R. J. Gillies, P. E. Kinahan, and H. Hricak, Radiomics: Images are more than pictures, they are data, Radiology, vol.2, pp.563-77, 2016.

P. Lambin, Radiomics: Extracting more information from medical imagesusing advanced feature analysis, Eur. J. Cancer, vol.48, pp.441-446, 2012.

V. Kumar, Radiomics: The process and the challenges, Magn. Respn. Imag, vol.30, pp.1234-1248, 2012.

R. T. Laure, G. Defraene, D. De-ruysscher, P. Lambin, and W. Van-elmpt, Quantitativeradiomics studies for tissue characterization: A review of technology and methodological procedures, Brit. J. Radiol, p.90, 2017.

P. Lambin, Radiomics: extracting more information from medical images using advanced feature analysis, European journal of cancer, vol.48, pp.441-446, 2012.

P. Leijenaar, Radiomics: the bridge between medical imaging and personalized medicine, Nature Reviews Clinical Oncology, vol.14, p.749, 2017.

C. Sun, Radiomic analysis for pretreatment predictionof response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study, EBioMedicine, vol.46, pp.160-169, 2019.

G. Dissaux, Pre-treatment 18f-fdg pet/ct radiomics predict local recurrence in patients treated with stereotactic radiotherapy for early-stage non-small cell lung cancer: a multicentric study, J Nucl Med, 2019.

F. Lucia, External validation of a combined pet and mri radiomics for prediction of recurrence in cervical cancer patients treated with chemotheraphy, Eur J Nucl Med Mol Imaging, vol.46, pp.864-877, 2019.

Z. C. Bai, Multiregional radiomics features from multiparametric mri for prediction of mgmt methylation status in glioblastoma multiforme: A multicentre study, Eur Radiol, vol.28, pp.3640-3650, 2018.

A. Zwanenburg and S. Löck, Why validation of prognostic models matters?, Radiother Oncol, vol.127, pp.370-373, 2018.

M. Hatt, F. Lucia, U. Schick, and D. Visvikis, Multicentric validation of radiomics findings:challenges and opportunities, EBioMedicine, vol.47, pp.20-21, 2019.

P. E. Galavis, C. Hollensen, N. Jallow, B. Paliwal, and R. Jeraj, Variability of textural featuresin fdg pet images due to different acquisition modes and reconstruction parameters, Acta Oncol, vol.49, pp.1012-1016, 2010.

J. Yan, Impact of image reconstruction settings on texture features in 18f-fdg pet, J Nucl Med, vol.56, pp.1667-1673, 2015.

J. Peerlings, Stability ofradiomics features in apparent diffusion coefficient maps from a multi-centre test-retest trial, Sci Rep, vol.9, 2019.

M. Shafiq-ui-hassan, Intrinsic dependencies of ct radiomic features on voxel size and number of gray levels, Med Phys, vol.44, pp.1050-1062, 2007.

R. Luo, Radiomics features harmonization for ct and cbct in rectal cancer, Radiotherapy and Oncology, vol.123, issue.17, pp.30603-30608, 2017.

R. Boellaard, Fdg pet/ct: Eanm procedureguidelines for tumour imaging: version 2.0, Eur J Nucl Med Mol Imaging, vol.42, pp.328-354, 2015.

A. Kaalep, Feasibility of state of the art pet/ct systems for performance harmonization, Eur J Nucl Med Mol Imaging, vol.45, pp.1344-1361, 2018.

J. Choe, Deep learning-based image conversion of ct reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses, Radiology, vol.292, pp.365-373, 2019.

C. Hognon, Standardization of multicentric image datasets with generative adversarial networks, IEEE MIC, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02447807

A. Chatterjee, Creating robust predictive radiomic models for data from independent institutions using normalization, IEEE Trans Radiat Plasma Med Sci. 1-1, 2019.

