BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Goceri E, Shah ZK, Layman R, Jiang X, Gurcan MN. Quantification of liver fat: A comprehensive review. Comput Biol Med 2016;71:174-89. [PMID: 26945465 DOI: 10.1016/j.compbiomed.2016.02.013] [Cited by in Crossref: 37] [Cited by in F6Publishing: 26] [Article Influence: 6.2] [Reference Citation Analysis]
Number Citing Articles
1 Cesaretti M, Addeo P, Schiavo L, Anty R, Iannelli A. Assessment of Liver Graft Steatosis: Where Do We Stand? Liver Transpl. 2019;25:500-509. [PMID: 30380197 DOI: 10.1002/lt.25379] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
2 Bharath R, Rajalakshmi P, Mateen MA. Multi-modal framework for automatic detection of diagnostically important regions in nonalcoholic fatty liver ultrasonic images. Biocybernetics and Biomedical Engineering 2018;38:586-601. [DOI: 10.1016/j.bbe.2018.03.008] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
3 Moccia S, Mattos LS, Patrini I, Ruperti M, Poté N, Dondero F, Cauchy F, Sepulveda A, Soubrane O, De Momi E, Diaspro A, Cesaretti M. Computer-assisted liver graft steatosis assessment via learning-based texture analysis. Int J CARS 2018;13:1357-67. [DOI: 10.1007/s11548-018-1787-6] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 3.3] [Reference Citation Analysis]
4 Hu L, Qiu C, Wang X, Xu M, Shao X, Wang Y. The association between diabetes mellitus and reduction in myocardial glucose uptake: a population-based 18F-FDG PET/CT study. BMC Cardiovasc Disord 2018;18:203. [PMID: 30373519 DOI: 10.1186/s12872-018-0943-9] [Cited by in Crossref: 12] [Cited by in F6Publishing: 9] [Article Influence: 3.0] [Reference Citation Analysis]
5 Zhou Q, Zhou Z, Chen C, Fan G, Chen G, Heng H, Ji J, Dai Y. Grading of hepatocellular carcinoma using 3D SE-DenseNet in dynamic enhanced MR images. Comput Biol Med 2019;107:47-57. [PMID: 30776671 DOI: 10.1016/j.compbiomed.2019.01.026] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.7] [Reference Citation Analysis]
6 Yang F, Jia X, Lei P, He Y, Xiang Y, Jiao J, Zhou S, Qian W, Duan Q. Quantification of hepatic steatosis in histologic images by deep learning method. J Xray Sci Technol 2019;27:1033-45. [PMID: 31744039 DOI: 10.3233/XST-190570] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
7 Teramoto T, Shinohara T, Takiyama A. Computer-aided classification of hepatocellular ballooning in liver biopsies from patients with NASH using persistent homology. Comput Methods Programs Biomed 2020;195:105614. [PMID: 32650090 DOI: 10.1016/j.cmpb.2020.105614] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
8 Shahabi M, Hassanpour H, Mashayekhi H. Rule extraction for fatty liver detection using neural networks. Neural Comput & Applic 2019;31:979-89. [DOI: 10.1007/s00521-017-3130-5] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 0.6] [Reference Citation Analysis]
9 Hu L, Shao X, Qiu C, Shao X, Wang X, Niu R, Wang Y. Hepatic steatosis is associated with abnormal hepatic enzymes, visceral adiposity, altered myocardial glucose uptake measured by 18F-FDG PET/CT. BMC Endocr Disord 2020;20:75. [PMID: 32460891 DOI: 10.1186/s12902-020-00556-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
10 Baek J, Poul SS, Basavarajappa L, Reddy S, Tai H, Hoyt K, Parker KJ. Clusters of Ultrasound Scattering Parameters for the Classification of Steatotic and Normal Livers. Ultrasound Med Biol 2021;47:3014-27. [PMID: 34315619 DOI: 10.1016/j.ultrasmedbio.2021.06.010] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Arjmand A, Angelis CT, Christou V, Tzallas AT, Tsipouras MG, Glavas E, Forlano R, Manousou P, Giannakeas N. Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples. Applied Sciences 2020;10:42. [DOI: 10.3390/app10010042] [Cited by in Crossref: 9] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
