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For: Li Y, Yan C, Weng S, Shi Z, Sun H, Chen J, Xu X, Ye R, Hong J. Texture analysis of multi-phase MRI images to detect expression of Ki67 in hepatocellular carcinoma. Clin Radiol 2019;74:813.e19-27. [PMID: 31362887 DOI: 10.1016/j.crad.2019.06.024] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 3.8] [Reference Citation Analysis]
Number Citing Articles
1 Wang X, Dai Y, Lin H, Cheng J, Zhang Y, Cao M, Zhou Y. Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas. Eur Radiol 2023. [PMID: 36738323 DOI: 10.1007/s00330-023-09412-7] [Reference Citation Analysis]
2 Miranda J, Horvat N, Fonseca GM, Araujo-Filho JAB, Fernandes MC, Charbel C, Chakraborty J, Coelho FF, Nomura CH, Herman P. Current status and future perspectives of radiomics in hepatocellular carcinoma. World J Gastroenterol 2023; 29(1): 43-60 [PMID: 36683711 DOI: 10.3748/wjg.v29.i1.43] [Reference Citation Analysis]
3 Zhang L, Duan S, Qi Q, Li Q, Ren S, Liu S, Mao B, Zhang Y, Wang S, Yang L, Liu R, Liu L, Li Y, Li N, Zhang L. Noninvasive Prediction of Ki‐67 Expression in Hepatocellular Carcinoma Using Machine Learning‐Based Ultrasomics. J of Ultrasound Medicine 2022. [DOI: 10.1002/jum.16126] [Reference Citation Analysis]
4 Li YM, Zhu YM, Gao LM, Han ZW, Chen XJ, Yan C, Ye RP, Cao DR. Radiomic analysis based on multi-phase magnetic resonance imaging to predict preoperatively microvascular invasion in hepatocellular carcinoma. World J Gastroenterol 2022; 28(24): 2733-2747 [DOI: 10.3748/wjg.v28.i24.2733] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Hu X, Zhou J, Li Y, Wang Y, Guo J, Sack I, Chen W, Yan F, Li R, Wang C. Added Value of Viscoelasticity for MRI-Based Prediction of Ki-67 Expression of Hepatocellular Carcinoma Using a Deep Learning Combined Radiomics (DLCR) Model. Cancers (Basel) 2022;14:2575. [PMID: 35681558 DOI: 10.3390/cancers14112575] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Beleù A, Autelitano D, Geraci L, Aluffi G, Cardobi N, De Robertis R, Martone E, Conci S, Ruzzenente A, D'onofrio M. Radiofrequency ablation of hepatocellular carcinoma: CT texture analysis of the ablated area to predict local recurrence. European Journal of Radiology 2022. [DOI: 10.1016/j.ejrad.2022.110250] [Reference Citation Analysis]
7 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: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
8 Harding-Theobald E, Louissaint J, Maraj B, Cuaresma E, Townsend W, Mendiratta-Lala M, Singal AG, Su GL, Lok AS, Parikh ND. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma. Aliment Pharmacol Ther 2021;54:890-901. [PMID: 34390014 DOI: 10.1111/apt.16563] [Cited by in Crossref: 19] [Cited by in F6Publishing: 16] [Article Influence: 9.5] [Reference Citation Analysis]
9 Zhu Y, Weng S, Li Y, Yan C, Ye R, Wen L, Zhou L, Gao L. A radiomics nomogram based on contrast-enhanced MRI for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma. Abdom Radiol (NY) 2021;46:3139-48. [PMID: 33641018 DOI: 10.1007/s00261-021-02989-x] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
10 Fan Y, Yu Y, Wang X, Hu M, Hu C. Radiomic analysis of Gd-EOB-DTPA-enhanced MRI predicts Ki-67 expression in hepatocellular carcinoma. BMC Med Imaging 2021;21:100. [PMID: 34130644 DOI: 10.1186/s12880-021-00633-0] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
11 Cannella R, Sartoris R, Grégory J, Garzelli L, Vilgrain V, Ronot M, Dioguardi Burgio M. Quantitative magnetic resonance imaging for focal liver lesions: bridging the gap between research and clinical practice. Br J Radiol 2021;94:20210220. [PMID: 33989042 DOI: 10.1259/bjr.20210220] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
12 Zhao YF, Xiong X, Chen K, Tang W, Yang X, Shi ZR. Evaluation of the Therapeutic Effect of Adjuvant Transcatheter Arterial Chemoembolization Based on Ki67 After Hepatocellular Carcinoma Surgery. Front Oncol 2021;11:605234. [PMID: 33718156 DOI: 10.3389/fonc.2021.605234] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
13 Lewis S, Hectors S, Taouli B. Radiomics of hepatocellular carcinoma. Abdom Radiol (NY) 2021;46:111-23. [PMID: 31925492 DOI: 10.1007/s00261-019-02378-5] [Cited by in Crossref: 25] [Cited by in F6Publishing: 22] [Article Influence: 12.5] [Reference Citation Analysis]
14 Hu MJ, Yu YX, Fan YF, Hu CH. CT-based radiomics model to distinguish necrotic hepatocellular carcinoma from pyogenic liver abscess. Clin Radiol 2021;76:161.e11-7. [PMID: 33267948 DOI: 10.1016/j.crad.2020.11.002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.3] [Reference Citation Analysis]
15 Geng Z, Zhang Y, Wang S, Li H, Zhang C, Yin S, Xie C, Dai Y. Radiomics Analysis of Susceptibility Weighted Imaging for Hepatocellular Carcinoma: Exploring the Correlation between Histopathology and Radiomics Features. Magn Reson Med Sci 2021;20:253-63. [PMID: 32788505 DOI: 10.2463/mrms.mp.2020-0060] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
16 Weng S, Xu X, Li Y, Yan C, Chen J, Ye R, Zhu Y, Wen L, Hong J. Quantitative analysis of multiphase magnetic resonance images may assist prediction of histopathological grade of small hepatocellular carcinoma. Ann Transl Med 2020;8:1023. [PMID: 32953823 DOI: 10.21037/atm-20-2874] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
17 Shi G, Han X, Wang Q, Ding Y, Liu H, Zhang Y, Dai Y. Evaluation of Multiple Prognostic Factors of Hepatocellular Carcinoma with Intra-Voxel Incoherent Motions Imaging by Extracting the Histogram Metrics. Cancer Manag Res 2020;12:6019-31. [PMID: 32765101 DOI: 10.2147/CMAR.S262973] [Cited by in Crossref: 9] [Cited by in F6Publishing: 11] [Article Influence: 3.0] [Reference Citation Analysis]
18 Feng M, Zhang M, Liu Y, Jiang N, Meng Q, Wang J, Yao Z, Gan W, Dai H. Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study. BMC Cancer 2020;20:611. [PMID: 32605628 DOI: 10.1186/s12885-020-07094-8] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
19 Bell D, Moore C. Linear discriminant analysis. Radiopaedia.org 2020. [DOI: 10.53347/rid-75059] [Reference Citation Analysis]
20 Ye Z, Cao L, Wei Y, Chen J, Zhang Z, Yao S, Duan T, Song B. Preoperative prediction of hepatocellular carcinoma with highly aggressive characteristics using quantitative parameters derived from hepatobiliary phase MR images. Ann Transl Med 2020;8:85. [PMID: 32175378 DOI: 10.21037/atm.2020.01.04] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]