F. Orlhac, A post-reconstruction harmonization method for multicenter radiomic studies in pet, J Nucl Med, 2018.

W. E. Johnson, C. Li, and A. Rabinovic, Adjusting batch effects in microarray expression data using empirical bayes methods, Biostatistics, vol.8, pp.118-145, 2007.

C. K. Stein, Removing batch effects from purified plasma cell gene expression microarrays with modified combat, BMC Bioinformatics, vol.16, 2015.

C. Chen, Removing batch effects in analysis of expression microarray data: An evaluation of six batch adjustment methods, PLoS ONE, vol.6, p.17238, 2011.

J. Luo, A comparison of batch effect removal methods for enhancement of prediction performance using maqc-ii microarry gene expression data, Pharmacogenomics J, vol.10, pp.278-91, 2010.

P. Kupfer, Batch correction of microarray data substantially improves the identification of genes differentially expressed in rheumatoid arthritis and osteoarthritis, BMC Med Genomics, vol.5, p.23, 2012.

P. A. Konstantinopoulos, Integrated analysis of multiple microarray datasets identifies a reproducible survival predictor in ovarian cancer, PLoS ONE, vol.6, p.18202, 2011.

F. Lucia, Prediction of outcome using pretreatment 18 f-fdg pet/ct and mri radiomics in locally advanced cervical cancer treated with chemoradiotherapy, European journal of nuclear medicine and molecular imaging, vol.45, pp.768-786, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01659241

M. Hatt, C. C. Le-rest, A. Turzo, C. Roux, and D. Visvikis, A fuzzy locally adaptive bayesian segmentation approach for volume determination in pet, IEEE transactions on medical imaging, vol.28, pp.881-893, 2009.
URL : https://hal.archives-ouvertes.fr/inserm-00372910

S. Pieper, M. Halle, R. Kikinis, and . Slicer, in biomedical imaging: Nano to macro, IEEE International Symposium on. IEEE 632-635, 2004.

A. Zwanenburg, Image biomarker standardisation initiative-feature definitions, 2016.

A. Zwanenburg, The image biomarker standardization initiative: standardized quantitative radiomics for high throughput image-based phenotyping, Radiology, vol.295, issue.2, pp.328-338, 2020.

F. Murtagh and P. Contreras, Methods of hierarchical clustering, Cs Math Stat, 2011.

P. J. Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J Comput Appl Math, vol.1, pp.53-65, 1987.

I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Datamining: Practical machine learning tools and techniques, 2016.

V. Fonti and E. Belitser, Feature selection using lasso, 2017.

L. Breiman, P. E. Kinahan, and H. Hricak, random forests, Machine learning, vol.45, pp.5-32, 2001.

V. N. Vapnik, The nature of statistical learning theory, 1995.

T. Hastie, R. Tibshirani, and J. Friedman, Unsupervised learning", in the elements of statistical learning, vol.485, p.585, 2009.

S. Varma and R. Simon, Bias in error estimation when using cross-validation for model selection, BMC bioinformatics, vol.7, p.91, 2006.

T. N. Lal, O. Chapelle, J. Weston, and A. Elisseeff, Embedded methods" in feature extraction: Foundations and applications studies in fuzziness and soft computing, vol.137, 2006.

D. Chicco, Ten quick tips for machine learning in computational biology, BioData mining, vol.10, p.35, 2017.

T. M. Deist, Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers, Med Phys, vol.45, pp.3449-3459, 2018.

R. J. Upadhaya, Comparison of radiomics models built through machine learning in a multicentric context with independent testing: Identical data, similar algorithms, different methodologies, IEEE Trans. Radiat. Plasma Med. Sci, vol.3, pp.192-200, 2019.

C. Muller, Removing batch effects from longitudinal gene expression-quantile normalization plus combat as best approach for microarray transciptome data, Radiology, 2016.

M. Shafiq-ui-hassan, Voxel size and gray level normalization of ct radiomic features in lung cancer, Sci Rep, vol.8, p.545, 2018.

F. Olrhac, Validation of a method to compensate multicenter effects affecting ct radiomics features, 2019.