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For: Crombé A, Kind M, Fadli D, Le Loarer F, Italiano A, Buy X, Saut O. Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients. Sci Rep 2020;10:15496. [PMID: 32968131 DOI: 10.1038/s41598-020-72535-0] [Cited by in Crossref: 15] [Cited by in F6Publishing: 18] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Destito M, Marzullo A, Leone R, Zaffino P, Steffanoni S, Erbella F, Calimeri F, Anzalone N, De Momi E, Ferreri AJM, Calimeri T, Spadea MF. Radiomics-Based Machine Learning Model for Predicting Overall and Progression-Free Survival in Rare Cancer: A Case Study for Primary CNS Lymphoma Patients. Bioengineering 2023;10:285. [DOI: 10.3390/bioengineering10030285] [Reference Citation Analysis]
2 Crombé A, Matcuk GR, Fadli D, Sambri A, Patel DB, Paioli A, Kind M, Spinnato P. Role of Imaging in Initial Prognostication of Locally Advanced Soft Tissue Sarcomas. Acad Radiol 2023;30:322-40. [PMID: 35534392 DOI: 10.1016/j.acra.2022.04.003] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
3 Nie K, Xiao Y. Radiomics in clinical trials: perspectives on standardization. Phys Med Biol 2022;68. [PMID: 36384049 DOI: 10.1088/1361-6560/aca388] [Reference Citation Analysis]
4 Crombé A, Roulleau-Dugage M, Italiano A. The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy. Cancer Commun (Lond) 2022;42:1288-313. [PMID: 36260064 DOI: 10.1002/cac2.12373] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 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]
6 Cui Y, Yin F. Impact of image quality on radiomics applications. Phys Med Biol 2022;67:15TR03. [DOI: 10.1088/1361-6560/ac7fd7] [Reference Citation Analysis]
7 Spinnato P, Kind M, Le Loarer F, Bianchi G, Colangeli M, Sambri A, Ponti F, van Langevelde K, Crombé A. Soft Tissue Sarcomas: The Role of Quantitative MRI in Treatment Response Evaluation. Acad Radiol 2022;29:1065-84. [PMID: 34548230 DOI: 10.1016/j.acra.2021.08.007] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
8 Saltybaeva N, Tanadini-lang S, Vuong D, Burgermeister S, Mayinger M, Bink A, Andratschke N, Guckenberger M, Bogowicz M. Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: multi-center study. Physics and Imaging in Radiation Oncology 2022. [DOI: 10.1016/j.phro.2022.05.006] [Reference Citation Analysis]
9 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: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
10 Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2021. [PMID: 34455593 DOI: 10.1002/mp.15195] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
11 Mali SA, Ibrahim A, Woodruff HC, Andrearczyk V, Müller H, Primakov S, Salahuddin Z, Chatterjee A, Lambin P. Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods. J Pers Med 2021;11:842. [PMID: 34575619 DOI: 10.3390/jpm11090842] [Cited by in Crossref: 24] [Cited by in F6Publishing: 29] [Article Influence: 12.0] [Reference Citation Analysis]
12 Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix AN, Lambin P, Bottari F, Tsoutzidis N, Miraglio B, Walsh S, Vos W, Hustinx R, Ferreira M, Lovinfosse P, Leijenaar RTH. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev 2021. [PMID: 34309893 DOI: 10.1002/med.21846] [Cited by in Crossref: 17] [Cited by in F6Publishing: 21] [Article Influence: 8.5] [Reference Citation Analysis]
13 Corso F, Tini G, Lo Presti G, Garau N, De Angelis SP, Bellerba F, Rinaldi L, Botta F, Rizzo S, Origgi D, Paganelli C, Cremonesi M, Rampinelli C, Bellomi M, Mazzarella L, Pelicci PG, Gandini S, Raimondi S. The Challenge of Choosing the Best Classification Method in Radiomic Analyses: Recommendations and Applications to Lung Cancer CT Images. Cancers (Basel) 2021;13:3088. [PMID: 34205631 DOI: 10.3390/cancers13123088] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
14 Gitto S, Cuocolo R, Albano D, Morelli F, Pescatori LC, Messina C, Imbriaco M, Sconfienza LM. CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies. Insights Imaging 2021;12:68. [PMID: 34076740 DOI: 10.1186/s13244-021-01008-3] [Cited by in Crossref: 14] [Cited by in F6Publishing: 16] [Article Influence: 7.0] [Reference Citation Analysis]
15 Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Iannicelli E, Laghi A. Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI. Cancers (Basel) 2021;13:2522. [PMID: 34063937 DOI: 10.3390/cancers13112522] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 7.0] [Reference Citation Analysis]
16 Shayesteh S, Nazari M, Salahshour A, Sandoughdaran S, Hajianfar G, Khateri M, Yaghobi Joybari A, Jozian F, Fatehi Feyzabad SH, Arabi H, Shiri I, Zaidi H. Treatment response prediction using MRI-based pre-, post-, and delta-radiomic features and machine learning algorithms in colorectal cancer. Med Phys 2021;48:3691-701. [PMID: 33894058 DOI: 10.1002/mp.14896] [Cited by in Crossref: 32] [Cited by in F6Publishing: 32] [Article Influence: 16.0] [Reference Citation Analysis]
17 Coppola F, Giannini V, Gabelloni M, Panic J, Defeudis A, Lo Monaco S, Cattabriga A, Cocozza MA, Pastore LV, Polici M, Caruso D, Laghi A, Regge D, Neri E, Golfieri R, Faggioni L. Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice. Diagnostics (Basel) 2021;11:756. [PMID: 33922483 DOI: 10.3390/diagnostics11050756] [Cited by in Crossref: 21] [Cited by in F6Publishing: 22] [Article Influence: 10.5] [Reference Citation Analysis]
18 D'Amore B, Smolinski-Zhao S, Daye D, Uppot RN. Role of Machine Learning and Artificial Intelligence in Interventional Oncology. Curr Oncol Rep 2021;23:70. [PMID: 33880651 DOI: 10.1007/s11912-021-01054-6] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
19 Crombé A, Buy X, Han F, Toupin S, Kind M. Assessment of Repeatability, Reproducibility, and Performances of T2 Mapping-Based Radiomics Features: A Comparative Study. J Magn Reson Imaging 2021;54:537-48. [PMID: 33594768 DOI: 10.1002/jmri.27558] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]