12 Zhou H, Zhou Y, Ding J, Chen Y, Wen J, Zhao L, Zhang Q, Jing X. Clinical evaluation of grayscale and linear scale hepatorenal indices for fatty liver quantification: a prospective study of a native Chinese population. Abdom Radiol (NY) 2022;47:1321-32. [PMID: 35150314 DOI: 10.1007/s00261-022-03434-3] [Reference Citation Analysis]
13 Ghelfi M, Maddalena LA, Stuart JA, Atkinson J, Harroun TA, Marquardt D. Vitamin E-inspired multi-scale imaging agent. Bioorg Med Chem Lett 2019;29:107-14. [PMID: 30459096 DOI: 10.1016/j.bmcl.2018.10.052] [Reference Citation Analysis]
14 Jianping W, Xuelian Z, Anjiang W, Haiying X. Efficacy and Safety of Glucagon-like Peptide-1 Receptor Agonists in the Treatment of Metabolic Associated Fatty Liver Disease: A Systematic Review and Meta-analysis. J Clin Gastroenterol 2021;55:586-93. [PMID: 34039937 DOI: 10.1097/MCG.0000000000001556] [Reference Citation Analysis]
15 Elbanna KY, Mansoori B, Mileto A, Rogalla P, S Guimarães L. Dual-energy CT in diffuse liver disease: is there a role? Abdom Radiol (NY) 2020;45:3413-24. [PMID: 32772121 DOI: 10.1007/s00261-020-02702-4] [Cited by in Crossref: 8] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
16 [DOI: 10.1109/tsp.2019.8768837] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
17 Tsiplakidou M, Tsipouras M, Giannakeas N, Tzallas A, Manousou P. Automated Detection of Liver Histopathological Findings Based on Biopsy Image Processing. Information 2017;8:36. [DOI: 10.3390/info8010036] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 1.8] [Reference Citation Analysis]
18 De Rudder M, Bouzin C, Nachit M, Louvegny H, Vande Velde G, Julé Y, Leclercq IA. Automated computerized image analysis for the user-independent evaluation of disease severity in preclinical models of NAFLD/NASH. Lab Invest 2020;100:147-60. [PMID: 31506634 DOI: 10.1038/s41374-019-0315-9] [Cited by in Crossref: 12] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
19 Liu F, Goh GB, Tiniakos D, Wee A, Leow WQ, Zhao JM, Rao HY, Wang XX, Wang Q, Wan WK, Lim KH, Romero-Gomez M, Petta S, Bugianesi E, Tan CK, Harrison SA, Anstee QM, Chang PJ, Wei L. qFIBS: An Automated Technique for Quantitative Evaluation of Fibrosis, Inflammation, Ballooning, and Steatosis in Patients With Nonalcoholic Steatohepatitis. Hepatology 2020;71:1953-66. [PMID: 31600834 DOI: 10.1002/hep.30986] [Cited by in Crossref: 27] [Cited by in F6Publishing: 23] [Article Influence: 13.5] [Reference Citation Analysis]
20 Qu H, Minacapelli CD, Tait C, Gupta K, Bhurwal A, Catalano C, Dafalla R, Metaxas D, Rustgi VK. Training of computational algorithms to predict NAFLD activity score and fibrosis stage from liver histopathology slides. Comput Methods Programs Biomed 2021;207:106153. [PMID: 34020377 DOI: 10.1016/j.cmpb.2021.106153] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
21 Bharath R, Mishra PK, Rajalakshmi P. Automated quantification of ultrasonic fatty liver texture based on curvelet transform and SVD. Biocybernetics and Biomedical Engineering 2018;38:145-57. [DOI: 10.1016/j.bbe.2017.12.004] [Cited by in Crossref: 8] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
22 Aguirre L, Palacios-Ortega S, Fernández-Quintela A, Hijona E, Bujanda L, Portillo MP. Pterostilbene Reduces Liver Steatosis and Modifies Hepatic Fatty Acid Profile in Obese Rats. Nutrients 2019;11:E961. [PMID: 31035507 DOI: 10.3390/nu11050961] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 2.7] [Reference Citation Analysis]
23 Pasanta D, Htun KT, Pan J, Tungjai M, Kaewjaeng S, Kim H, Kaewkhao J, Kothan S. Magnetic Resonance Spectroscopy of Hepatic Fat from Fundamental to Clinical Applications. Diagnostics (Basel) 2021;11:842. [PMID: 34067193 DOI: 10.3390/diagnostics11050842] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
