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For: 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]
Number Citing Articles
1 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]
2 Crombé A, Bertolo F, Fadli D, Kind M, Le Loarer F, Perret R, Chaire V, Spinnato P, Lucchesi C, Italiano A. Distinct patterns of the natural evolution of soft tissue sarcomas on pre-treatment MRIs captured with delta-radiomics correlate with gene expression profiles. Eur Radiol 2023;33:1205-18. [PMID: 36029343 DOI: 10.1007/s00330-022-09104-8] [Reference Citation Analysis]
3 Erber BM, Reidler P, Goller SS, Ricke J, Dürr HR, Klein A, Lindner L, Di Gioia D, Geith T, Baur-Melnyk A, Armbruster M. Impact of Dynamic Contrast Enhanced and Diffusion-Weighted MR Imaging on Detection of Early Local Recurrence of Soft Tissue Sarcoma. J Magn Reson Imaging 2023;57:622-30. [PMID: 35582900 DOI: 10.1002/jmri.28236] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [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 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]
6 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]
7 Yue Z, Wang X, Yu T, Shang S, Liu G, Jing W, Yang H, Luo Y, Jiang X. Multi-parametric MRI-based radiomics for the diagnosis of malignant soft-tissue tumor. Magn Reson Imaging 2022:S0730-725X(22)00062-5. [PMID: 35525523 DOI: 10.1016/j.mri.2022.05.003] [Reference Citation Analysis]
8 Giraudo C, Fichera G, Stramare R, Bisogno G, Motta R, Evangelista L, Cecchin D, Zucchetta P. Radiomic features as biomarkers of soft tissue paediatric sarcomas: preliminary results of a PET/MR study. Radiol Oncol 2022;56:138-41. [PMID: 35344641 DOI: 10.2478/raon-2022-0013] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Peeken JC, Asadpour R, Specht K, Chen EY, Klymenko O, Akinkuoroye V, Hippe DS, Spraker MB, Schaub SK, Dapper H, Knebel C, Mayr NA, Gersing AS, Woodruff HC, Lambin P, Nyflot MJ, Combs SE. MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy. Radiother Oncol 2021;164:73-82. [PMID: 34506832 DOI: 10.1016/j.radonc.2021.08.023] [Cited by in Crossref: 13] [Cited by in F6Publishing: 12] [Article Influence: 6.5] [Reference Citation Analysis]
10 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]
11 Dionisio FCF, Oliveira LS, Hernandes MA, Engel EE, de Azevedo-Marques PM, Nogueira-Barbosa MH. Manual versus semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging: evaluation of similarity and comparison of segmentation times. Radiol Bras 2021;54:155-64. [PMID: 34108762 DOI: 10.1590/0100-3984.2020.0028] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
12 Navarro F, Dapper H, Asadpour R, Knebel C, Spraker MB, Schwarze V, Schaub SK, Mayr NA, Specht K, Woodruff HC, Lambin P, Gersing AS, Nyflot MJ, Menze BH, Combs SE, Peeken JC. Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging. Cancers (Basel) 2021;13:2866. [PMID: 34201251 DOI: 10.3390/cancers13122866] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
13 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]
14 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]
15 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]
16 Razek AAKA. Editorial for “Preoperative MRI ‐Based Radiomic Machine‐Learning Nomogram May Accurately Distinguish Between Benign and Malignant Soft Tissue Lesions: A Two‐Center Study”. J Magn Reson Imaging 2020;52:883-4. [DOI: 10.1002/jmri.27163] [Cited by in Crossref: 19] [Cited by in F6Publishing: 19] [Article Influence: 6.3] [Reference Citation Analysis]