Radiomics: Images are more than pictures, they are data, Radiology, vol.2, pp.563-77, 2016. ,
Radiomics: Extracting more information from medical imagesusing advanced feature analysis, Eur. J. Cancer, vol.48, pp.441-446, 2012. ,
Radiomics: The process and the challenges, Magn. Respn. Imag, vol.30, pp.1234-1248, 2012. ,
Quantitativeradiomics studies for tissue characterization: A review of technology and methodological procedures, Brit. J. Radiol, p.90, 2017. ,
Radiomics: extracting more information from medical images using advanced feature analysis, European journal of cancer, vol.48, pp.441-446, 2012. ,
Radiomics: the bridge between medical imaging and personalized medicine, Nature Reviews Clinical Oncology, vol.14, p.749, 2017. ,
Radiomic analysis for pretreatment predictionof response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study, EBioMedicine, vol.46, pp.160-169, 2019. ,
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. ,
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. ,
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. ,
Why validation of prognostic models matters?, Radiother Oncol, vol.127, pp.370-373, 2018. ,
Multicentric validation of radiomics findings:challenges and opportunities, EBioMedicine, vol.47, pp.20-21, 2019. ,
Variability of textural featuresin fdg pet images due to different acquisition modes and reconstruction parameters, Acta Oncol, vol.49, pp.1012-1016, 2010. ,
Impact of image reconstruction settings on texture features in 18f-fdg pet, J Nucl Med, vol.56, pp.1667-1673, 2015. ,
Stability ofradiomics features in apparent diffusion coefficient maps from a multi-centre test-retest trial, Sci Rep, vol.9, 2019. ,
Intrinsic dependencies of ct radiomic features on voxel size and number of gray levels, Med Phys, vol.44, pp.1050-1062, 2007. ,
Radiomics features harmonization for ct and cbct in rectal cancer, Radiotherapy and Oncology, vol.123, issue.17, pp.30603-30608, 2017. ,
Fdg pet/ct: Eanm procedureguidelines for tumour imaging: version 2.0, Eur J Nucl Med Mol Imaging, vol.42, pp.328-354, 2015. ,
Feasibility of state of the art pet/ct systems for performance harmonization, Eur J Nucl Med Mol Imaging, vol.45, pp.1344-1361, 2018. ,
Deep learning-based image conversion of ct reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses, Radiology, vol.292, pp.365-373, 2019. ,
Standardization of multicentric image datasets with generative adversarial networks, IEEE MIC, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02447807
Creating robust predictive radiomic models for data from independent institutions using normalization, IEEE Trans Radiat Plasma Med Sci. 1-1, 2019. ,
A post-reconstruction harmonization method for multicenter radiomic studies in pet, J Nucl Med, 2018. ,
Adjusting batch effects in microarray expression data using empirical bayes methods, Biostatistics, vol.8, pp.118-145, 2007. ,
Removing batch effects from purified plasma cell gene expression microarrays with modified combat, BMC Bioinformatics, vol.16, 2015. ,
Removing batch effects in analysis of expression microarray data: An evaluation of six batch adjustment methods, PLoS ONE, vol.6, p.17238, 2011. ,
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. ,
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. ,
Integrated analysis of multiple microarray datasets identifies a reproducible survival predictor in ovarian cancer, PLoS ONE, vol.6, p.18202, 2011. ,
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
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
in biomedical imaging: Nano to macro, IEEE International Symposium on. IEEE 632-635, 2004. ,
, Image biomarker standardisation initiative-feature definitions, 2016.
The image biomarker standardization initiative: standardized quantitative radiomics for high throughput image-based phenotyping, Radiology, vol.295, issue.2, pp.328-338, 2020. ,
Methods of hierarchical clustering, Cs Math Stat, 2011. ,
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J Comput Appl Math, vol.1, pp.53-65, 1987. ,
Datamining: Practical machine learning tools and techniques, 2016. ,
Feature selection using lasso, 2017. ,
random forests, Machine learning, vol.45, pp.5-32, 2001. ,
The nature of statistical learning theory, 1995. ,
Unsupervised learning", in the elements of statistical learning, vol.485, p.585, 2009. ,
Bias in error estimation when using cross-validation for model selection, BMC bioinformatics, vol.7, p.91, 2006. ,
Embedded methods" in feature extraction: Foundations and applications studies in fuzziness and soft computing, vol.137, 2006. ,
Ten quick tips for machine learning in computational biology, BioData mining, vol.10, p.35, 2017. ,
Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers, Med Phys, vol.45, pp.3449-3459, 2018. ,
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. ,
Removing batch effects from longitudinal gene expression-quantile normalization plus combat as best approach for microarray transciptome data, Radiology, 2016. ,
Voxel size and gray level normalization of ct radiomic features in lung cancer, Sci Rep, vol.8, p.545, 2018. ,
Validation of a method to compensate multicenter effects affecting ct radiomics features, 2019. ,