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For: Crombé A, Saut O, Guigui J, Italiano A, Buy X, Kind M. Influence of temporal parameters of DCE‐MRI on the quantification of heterogeneity in tumor vascularization. J Magn Reson Imaging 2019;50:1773-88. [DOI: 10.1002/jmri.26753] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 2.8] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 Giraudo C, Fichera G, Del Fiore P, Mocellin S, Brunello A, Rastrelli M, Stramare R. Tumor cellularity beyond the visible in soft tissue sarcomas: Results of an ADC-based, single center, and preliminary radiomics study. Front Oncol 2022;12:879553. [DOI: 10.3389/fonc.2022.879553] [Reference Citation Analysis]
3 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]
4 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]
5 Crombé A, Cousin S, Spalato-Ceruso M, Le Loarer F, Toulmonde M, Michot A, Kind M, Stoeckle E, Italiano A. Implementing a Machine Learning Strategy to Predict Pathologic Response in Patients With Soft Tissue Sarcomas Treated With Neoadjuvant Chemotherapy. JCO Clin Cancer Inform 2021;5:958-72. [PMID: 34524884 DOI: 10.1200/CCI.21.00062] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
6 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]
7 Xie Y, Zhao J, Zhang P. A multicompartment model for intratumor tissue-specific analysis of DCE-MRI using non-negative matrix factorization. Med Phys 2021;48:2400-11. [PMID: 33608885 DOI: 10.1002/mp.14793] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
8 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]
9 Kalisvaart GM, Bloem JL, Bovée JVMG, van de Sande MAJ, Gelderblom H, van der Hage JA, Hartgrink HH, Krol ADG, de Geus-Oei LF, Grootjans W. Personalising sarcoma care using quantitative multimodality imaging for response assessment. Clin Radiol 2021;76:313.e1-313.e13. [PMID: 33483087 DOI: 10.1016/j.crad.2020.12.009] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
10 Grünewald TG, Alonso M, Avnet S, Banito A, Burdach S, Cidre-Aranaz F, Di Pompo G, Distel M, Dorado-Garcia H, Garcia-Castro J, González-González L, Grigoriadis AE, Kasan M, Koelsche C, Krumbholz M, Lecanda F, Lemma S, Longo DL, Madrigal-Esquivel C, Morales-Molina Á, Musa J, Ohmura S, Ory B, Pereira-Silva M, Perut F, Rodriguez R, Seeling C, Al Shaaili N, Shaabani S, Shiavone K, Sinha S, Tomazou EM, Trautmann M, Vela M, Versleijen-Jonkers YM, Visgauss J, Zalacain M, Schober SJ, Lissat A, English WR, Baldini N, Heymann D. Sarcoma treatment in the era of molecular medicine. EMBO Mol Med 2020;12:e11131. [PMID: 33047515 DOI: 10.15252/emmm.201911131] [Cited by in Crossref: 62] [Cited by in F6Publishing: 66] [Article Influence: 20.7] [Reference Citation Analysis]
11 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]
12 Crombé A, Fadli D, Italiano A, Saut O, Buy X, Kind M. Systematic review of sarcomas radiomics studies: Bridging the gap between concepts and clinical applications? Eur J Radiol 2020;132:109283. [PMID: 32980727 DOI: 10.1016/j.ejrad.2020.109283] [Cited by in Crossref: 24] [Cited by in F6Publishing: 20] [Article Influence: 8.0] [Reference Citation Analysis]
13 Crombé A, Fadli D, Buy X, Italiano A, Saut O, Kind M. High-Grade Soft-Tissue Sarcomas: Can Optimizing Dynamic Contrast-Enhanced MRI Postprocessing Improve Prognostic Radiomics Models? J Magn Reson Imaging 2020;52:282-97. [PMID: 31922323 DOI: 10.1002/jmri.27040] [Cited by in Crossref: 14] [Cited by in F6Publishing: 16] [Article Influence: 4.7] [Reference Citation Analysis]