-504, 2016.
DOI : 10.1093/annonc/mdw217
URL : https://academic.oup.com/annonc/article-pdf/27/8/1492/6679530/mdw217.pdf

W. Hugo, J. Zaretsky, L. Sun, C. Song, B. Moreno et al., Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma

J. Rosenberg, J. Hoffman-censits, T. Powles, M. Van-der-heijden, A. Balar et al., Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial, The Lancet, vol.387, issue.10031
DOI : 10.1016/S0140-6736(16)00561-4

P. Hegde, V. Karanikas, and S. Evers, The Where, the When, and the How of Immune Monitoring for Cancer Immunotherapies in the Era of Checkpoint Inhibition, Clinical Cancer Research, vol.22, issue.8, pp.1865-74, 2016.
DOI : 10.1158/1078-0432.CCR-15-1507

M. Orooji, S. Rakshit, N. Beig, A. Madabhushi, and V. Velcheti, Computerized textural analysis of lung CT to enable quantification of tumor infiltrating lymphocytes in NSCLC, J

C. Tang, A. Amer, B. Hobbs, X. Li, C. Behrens et al., Pathology-Based Non-Small Cell Lung Cancer Radiomics Signature Describing the Local Tumor Immune Environment: Discovery and Validation, International Journal of Radiation Oncology*Biology*Physics, vol.96, issue.2, pp.42-45, 2016.
DOI : 10.1016/j.ijrobp.2016.06.114

M. Teng, S. Ngiow, A. Ribas, and M. Smyth, Classifying Cancers Based on T-cell Infiltration and PD-L1, Cancer Research, vol.75, issue.11, pp.2139-2184, 2015.
DOI : 10.1158/0008-5472.CAN-15-0255
URL : http://cancerres.aacrjournals.org/content/canres/75/11/2139.full.pdf

K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby et al., The Cancer Imaging

. Archive, Maintaining and Operating a Public Information Repository, J Digit Imaging, vol.26, pp.1045-57, 2013.

D. Mackin, X. Fave, L. Zhang, D. Fried, Y. J. Taylor et al., Measuring Computed Tomography Scanner Variability of Radiomics Features, Investigative Radiology, vol.50, issue.11, pp.757-65, 2015.
DOI : 10.1097/RLI.0000000000000180