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Cited by in F6Publishing
For: Yu JY, Zhang HP, Tang ZY, Zhou J, He XJ, Liu YY, Liu XJ, Guo DJ. Value of texture analysis based on enhanced MRI for predicting an early therapeutic response to transcatheter arterial chemoembolisation combined with high-intensity focused ultrasound treatment in hepatocellular carcinoma. Clin Radiol 2018; 73: 758.e9-758. e18. [PMID: 29804627 DOI: 10.1016/j.crad.2018.04.013] [Cited by in Crossref: 22] [Cited by in F6Publishing: 20] [Article Influence: 5.5] [Reference Citation Analysis]
Number Citing Articles
1 Song W, Yu X, Guo D, Liu H, Tang Z, Liu X, Zhou J, Zhang H, Liu Y. MRI-Based Radiomics: Associations With the Recurrence-Free Survival of Patients With Hepatocellular Carcinoma Treated With Conventional Transcatheter Arterial Chemoembolization.J Magn Reson Imaging. 2020;52:461-473. [PMID: 31675174 DOI: 10.1002/jmri.26977] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 5.0] [Reference Citation Analysis]
2 Haj-mirzaian A, Kadivar A, Kamel IR, Zaheer A. Updates on Imaging of Liver Tumors. Curr Oncol Rep 2020;22. [DOI: 10.1007/s11912-020-00907-w] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
3 Aujay G, Etchegaray C, Blanc J, Lapuyade B, Papadopoulos P, Pey M, Bordenave L, Trillaud H, Saut O, Pinaquy J. Comparison of MRI-based response criteria and radiomics for the prediction of early response to transarterial radioembolization in patients with hepatocellular carcinoma. Diagnostic and Interventional Imaging 2022. [DOI: 10.1016/j.diii.2022.01.009] [Reference Citation Analysis]
4 Zhang J, Liu X, Zhang H, He X, Liu Y, Zhou J, Guo D. Texture Analysis Based on Preoperative Magnetic Resonance Imaging (MRI) and Conventional MRI Features for Predicting the Early Recurrence of Single Hepatocellular Carcinoma after Hepatectomy. Academic Radiology 2019;26:1164-73. [DOI: 10.1016/j.acra.2018.10.011] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 4.0] [Reference Citation Analysis]
5 Luo J, Huang Z, Wang M, Li T, Huang J. Prognostic role of multiparameter MRI and radiomics in progression of advanced unresectable hepatocellular carcinoma following combined transcatheter arterial chemoembolization and lenvatinib therapy. BMC Gastroenterol 2022;22:108. [PMID: 35260095 DOI: 10.1186/s12876-022-02129-9] [Reference Citation Analysis]
6 Zhao Y, Wang N, Wu J, Zhang Q, Lin T, Yao Y, Chen Z, Wang M, Sheng L, Liu J, Song Q, Wang F, An X, Guo Y, Li X, Wu T, Liu AL. Radiomics Analysis Based on Contrast-Enhanced MRI for Prediction of Therapeutic Response to Transarterial Chemoembolization in Hepatocellular Carcinoma. Front Oncol. 2021;11:582788. [PMID: 33868988 DOI: 10.3389/fonc.2021.582788] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
7 Wen L, Weng S, Yan C, Ye R, Zhu Y, Zhou L, Gao L, Li Y. A Radiomics Nomogram for Preoperative Prediction of Early Recurrence of Small Hepatocellular Carcinoma After Surgical Resection or Radiofrequency Ablation. Front Oncol 2021;11:657039. [PMID: 34026632 DOI: 10.3389/fonc.2021.657039] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
8 Xiong H, He X, Guo D. Value of MRI texture analysis for predicting high-grade prostate cancer. Clin Imaging 2021;72:168-74. [PMID: 33279769 DOI: 10.1016/j.clinimag.2020.10.028] [Reference Citation Analysis]
