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For:	Zhang F, Zhong LZ, Zhao X, Dong D, Yao JJ, Wang SY, Liu Y, Zhu D, Wang Y, Wang GJ, Wang YM, Li D, Wei J, Tian J, Shan H. A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study. Ther Adv Med Oncol 2020;12:1758835920971416. [PMID: 33403013 DOI: 10.1177/1758835920971416] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]

For:

Zhang F, Zhong LZ, Zhao X, Dong D, Yao JJ, Wang SY, Liu Y, Zhu D, Wang Y, Wang GJ, Wang YM, Li D, Wei J, Tian J, Shan H. A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study. Ther Adv Med Oncol 2020;12:1758835920971416. [PMID: 33403013 DOI: 10.1177/1758835920971416] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]

Number	Citing Articles
1	Ng WT, But B, Choi HC, de Bree R, Lee AW, Lee VH, López F, Mäkitie AA, Rodrigo JP, Saba NF, Tsang RK, Ferlito A. Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review. CMAR 2022;Volume 14:339-66. [DOI: 10.2147/cmar.s341583] [Reference Citation Analysis]
2	Zhao X, Liang YJ, Zhang X, Wen DX, Fan W, Tang LQ, Dong D, Tian J, Mai HQ. Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma. Eur J Nucl Med Mol Imaging 2022. [PMID: 35471254 DOI: 10.1007/s00259-022-05793-x] [Reference Citation Analysis]
3	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 F6Publishing: 1] [Reference Citation Analysis]
4	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] [Reference Citation Analysis]
5	Yan Y, Liu Y, Tao J, Li Z, Qu X, Guo J, Xian J. Preoperative Prediction of Malignant Transformation of Sinonasal Inverted Papilloma Using MR Radiomics. Front Oncol 2022;12:870544. [DOI: 10.3389/fonc.2022.870544] [Reference Citation Analysis]