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Cited by in F6Publishing
For: Bin L, Yuan T, Zhaohui S, Wenting R, Zhiqiang L, Peng H, Shuying Y, Lei D, Jianyang W, Jingbo W, Tao Z, Xiaotong L, Nan B, Jianrong D. A deep learning-based dual-omics prediction model for radiation pneumonitis. Med Phys 2021. [PMID: 34224595 DOI: 10.1002/mp.15079] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
Number Citing Articles
1 Zhang Z, Wei X. Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy. Semin Cancer Biol 2023;90:57-72. [PMID: 36796530 DOI: 10.1016/j.semcancer.2023.02.005] [Reference Citation Analysis]
2 Zhou L, Wen Y, Zhang G, Wang L, Wu S, Zhang S. Machine Learning-Based Multiomics Prediction Model for Radiation Pneumonitis. J Oncol 2023;2023:5328927. [PMID: 36852328 DOI: 10.1155/2023/5328927] [Reference Citation Analysis]
3 Ma Z, Liang B, Wei R, Liu Y, Bao Y, Yuan M, Men Y, Wang J, Deng L, Zhai Y, Bi N, Wang L, Dai J, Hui Z. Developing dosiomics models for the prediction of postoperative radiotherapy-induced esophagitis in patients with non-small cell lung cancer.. [DOI: 10.21203/rs.3.rs-2465686/v1] [Reference Citation Analysis]
4 Xia WL, Liang B, Men K, Zhang K, Tian Y, Li MH, Lu NN, Li YX, Dai JR. Prediction of adaptive strategies based on deformation vector field features for MR-guided adaptive radiotherapy of prostate cancer. Med Phys 2022. [PMID: 36583878 DOI: 10.1002/mp.16192] [Reference Citation Analysis]
5 Feng B, Zhou W, Yang X, Luo H, Zhang X, Yang D, Tao D, Wu Y, Jin F. Pseudo-siamese network combined with dosimetric and clinical factors, radiomics features, CT images and 3D dose distribution for the prediction of radiation pneumonitis: A feasibility study. Clinical and Translational Radiation Oncology 2022. [DOI: 10.1016/j.ctro.2022.11.011] [Reference Citation Analysis]
6 Appelt AL, Elhaminia B, Gooya A, Gilbert A, Nix M. Deep Learning for Radiotherapy Outcome Prediction Using Dose Data - A Review. Clin Oncol (R Coll Radiol) 2021:S0936-6555(21)00462-3. [PMID: 34924256 DOI: 10.1016/j.clon.2021.12.002] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
7 Liu Y, Liu H. Utilization of Nursing Defect Management Evaluation and Deep Learning in Nursing Process Reengineering Optimization. Comput Math Methods Med 2021;2021:8019385. [PMID: 34819992 DOI: 10.1155/2021/8019385] [Reference Citation Analysis]