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For: Daginawala N, Li B, Buch K, Yu H, Tischler B, Qureshi MM, Soto JA, Anderson S. Using texture analyses of contrast enhanced CT to assess hepatic fibrosis. Eur J Radiol. 2016;85:511-517. [PMID: 26860661 DOI: 10.1016/j.ejrad.2015.12.009] [Cited by in Crossref: 52] [Cited by in F6Publishing: 44] [Article Influence: 7.4] [Reference Citation Analysis]
Number Citing Articles
1 Homayounieh F, Saini S, Mostafavi L, Doda Khera R, Sühling M, Schmidt B, Singh R, Flohr T, Kalra MK. Accuracy of radiomics for differentiating diffuse liver diseases on non-contrast CT. Int J CARS 2020;15:1727-36. [DOI: 10.1007/s11548-020-02212-0] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Zheng BH, Liu LZ, Zhang ZZ, Shi JY, Dong LQ, Tian LY, Ding ZB, Ji Y, Rao SX, Zhou J, Fan J, Wang XY, Gao Q. Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients. BMC Cancer. 2018;18:1148. [PMID: 30463529 DOI: 10.1186/s12885-018-5024-z] [Cited by in Crossref: 52] [Cited by in F6Publishing: 52] [Article Influence: 13.0] [Reference Citation Analysis]
3 Obmann VC, Mertineit N, Berzigotti A, Marx C, Ebner L, Kreis R, Vermathen P, Heverhagen JT, Christe A, Huber AT. CT predicts liver fibrosis: Prospective evaluation of morphology- and attenuation-based quantitative scores in routine portal venous abdominal scans. PLoS One 2018;13:e0199611. [PMID: 29990333 DOI: 10.1371/journal.pone.0199611] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
4 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]
5 Li Q, Yu B, Tian X, Cui X, Zhang R, Guo Q. Deep residual nets model for staging liver fibrosis on plain CT images. Int J Comput Assist Radiol Surg 2020;15:1399-406. [PMID: 32556922 DOI: 10.1007/s11548-020-02206-y] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
6 Feng P, Wang ZD, Fan W, Liu H, Pan JJ. Diagnostic advances of artificial intelligence and radiomics in gastroenterology. Artif Intell Gastroenterol 2020; 1(2): 37-50 [DOI: 10.35712/aig.v1.i2.37] [Reference Citation Analysis]
7 Lubner MG, Pickhardt PJ. Multidetector Computed Tomography for Retrospective, Noninvasive Staging of Liver Fibrosis. Gastroenterol Clin North Am 2018;47:569-84. [PMID: 30115438 DOI: 10.1016/j.gtc.2018.04.012] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 2.5] [Reference Citation Analysis]
8 Zhu Y, Mao Y, Chen J, Qiu Y, Guan Y, Wang Z, He J. Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma. Sci Rep 2021;11:6933. [PMID: 33767315 DOI: 10.1038/s41598-021-86497-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
9 Horowitz JM, Venkatesh SK, Ehman RL, Jhaveri K, Kamath P, Ohliger MA, Samir AE, Silva AC, Taouli B, Torbenson MS, Wells ML, Yeh B, Miller FH. Evaluation of hepatic fibrosis: A review from the society of abdominal radiology disease focus panel. Abdom Radiol (NY). 2017;42:2037-2053. [PMID: 28624924 DOI: 10.1007/s00261-017-1211-7] [Cited by in Crossref: 55] [Cited by in F6Publishing: 50] [Article Influence: 11.0] [Reference Citation Analysis]
10 Alessandrino F, Qin L, Cruz G, Sahu S, Rosenthal MH, Meyerhardt JA, Shinagare AB. 5-Fluorouracil induced liver toxicity in patients with colorectal cancer: role of computed tomography texture analysis as a potential biomarker. Abdom Radiol 2019;44:3099-106. [DOI: 10.1007/s00261-019-02110-3] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.7] [Reference Citation Analysis]
