Li, Z.; Li, Z.; Lam, S.K.; Wang, X.; Wang, P.; Song, L.; Lee, F.K.-H.; Yip, C.W.-Y.; Cai, J.; Li, T.
A Multi-Modal Deep Learning Approach for Predicting Eligibility for Adaptive Radiation Therapy in Nasopharyngeal Carcinoma Patients. Cancers 2025, 17, 2350.
https://doi.org/10.3390/cancers17142350
AMA Style
Li Z, Li Z, Lam SK, Wang X, Wang P, Song L, Lee FK-H, Yip CW-Y, Cai J, Li T.
A Multi-Modal Deep Learning Approach for Predicting Eligibility for Adaptive Radiation Therapy in Nasopharyngeal Carcinoma Patients. Cancers. 2025; 17(14):2350.
https://doi.org/10.3390/cancers17142350
Chicago/Turabian Style
Li, Zhichun, Zihan Li, Sai Kit Lam, Xiang Wang, Peilin Wang, Liming Song, Francis Kar-Ho Lee, Celia Wai-Yi Yip, Jing Cai, and Tian Li.
2025. "A Multi-Modal Deep Learning Approach for Predicting Eligibility for Adaptive Radiation Therapy in Nasopharyngeal Carcinoma Patients" Cancers 17, no. 14: 2350.
https://doi.org/10.3390/cancers17142350
APA Style
Li, Z., Li, Z., Lam, S. K., Wang, X., Wang, P., Song, L., Lee, F. K.-H., Yip, C. W.-Y., Cai, J., & Li, T.
(2025). A Multi-Modal Deep Learning Approach for Predicting Eligibility for Adaptive Radiation Therapy in Nasopharyngeal Carcinoma Patients. Cancers, 17(14), 2350.
https://doi.org/10.3390/cancers17142350
