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Cited by in F6Publishing
For: Son JH, Lee SS, Lee Y, Kang BK, Sung YS, Jo S, Yu E. Assessment of liver fibrosis severity using computed tomography-based liver and spleen volumetric indices in patients with chronic liver disease.Eur Radiol. 2020;30:3486-3496. [PMID: 32055946 DOI: 10.1007/s00330-020-06665-4] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Müller L, Kloeckner R, Mähringer-Kunz A, Stoehr F, Düber C, Arnhold G, Gairing SJ, Foerster F, Weinmann A, Galle PR, Mittler J, Pinto Dos Santos D, Hahn F. Fully automated AI-based splenic segmentation for predicting survival and estimating the risk of hepatic decompensation in TACE patients with HCC. Eur Radiol 2022. [PMID: 35394184 DOI: 10.1007/s00330-022-08737-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 [DOI: 10.1109/isbi52829.2022.9761467] [Reference Citation Analysis]
3 Wang J, Tang S, Mao Y, Wu J, Xu S, Yue Q, Chen J, He J, Yin Y. Radiomics analysis of contrast-enhanced CT for staging liver fibrosis: an update for image biomarker. Hepatol Int. [DOI: 10.1007/s12072-022-10326-7] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Wu L, Ning B, Yang J, Chen Y, Zhang C, Yan Y, Koundal D. Diagnosis of Liver Cirrhosis and Liver Fibrosis by Artificial Intelligence Algorithm-Based Multislice Spiral Computed Tomography. Computational and Mathematical Methods in Medicine 2022;2022:1-8. [DOI: 10.1155/2022/1217003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Heo S, Kim DW, Choi SH, Kim SW, Jang JK. Diagnostic performance of liver fibrosis assessment by quantification of liver surface nodularity on computed tomography and magnetic resonance imaging: systematic review and meta-analysis. Eur Radiol. [DOI: 10.1007/s00330-021-08436-1] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Meddeb A, Kossen T, Bressem KK, Hamm B, Nagel SN. Evaluation of a Deep Learning Algorithm for Automated Spleen Segmentation in Patients with Conditions Directly or Indirectly Affecting the Spleen. Tomography 2021;7:950-60. [PMID: 34941650 DOI: 10.3390/tomography7040078] [Reference Citation Analysis]
7 Harada K, Ishinuki T, Ohashi Y, Tanaka T, Chiba A, Numasawa K, Imai T, Hayasaka S, Tsugiki T, Miyanishi K, Nagayama M, Takemasa I, Kato J, Mizuguchi T. Nature of the liver volume depending on the gender and age assessing volumetry from a reconstruction of the computed tomography. PLoS One 2021;16:e0261094. [PMID: 34879120 DOI: 10.1371/journal.pone.0261094] [Reference Citation Analysis]
8 Kwon JH, Lee SS, Yoon JS, Suk HI, Sung YS, Kim HS, Lee CM, Kim KM, Lee SJ, Kim SY. Liver-to-Spleen Volume Ratio Automatically Measured on CT Predicts Decompensation in Patients with B Viral Compensated Cirrhosis. Korean J Radiol 2021;22:1985-95. [PMID: 34564961 DOI: 10.3348/kjr.2021.0348] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
9 Liu W, Liu X, Peng M, Chen GQ, Liu PH, Cui XW, Jiang F, Dietrich CF. Artificial intelligence for hepatitis evaluation. World J Gastroenterol 2021; 27(34): 5715-5726 [PMID: 34629796 DOI: 10.3748/wjg.v27.i34.5715] [Cited by in CrossRef: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
10 Sung YS, Park B, Park HJ, Lee SS. Radiomics and deep learning in liver diseases. J Gastroenterol Hepatol 2021;36:561-8. [PMID: 33709608 DOI: 10.1111/jgh.15414] [Reference Citation Analysis]
11 Kim DW, Ha J, Lee SS, Kwon JH, Kim NY, Sung YS, Yoon JS, Suk HI, Lee Y, Kang BK. Population-based and Personalized Reference Intervals for Liver and Spleen Volumes in Healthy Individuals and Those with Viral Hepatitis. Radiology 2021;:204183. [PMID: 34402668 DOI: 10.1148/radiol.2021204183] [Reference Citation Analysis]
12 Im WH, Song JS, Jang W. Noninvasive staging of liver fibrosis: review of current quantitative CT and MRI-based techniques. Abdom Radiol (NY) 2021. [PMID: 34228199 DOI: 10.1007/s00261-021-03181-x] [Reference Citation Analysis]
13 Dai HT, Chen B, Tang KY, Zhang GY, Wen CY, Xiang XH, Yang JY, Guo Y, Lin R, Huang YH. Prognostic value of splenic volume in hepatocellular carcinoma patients receiving transarterial chemoembolization. J Gastrointest Oncol 2021;12:1141-51. [PMID: 34295563 DOI: 10.21037/jgo-21-226] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
14 Nagayama Y, Kato Y, Inoue T, Nakaura T, Oda S, Kidoh M, Ikeda O, Hirai T. Liver fibrosis assessment with multiphasic dual-energy CT: diagnostic performance of iodine uptake parameters. Eur Radiol 2021;31:5779-90. [PMID: 33768289 DOI: 10.1007/s00330-021-07706-2] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Ahn Y, Yoon JS, Lee SS, Suk HI, Son JH, Sung YS, Lee Y, Kang BK, Kim HS. Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images. Korean J Radiol 2020;21:987-97. [PMID: 32677383 DOI: 10.3348/kjr.2020.0237] [Cited by in Crossref: 11] [Cited by in F6Publishing: 18] [Article Influence: 5.5] [Reference Citation Analysis]
16 Bao H, Li G, Fang Y, Lai Q, Bao H, Zheng Y, Hu Y. Immunosuppression and cardiovascular dysfunction in patients with severe versus mild coronavirus disease 2019: a case series. Clin Transl Immunology 2020;9:e1188. [PMID: 33024561 DOI: 10.1002/cti2.1188] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]