Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies

[1]  R. Steenbakkers,et al.  The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. , 2020, Radiology.

[2]  Dimitris Visvikis,et al.  Pretreatment 18F-FDG PET/CT Radiomics Predict Local Recurrence in Patients Treated with Stereotactic Body Radiotherapy for Early-Stage Non–Small Cell Lung Cancer: A Multicentric Study , 2019, The Journal of Nuclear Medicine.

[3]  Dimitris Visvikis,et al.  Standardization of Multicentric Image Datasets with Generative Adversarial Networks , 2019 .

[4]  Dimitris Visvikis,et al.  Multicentric validation of radiomics findings: challenges and opportunities , 2019, EBioMedicine.

[5]  Ping Liu,et al.  Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study , 2019, EBioMedicine.

[6]  Sang Min Lee,et al.  Deep Learning-based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses. , 2019, Radiology.

[7]  P. Lambin,et al.  Stability of radiomics features in apparent diffusion coefficient maps from a multi-centre test-retest trial , 2019, Scientific Reports.

[8]  Caroline Reinhold,et al.  Comparison of Radiomics Models Built Through Machine Learning in a Multicentric Context With Independent Testing: Identical Data, Similar Algorithms, Different Methodologies , 2019, IEEE Transactions on Radiation and Plasma Medical Sciences.

[9]  Caroline Reinhold,et al.  Creating Robust Predictive Radiomic Models for Data From Independent Institutions Using Normalization , 2019, IEEE Transactions on Radiation and Plasma Medical Sciences.

[10]  Nicholas Ayache,et al.  Validation of a method to compensate multicenter effects affecting CT radiomic features , 2019 .

[11]  M. Hatt,et al.  External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy , 2018, European Journal of Nuclear Medicine and Molecular Imaging.

[12]  M. Soussan,et al.  A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET , 2018, The Journal of Nuclear Medicine.

[13]  Geoffrey G. Zhang,et al.  Voxel size and gray level normalization of CT radiomic features in lung cancer , 2018, Scientific Reports.

[14]  Timothy Solberg,et al.  Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers , 2018, Medical physics.

[15]  Steffen Löck,et al.  Why validation of prognostic models matters? , 2018, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[16]  Martin A Lodge,et al.  Feasibility of state of the art PET/CT systems performance harmonisation , 2018, European Journal of Nuclear Medicine and Molecular Imaging.

[17]  Dimitris Visvikis,et al.  Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy , 2018, European Journal of Nuclear Medicine and Molecular Imaging.

[18]  Chaofeng Liang,et al.  Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study , 2018, European Radiology.

[19]  A. Patzak,et al.  Caveolin 1 Promotes Renal Water and Salt Reabsorption , 2018, Scientific Reports.

[20]  Davide Chicco,et al.  Ten quick tips for machine learning in computational biology , 2017, BioData Mining.

[21]  P. Lambin,et al.  Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.

[22]  Jiazhou Wang,et al.  OC-0160: Radiomics Features Harmonization for CT and CBCT in Rectal Cancer , 2017 .

[23]  Geoffrey G. Zhang,et al.  Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels , 2017, Medical physics.

[24]  Philippe Lambin,et al.  Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures , 2017, The British journal of radiology.

[25]  Terrence J. Sejnowski,et al.  Unsupervised Learning , 2018, Encyclopedia of GIS.

[26]  Steffen Löck,et al.  Image biomarker standardisation initiative - feature definitions , 2016, ArXiv.

[27]  Harald Binder,et al.  Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data , 2016, PloS one.

[28]  Paul Kinahan,et al.  Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.

[29]  D. Townsend,et al.  Impact of Image Reconstruction Settings on Texture Features in 18F-FDG PET , 2015, The Journal of Nuclear Medicine.

[30]  B. Barlogie,et al.  Removing batch effects from purified plasma cell gene expression microarrays with modified ComBat , 2015, BMC Bioinformatics.

[31]  Eric J. W. Visser,et al.  FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0 , 2014, European Journal of Nuclear Medicine and Molecular Imaging.

[32]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[33]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[34]  R. Guthke,et al.  Batch correction of microarray data substantially improves the identification of genes differentially expressed in Rheumatoid Arthritis and Osteoarthritis , 2012, BMC Medical Genomics.

[35]  Fionn Murtagh,et al.  Methods of Hierarchical Clustering , 2011, ArXiv.

[36]  John Quackenbush,et al.  Integrated Analysis of Multiple Microarray Datasets Identifies a Reproducible Survival Predictor in Ovarian Cancer , 2011, PloS one.

[37]  Chunyu Liu,et al.  Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods , 2011, PloS one.

[38]  R. Jeraj,et al.  Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters , 2010, Acta oncologica.

[39]  Tieliu Shi,et al.  A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data , 2010, The Pharmacogenomics Journal.

[40]  Naoki Ishiguro,et al.  A MULTICENTRE STUDY , 2010 .

[41]  Klaus Nordhausen,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman , 2009 .

[42]  Christian Roux,et al.  A Fuzzy Locally Adaptive Bayesian Segmentation Approach for Volume Determination in PET , 2009, IEEE Transactions on Medical Imaging.

[43]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[44]  Jason Weston,et al.  Embedded Methods , 2006, Feature Extraction.

[45]  Richard Simon,et al.  Bias in error estimation when using cross-validation for model selection , 2006, BMC Bioinformatics.

[46]  Ron Kikinis,et al.  3D Slicer , 2012, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[47]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[48]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[49]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[50]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .