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For: Schawkat K, Ciritsis A, von Ulmenstein S, Honcharova-Biletska H, Jüngst C, Weber A, Gubler C, Mertens J, Reiner CS. Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology. Eur Radiol 2020;30:4675-85. [PMID: 32270315 DOI: 10.1007/s00330-020-06831-8] [Cited by in Crossref: 14] [Cited by in F6Publishing: 12] [Article Influence: 7.0] [Reference Citation Analysis]
Number Citing Articles
1 Dinani AM, Kowdley KV, Noureddin M. Application of Artificial Intelligence for Diagnosis and Risk Stratification in NAFLD and NASH: The State of the Art. Hepatology 2021. [PMID: 33928671 DOI: 10.1002/hep.31869] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
2 Venkatesh SK, Torbenson MS. Liver fibrosis quantification. Abdom Radiol. [DOI: 10.1007/s00261-021-03396-y] [Reference Citation Analysis]
3 Pollack BL, Batmanghelich K, Cai SS, Gordon E, Wallace S, Catania R, Morillo-Hernandez C, Furlan A, Borhani AA. Deep Learning Prediction of Voxel-Level Liver Stiffness in Patients with Nonalcoholic Fatty Liver Disease. Radiol Artif Intell 2021;3:e200274. [PMID: 34870213 DOI: 10.1148/ryai.2021200274] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Hill CE, Biasiolli L, Robson MD, Grau V, Pavlides M. Emerging artificial intelligence applications in liver magnetic resonance imaging. World J Gastroenterol 2021; 27(40): 6825-6843 [PMID: 34790009 DOI: 10.3748/wjg.v27.i40.6825] [Reference Citation Analysis]
5 Roubidoux MA, Kaur JS, Rhoades DA. Health Disparities in Cancer Among American Indians and Alaska Natives. Acad Radiol 2021:S1076-6332(21)00480-3. [PMID: 34802904 DOI: 10.1016/j.acra.2021.10.011] [Reference Citation Analysis]
6 Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F; Special Interest Group (SIG) Artificial Intelligence and Liver Diseases; Italian Association for the Study of the Liver (AISF). The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2021:S1590-8658(21)00317-0. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Reference Citation Analysis]
7 Cardobi N, Dal Palù A, Pedrini F, Beleù A, Nocini R, De Robertis R, Ruzzenente A, Salvia R, Montemezzi S, D'Onofrio M. An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging. Cancers (Basel) 2021;13:2162. [PMID: 33946223 DOI: 10.3390/cancers13092162] [Reference Citation Analysis]
8 Sofias AM, De Lorenzi F, Peña Q, Azadkhah Shalmani A, Vucur M, Wang JW, Kiessling F, Shi Y, Consolino L, Storm G, Lammers T. Therapeutic and diagnostic targeting of fibrosis in metabolic, proliferative and viral disorders. Adv Drug Deliv Rev 2021;175:113831. [PMID: 34139255 DOI: 10.1016/j.addr.2021.113831] [Reference Citation Analysis]
9 Zhao R, Gong XJ, Ge YQ, Zhao H, Wang LS, Yu HZ, Liu B. Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis. Can J Gastroenterol Hepatol 2021;2021:6677821. [PMID: 33791254 DOI: 10.1155/2021/6677821] [Reference Citation Analysis]
10 Qu Z, Yang S, Xing F, Tong R, Yang C, Guo R, Huang J, Lu F, Fu C, Yan X, Hectors S, Gillen K, Wang Y, Liu C, Zhan S, Li J. Magnetic resonance quantitative susceptibility mapping in the evaluation of hepatic fibrosis in chronic liver disease: a feasibility study. Quant Imaging Med Surg 2021;11:1170-83. [PMID: 33816158 DOI: 10.21037/qims-20-720] [Reference Citation Analysis]
11 Wang Q, Liu H, Zhu Z, Sheng Y, Du Y, Li Y, Liu J, Zhang J, Xing W. Feasibility of T1 mapping with histogram analysis for the diagnosis and staging of liver fibrosis: Preclinical results. Magn Reson Imaging 2021;76:79-86. [PMID: 33242591 DOI: 10.1016/j.mri.2020.11.006] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
12 Taouli B, Alves FC. Imaging biomarkers of diffuse liver disease: current status. Abdom Radiol (NY) 2020;45:3381-5. [PMID: 32583139 DOI: 10.1007/s00261-020-02619-y] [Reference Citation Analysis]
13 Wong GL, Yuen PC, Ma AJ, Chan AW, Leung HH, Wong VW. Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis. J Gastroenterol Hepatol 2021;36:543-50. [PMID: 33709607 DOI: 10.1111/jgh.15385] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 8.0] [Reference Citation Analysis]
14 Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, Treeprasertsuk S. Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis. BMC Gastroenterol 2021;21:10. [PMID: 33407169 DOI: 10.1186/s12876-020-01585-5] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
15 Ni M, Wang L, Yu H, Wen X, Yang Y, Liu G, Hu Y, Li Z. Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T1 -Weighted Imaging: Comparison of Different Radiomics Models. J Magn Reson Imaging 2021;53:1080-9. [PMID: 33043991 DOI: 10.1002/jmri.27391] [Reference Citation Analysis]