24 [DOI: 10.1109/healthcom.2018.8531118] [Cited by in Crossref: 15] [Cited by in F6Publishing: 2] [Article Influence: 3.8] [Reference Citation Analysis]
25 Wang X, Xu M, Peng Y, Naren Q, Xu Y, Wang X, Yang G, Shi X, Li X. Triptolide enhances lipolysis of adipocytes by enhancing ATGL transcription via upregulation of p53. Phytother Res 2020;34:3298-310. [PMID: 32614500 DOI: 10.1002/ptr.6779] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
26 Girometti R, Pancot M, Como G, Zuiani C. Imaging of liver transplantation. Eur J Radiol 2017;93:295-307. [PMID: 28545872 DOI: 10.1016/j.ejrad.2017.05.014] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
27 Oprić D, Stankovich AD, Nenadović A, Kovačević S, Obradović DD, de Luka S, Nešović-ostojić J, Milašin J, Ilić AŽ, Trbovich AM. Fractal analysis tools for early assessment of liver inflammation induced by chronic consumption of linseed, palm and sunflower oils. Biomedical Signal Processing and Control 2020;61:101959. [DOI: 10.1016/j.bspc.2020.101959] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
28 Tien C, Remulla D, Kwon Y, Emamaullee J. Contemporary strategies to assess and manage liver donor steatosis: a review. Curr Opin Organ Transplant 2021;26:474-81. [PMID: 34524179 DOI: 10.1097/MOT.0000000000000893] [Reference Citation Analysis]
29 Svegliati-baroni G, Pierantonelli I, Torquato P, Marinelli R, Ferreri C, Chatgilialoglu C, Bartolini D, Galli F. Lipidomic biomarkers and mechanisms of lipotoxicity in non-alcoholic fatty liver disease. Free Radical Biology and Medicine 2019;144:293-309. [DOI: 10.1016/j.freeradbiomed.2019.05.029] [Cited by in Crossref: 43] [Cited by in F6Publishing: 48] [Article Influence: 14.3] [Reference Citation Analysis]
30 Armstrong T, Dregely I, Stemmer A, Han F, Natsuaki Y, Sung K, Wu HH. Free-breathing liver fat quantification using a multiecho 3D stack-of-radial technique. Magn Reson Med 2018;79:370-82. [PMID: 28419582 DOI: 10.1002/mrm.26693] [Cited by in Crossref: 26] [Cited by in F6Publishing: 18] [Article Influence: 5.2] [Reference Citation Analysis]
31 Homeyer A, Nasr P, Engel C, Kechagias S, Lundberg P, Ekstedt M, Kost H, Weiss N, Palmer T, Hahn HK, Treanor D, Lundström C. Automated quantification of steatosis: agreement with stereological point counting. Diagn Pathol 2017;12:80. [PMID: 29132399 DOI: 10.1186/s13000-017-0671-y] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 1.6] [Reference Citation Analysis]
32 Honka M, Rebelos E, Malaspina S, Nuutila P. Hepatic Positron Emission Tomography: Applications in Metabolism, Haemodynamics and Cancer. Metabolites 2022;12:321. [DOI: 10.3390/metabo12040321] [Reference Citation Analysis]
33 Das S, Swain SK, Addala PK, Balasubramaniam R, Gopakumar CV, Zirpe D, Renganathan K, Kollu H, Patel D, Vibhute BB, Rao PS, Krishnan E, Gopasetty M, Khakhar AK, Vaidya A, Ramamurthy A. Initial Poor Function and Primary Nonfunction in Deceased-Donor Orthotopic Liver Transplantation Maintaining Short Cold Ischemic Time. Prog Transplant 2016;26:340-7. [PMID: 27543202 DOI: 10.1177/1526924816663516] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 0.7] [Reference Citation Analysis]
34 Wang X, Zhang X, Ma L, Li S. Simultaneous quantification of hepatic MRI-PDFF and R2* in a rabbit model with nonalcoholic fatty liver disease. Sci China Life Sci 2018;61:1107-14. [PMID: 29934919 DOI: 10.1007/s11427-017-9279-1] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
35 Wang Q, Xue W, Zhang X, Jin F, Hahn J. S2FLNet: Hepatic steatosis detection network with body shape. Comput Biol Med 2021;140:105088. [PMID: 34864582 DOI: 10.1016/j.compbiomed.2021.105088] [Reference Citation Analysis]