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Cited by in F6Publishing
For: Choi JM, Yu JS, Cho ES, Kim JH, Chung JJ. Texture Analysis of Hepatocellular Carcinoma on Magnetic Resonance Imaging: Assessment for Performance in Predicting Histopathologic Grade. J Comput Assist Tomogr 2020;44:901-10. [PMID: 32976263 DOI: 10.1097/RCT.0000000000001087] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
Number Citing Articles
1 Han YE, Cho Y, Kim MJ, Park BJ, Sung DJ, Han NY, Sim KC, Park YS, Park BN. Hepatocellular carcinoma pathologic grade prediction using radiomics and machine learning models of gadoxetic acid-enhanced MRI: a two-center study. Abdom Radiol (NY) 2022. [PMID: 36131163 DOI: 10.1007/s00261-022-03679-y] [Reference Citation Analysis]
2 Brancato V, Garbino N, Salvatore M, Cavaliere C. MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma. Diagnostics 2022;12:1085. [DOI: 10.3390/diagnostics12051085] [Reference Citation Analysis]
3 Gong XQ, Tao YY, Wu YK, Liu N, Yu X, Wang R, Zheng J, Liu N, Huang XH, Li JD, Yang G, Wei XQ, Yang L, Zhang XM. Progress of MRI Radiomics in Hepatocellular Carcinoma. Front Oncol 2021;11:698373. [PMID: 34616673 DOI: 10.3389/fonc.2021.698373] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]