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Cited by in F6Publishing
For: Lu HC, Wang F, Yin JD. Texture Analysis Based on Sagittal Fat-Suppression and Transverse T2-Weighted Magnetic Resonance Imaging for Determining Local Invasion of Rectal Cancer.Front Oncol. 2020;10:1476. [PMID: 33014786 DOI: 10.3389/fonc.2020.01476] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Li M, Xu X, Qian P, Jiang H, Jiang J, Sun J, Lu Z. Texture Analysis in the Assessment of Rectal Cancer: Comparison of T2WI and Diffusion-Weighted Imaging. Comput Math Methods Med 2021;2021:9976440. [PMID: 34567237 DOI: 10.1155/2021/9976440] [Reference Citation Analysis]
2 Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, Maurea S. Radiomics and machine learning applications in rectal cancer: Current update and future perspectives. World J Gastroenterol 2021; 27(32): 5306-5321 [PMID: 34539134 DOI: 10.3748/wjg.v27.i32.5306] [Cited by in CrossRef: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
3 Coppola F, Giannini V, Gabelloni M, Panic J, Defeudis A, Lo Monaco S, Cattabriga A, Cocozza MA, Pastore LV, Polici M, Caruso D, Laghi A, Regge D, Neri E, Golfieri R, Faggioni L. Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice. Diagnostics (Basel) 2021;11:756. [PMID: 33922483 DOI: 10.3390/diagnostics11050756] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Su R, Wu S, Shen H, Chen Y, Zhu J, Zhang Y, Jia H, Li M, Chen W, He Y, Gao F. Combining Clinicopathology, IVIM-DWI and Texture Parameters for a Nomogram to Predict Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients. Front Oncol 2022;12:886101. [PMID: 35712519 DOI: 10.3389/fonc.2022.886101] [Reference Citation Analysis]