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Cited by in F6Publishing
For: Nam JY, Lee JH, Bae J, Chang Y, Cho Y, Sinn DH, Kim BH, Kim SH, Yi NJ, Lee KW, Kim JM, Park JW, Kim YJ, Yoon JH, Joh JW, Suh KS. Novel Model to Predict HCC Recurrence after Liver Transplantation Obtained Using Deep Learning: A Multicenter Study. Cancers (Basel) 2020;12:E2791. [PMID: 33003306 DOI: 10.3390/cancers12102791] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Zou ZM, Chang DH, Liu H, Xiao YD. Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know? Insights Imaging 2021;12:31. [PMID: 33675433 DOI: 10.1186/s13244-021-00977-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
2 Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14(4): 765-793 [DOI: 10.4251/wjgo.v14.i4.765] [Reference Citation Analysis]
3 Moldogazieva NT, Mokhosoev IM, Zavadskiy SP, Terentiev AA. Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine. Biomedicines 2021;9:159. [PMID: 33562077 DOI: 10.3390/biomedicines9020159] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
4 Ahn JC, Qureshi TA, Singal AG, Li D, Yang JD. Deep learning in hepatocellular carcinoma: Current status and future perspectives. World J Hepatol 2021; 13(12): 2039-2051 [DOI: 10.4254/wjh.v13.i12.2039] [Reference Citation Analysis]