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For: Li Y, Ammari S, Balleyguier C, Lassau N, Chouzenoux E. Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features. Cancers (Basel) 2021;13:3000. [PMID: 34203896 DOI: 10.3390/cancers13123000] [Cited by in Crossref: 9] [Cited by in F6Publishing: 11] [Article Influence: 9.0] [Reference Citation Analysis]
Number Citing Articles
1 Soliman MA, Kelahan LC, Magnetta M, Savas H, Agrawal R, Avery RJ, Aouad P, Liu B, Xue Y, Chae YK, Salem R, Benson AB, Yaghmai V, Velichko YS. A Framework for Harmonization of Radiomics Data for Multicenter Studies and Clinical Trials. JCO Clinical Cancer Informatics 2022. [DOI: 10.1200/cci.22.00023] [Reference Citation Analysis]
2 Tafuri B, Lombardi A, Nigro S, Urso D, Monaco A, Pantaleo E, Diacono D, De Blasi R, Bellotti R, Tangaro S, Logroscino G. The impact of harmonization on radiomic features in Parkinson’s disease and healthy controls: A multicenter study. Front Neurosci 2022;16:1012287. [DOI: 10.3389/fnins.2022.1012287] [Reference Citation Analysis]
3 Ibrahim A, Lu L, Yang H, Akin O, Schwartz LH, Zhao B. The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model. Applied Sciences 2022;12:9824. [DOI: 10.3390/app12199824] [Reference Citation Analysis]
4 Teng X, Zhang J, Zwanenburg A, Sun J, Huang Y, Lam S, Zhang Y, Li B, Zhou T, Xiao H, Liu C, Li W, Han X, Ma Z, Li T, Cai J. Building reliable radiomic models using image perturbation. Sci Rep 2022;12:10035. [PMID: 35710850 DOI: 10.1038/s41598-022-14178-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
5 Scalco E, Rizzo G, Mastropietro A. The stability of oncologic MRI radiomic features and the potential role of deep learning: a review. Phys Med Biol 2022;67:09TR03. [DOI: 10.1088/1361-6560/ac60b9] [Reference Citation Analysis]
6 Fatania K, Mohamud F, Clark A, Nix M, Short SC, O'Connor J, Scarsbrook AF, Currie S. Intensity standardization of MRI prior to radiomic feature extraction for artificial intelligence research in glioma-a systematic review. Eur Radiol 2022. [PMID: 35486171 DOI: 10.1007/s00330-022-08807-2] [Reference Citation Analysis]
7 Refaee T, Salahuddin Z, Widaatalla Y, Primakov S, Woodruff HC, Hustinx R, Mottaghy FM, Ibrahim A, Lambin P. CT Reconstruction Kernels and the Effect of Pre- and Post-Processing on the Reproducibility of Handcrafted Radiomic Features. JPM 2022;12:553. [DOI: 10.3390/jpm12040553] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Li Y, Ammari S, Lawrance L, Quillent A, Assi T, Lassau N, Chouzenoux E. Radiomics-Based Method for Predicting the Glioma Subtype as Defined by Tumor Grade, IDH Mutation, and 1p/19q Codeletion. Cancers 2022;14:1778. [DOI: 10.3390/cancers14071778] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Huang G, Cui Y, Wang P, Ren J, Wang L, Ma Y, Jia Y, Ma X, Zhao L. Multi-Parametric Magnetic Resonance Imaging-Based Radiomics Analysis of Cervical Cancer for Preoperative Prediction of Lymphovascular Space Invasion. Front Oncol 2021;11:663370. [PMID: 35096556 DOI: 10.3389/fonc.2021.663370] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
10 Acquitter C, Piram L, Sabatini U, Gilhodes J, Moyal Cohen-Jonathan E, Ken S, Lemasson B. Radiomics-Based Detection of Radionecrosis Using Harmonized Multiparametric MRI. Cancers (Basel) 2022;14:286. [PMID: 35053450 DOI: 10.3390/cancers14020286] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
11 Stamoulou E, Manikis GC, Tsiknakis M, Marias K. ComBat harmonization for multicenter MRI based radiomics features. 2021 IEEE International Conference on Imaging Systems and Techniques (IST) 2021. [DOI: 10.1109/ist50367.2021.9745836] [Reference Citation Analysis]