BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
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 Zhang D, Cao Y, Sun Y, Zhao X, Peng C, Zhao J, Bao X, Wang L, Zhang C. Radiomics nomograms based on R2* mapping and clinical biomarkers for staging of liver fibrosis in patients with chronic hepatitis B: a single-center retrospective study. Eur Radiol 2022. [PMID: 36149481 DOI: 10.1007/s00330-022-09137-z] [Reference Citation Analysis]
2 Zou L, Zhang H, Wang Q, Zhong W, Du Y, Liu H, Xing W. Simultaneous liver steatosis, fibrosis and iron deposition quantification with mDixon quant based on radiomics analysis in a rabbit model. Magn Reson Imaging 2022:S0730-725X(22)00147-3. [PMID: 35988836 DOI: 10.1016/j.mri.2022.08.013] [Reference Citation Analysis]
3 Roubidoux MA, Kaur JS, Rhoades DA. Health Disparities in Cancer Among American Indians and Alaska Natives. Acad Radiol 2022;29:1013-21. [PMID: 34802904 DOI: 10.1016/j.acra.2021.10.011] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Zhao R, Zhao H, Ge Y, Zhou F, Wang L, Yu H, Gong X, Granito A. Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis. Canadian Journal of Gastroenterology and Hepatology 2022;2022:1-9. [DOI: 10.1155/2022/2249447] [Reference Citation Analysis]
5 Attia IM. Log-Linear Model and Multistate Model to Assess the Rate of Fibrosis in Patients With NAFLD. Front Appl Math Stat 2022;8:899247. [DOI: 10.3389/fams.2022.899247] [Reference Citation Analysis]
6 Sim KC, Kim MJ, Cho Y, Kim HJ, Park BJ, Sung DJ, Han YE, Han NY, Kim TH, Lee YJ. Diagnostic Feasibility of Magnetic Resonance Elastography Radiomics Analysis for the Assessment of Hepatic Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. J Comput Assist Tomogr 2022. [PMID: 35483092 DOI: 10.1097/RCT.0000000000001308] [Reference Citation Analysis]
7 Chen ZW, Xiao HM, Ye X, Liu K, Rios RS, Zheng KI, Jin Y, Targher G, Byrne CD, Shi J, Yan Z, Chi XL, Zheng MH. A novel radiomics signature based on T2-weighted imaging accurately predicts hepatic inflammation in individuals with biopsy-proven nonalcoholic fatty liver disease: a derivation and independent validation study. Hepatobiliary Surg Nutr 2022;11:212-26. [PMID: 35464279 DOI: 10.21037/hbsn-21-23] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
8 Venkatesh SK, Torbenson MS. Liver fibrosis quantification. Abdom Radiol (NY) 2022;47:1032-52. [PMID: 35022806 DOI: 10.1007/s00261-021-03396-y] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
9 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] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 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] [Cited by in CrossRef: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
11 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;74:2233-40. [PMID: 33928671 DOI: 10.1002/hep.31869] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
12 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]
13 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] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
14 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] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
15 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]
16 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] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
17 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]
18 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] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
19 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: 15] [Article Influence: 3.0] [Reference Citation Analysis]
20 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]
21 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] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 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] [Cited by in F6Publishing: 5] [Reference Citation Analysis]