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For: Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel) 2021;11:1523. [PMID: 34573865 DOI: 10.3390/diagnostics11091523] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Kulanthaivelu R, Kohan A, Hinzpeter R, Liu ZA, Hope A, Huang SH, Waldron J, O’sullivan B, Ortega C, Metser U, Veit-haibach P. Prognostic value of PET/CT and MR-based baseline radiomics among patients with non-metastatic nasopharyngeal carcinoma. Front Oncol 2022;12. [DOI: 10.3389/fonc.2022.952763] [Reference Citation Analysis]
2 Hu Q, Wang G, Song X, Wan J, Li M, Zhang F, Chen Q, Cao X, Li S, Wang Y. Machine Learning Based on MRI DWI Radiomics Features for Prognostic Prediction in Nasopharyngeal Carcinoma. Cancers 2022;14:3201. [DOI: 10.3390/cancers14133201] [Reference Citation Analysis]
3 Bao D, Liu Z, Geng Y, Li L, Xu H, Zhang Y, Hu L, Zhao X, Zhao Y, Luo D. Baseline MRI-based radiomics model assisted predicting disease progression in nasopharyngeal carcinoma patients with complete response after treatment. Cancer Imaging 2022;22. [DOI: 10.1186/s40644-022-00448-4] [Reference Citation Analysis]