Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma-Reference-Cited by-同舟云学术

Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma

Published:2022-04-26 Issue:8 Volume:49 Page:2972-2982
ISSN:1619-7070
Container-title:European Journal of Nuclear Medicine and Molecular Imaging
language:en
Short-container-title:Eur J Nucl Med Mol Imaging

Author:

Zhao Xun,Liang Yu-Jing,Zhang Xu,Wen Dong-Xiang,Fan Wei,Tang Lin-Quan,Dong Di,Tian Jie^ORCID,Mai Hai-Qiang

Funder

National Key R&D Program of China

National Natural Science Foundation of China

the Beijing Natural Science Foundation

Strategic Priority Research Program of Chinese Academy of Sciences

the Youth Innovation Promotion Association CAS

the Sci-Tech Project Foundation of Guangzhou City

the Sun Yat-sen University Clinical Research 5010 Program

Innovative research team of high-level local universities in Shanghai

the Natural Science Foundation of Guangdong Province

the Natural Science Foundation of Guangdong Province for Distinguished Young Scholar

the Health & Medical Collaborative Innovation Project of Guangzhou City

Pearl River S&T Nova Program of Guangzhou

the Planned Science and Technology Project of Guangdong Province

Natural Science Foundation of Guangdong Province

the Key Youth Teacher Cultivating Program of Sun Yat-sen University

the Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine,Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00259-022-05793-x.pdf

Reference30 articles.

1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.

2. Lee AWM, Ma BBY, Ng WT, Chan ATC. Management of nasopharyngeal carcinoma: current practice and future perspective. J Clin Oncol. 2015;33:3356–64.

3. Ng WT, Soong YL, Ahn YC, AlHussain H, Choi HCW, Corry J, et al. International recommendations on reirradiation by intensity modulated radiation therapy for locally recurrent nasopharyngeal carcinoma. Int J Radiat Oncol Biol Phys. 2021;110:682–95.

4. Liu L-T, Chen Q-Y, Tang L-Q, Zhang L, Guo S-S, Guo L, et al. With or without reirradiation in advanced local recurrent nasopharyngeal carcinoma: a case-control study. BMC Cancer. 2016;16:774.

5. Sun X-S, Liang Y-J, Jia G-D, Liu S-L, Liu L-T, Guo S-S, et al. Establishment of a prognostic nomogram to identify optimal candidates for local treatment among patients with local recurrent nasopharyngeal carcinoma. Oral Oncol. 2020;106:104711.

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