9 Oezdemir I, Wessner CE, Shaw C, Eisenbrey JR, Hoyt K. Tumor Vascular Networks Depicted in Contrast-Enhanced Ultrasound Images as a Predictor for Transarterial Chemoembolization Treatment Response. Ultrasound Med Biol 2020;46:2276-86. [PMID: 32561069 DOI: 10.1016/j.ultrasmedbio.2020.05.010] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 3.5] [Reference Citation Analysis]
10 Thomas JV, Abou Elkassem AM, Ganeshan B, Smith AD. MR Imaging Texture Analysis in the Abdomen and Pelvis. Magn Reson Imaging Clin N Am 2020;28:447-56. [PMID: 32624161 DOI: 10.1016/j.mric.2020.03.009] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
11 Peng J, Kang S, Ning Z, Deng H, Shen J, Xu Y, Zhang J, Zhao W, Li X, Gong W, Huang J, Liu L. Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging. Eur Radiol. 2020;30:413-424. [PMID: 31332558 DOI: 10.1007/s00330-019-06318-1] [Cited by in Crossref: 29] [Cited by in F6Publishing: 22] [Article Influence: 9.7] [Reference Citation Analysis]
12 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: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
13 Avanzo M, Wei L, Stancanello J, Vallières M, Rao A, Morin O, Mattonen SA, El Naqa I. Machine and deep learning methods for radiomics. Med Phys 2020;47:e185-202. [PMID: 32418336 DOI: 10.1002/mp.13678] [Cited by in Crossref: 36] [Cited by in F6Publishing: 34] [Article Influence: 36.0] [Reference Citation Analysis]
14 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] [Reference Citation Analysis]
15 Masokano IB, Liu W, Xie S, Marcellin DFH, Pei Y, Li W. The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges. Cancer Imaging 2020;20:67. [PMID: 32962762 DOI: 10.1186/s40644-020-00341-y] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
16 Ye Z, Jiang H, Chen J, Liu X, Wei Y, Xia C, Duan T, Cao L, Zhang Z, Song B. Texture analysis on gadoxetic acid enhanced-MRI for predicting Ki-67 status in hepatocellular carcinoma: A prospective study. Chin J Cancer Res. 2019;31:806-817. [PMID: 31814684 DOI: 10.21147/j.issn.1000-9604.2019.05.10] [Cited by in Crossref: 7] [Cited by in F6Publishing: 10] [Article Influence: 2.3] [Reference Citation Analysis]
17 Ji Y, Zhu J, Zhu L, Zhu Y, Zhao H. High-Intensity Focused Ultrasound Ablation for Unresectable Primary and Metastatic Liver Cancer: Real-World Research in a Chinese Tertiary Center With 275 Cases. Front Oncol 2020;10:519164. [PMID: 33194582 DOI: 10.3389/fonc.2020.519164] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
18 Gu D, Xie Y, Wei J, Li W, Ye Z, Zhu Z, Tian J, Li X. MRI-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma.J Magn Reson Imaging. 2020;52:1679-1687. [PMID: 32491239 DOI: 10.1002/jmri.27199] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
19 Avanzo M, Trianni A, Botta F, Talamonti C, Stasi M, Iori M. Artificial Intelligence and the Medical Physicist: Welcome to the Machine. Applied Sciences 2021;11:1691. [DOI: 10.3390/app11041691] [Cited by in Crossref: 8] [Cited by in F6Publishing: 2] [Article Influence: 8.0] [Reference Citation Analysis]
20 He X, Xiong H, Zhang H, Liu X, Zhou J, Guo D. Value of MRI texture analysis for predicting new Gleason grade group. Br J Radiol 2021;94:20210005. [PMID: 33684304 DOI: 10.1259/bjr.20210005] [Reference Citation Analysis]
21 Shao Y, Zhang YX, Chen HH, Lu SS, Zhang SC, Zhang JX. Advances in the application of artificial intelligence in solid tumor imaging. Artif Intell Cancer 2021; 2(2): 12-24 [DOI: 10.35713/aic.v2.i2.12] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 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] [Reference Citation Analysis]