11 Alnazer I, Bourdon P, Urruty T, Falou O, Khalil M, Shahin A, Fernandez-Maloigne C. Recent advances in medical image processing for the evaluation of chronic kidney disease. Med Image Anal 2021;69:101960. [PMID: 33517241 DOI: 10.1016/j.media.2021.101960] [Reference Citation Analysis]
12 Petitclerc L, Sebastiani G, Gilbert G, Cloutier G, Tang A. Liver fibrosis: Review of current imaging and MRI quantification techniques. J Magn Reson Imaging. 2017;45:1276-1295. [PMID: 27981751 DOI: 10.1002/jmri.25550] [Cited by in Crossref: 89] [Cited by in F6Publishing: 78] [Article Influence: 14.8] [Reference Citation Analysis]
13 Lubner MG, Jones D, Kloke J, Said A, Pickhardt PJ. CT texture analysis of the liver for assessing hepatic fibrosis in patients with hepatitis C virus. Br J Radiol 2019;92:20180153. [PMID: 30182750 DOI: 10.1259/bjr.20180153] [Cited by in Crossref: 10] [Cited by in F6Publishing: 12] [Article Influence: 2.5] [Reference Citation Analysis]
14 Lubner MG, Graffy PM, Said A, Watson R, Zea R, Malecki KM, Pickhardt PJ. Utility of Multiparametric CT for Identification of High-Risk NAFLD. AJR Am J Roentgenol 2021;216:659-68. [PMID: 33474981 DOI: 10.2214/AJR.20.22842] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Choi B, Choi IY, Cha SH, Yeom SK, Chung HH, Lee SH, Cha J, Lee JH. Feasibility of computed tomography texture analysis of hepatic fibrosis using dual-energy spectral detector computed tomography. Jpn J Radiol 2020;38:1179-89. [PMID: 32666182 DOI: 10.1007/s11604-020-01020-5] [Reference Citation Analysis]
16 Nandwana SB, Finazzo J, Alexander LF, Cox K. Using qualitative descriptors of chronic liver disease on MRI: A practice prone to error. Clin Imaging 2021;74:89-92. [PMID: 33461018 DOI: 10.1016/j.clinimag.2020.12.023] [Reference Citation Analysis]
17 Chartampilas E. Imaging of nonalcoholic fatty liver disease and its clinical utility. Hormones (Athens) 2018;17:69-81. [PMID: 29858854 DOI: 10.1007/s42000-018-0012-x] [Cited by in Crossref: 14] [Cited by in F6Publishing: 11] [Article Influence: 3.5] [Reference Citation Analysis]
18 Li W, Huang Y, Zhuang BW, Liu GJ, Hu HT, Li X, Liang JY, Wang Z, Huang XW, Zhang CQ, Ruan SM, Xie XY, Kuang M, Lu MD, Chen LD, Wang W. Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis. Eur Radiol. 2019;29:1496-1506. [PMID: 30178143 DOI: 10.1007/s00330-018-5680-z] [Cited by in Crossref: 34] [Cited by in F6Publishing: 33] [Article Influence: 8.5] [Reference Citation Analysis]
19 Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. Radiographics. 2017;37:1483-1503. [PMID: 28898189 DOI: 10.1148/rg.2017170056] [Cited by in Crossref: 300] [Cited by in F6Publishing: 278] [Article Influence: 60.0] [Reference Citation Analysis]
20 Aubé C, Bazeries P, Lebigot J, Cartier V, Boursier J. Liver fibrosis, cirrhosis, and cirrhosis-related nodules: Imaging diagnosis and surveillance. Diagnostic and Interventional Imaging 2017;98:455-68. [DOI: 10.1016/j.diii.2017.03.003] [Cited by in Crossref: 14] [Cited by in F6Publishing: 12] [Article Influence: 2.8] [Reference Citation Analysis]
21 Naganawa S, Enooku K, Tateishi R, Akai H, Yasaka K, Shibahara J, Ushiku T, Abe O, Ohtomo K, Kiryu S. Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis. Eur Radiol. 2018;28:3050-3058. [PMID: 29404772 DOI: 10.1007/s00330-017-5270-5] [Cited by in Crossref: 20] [Cited by in F6Publishing: 19] [Article Influence: 5.0] [Reference Citation Analysis]
22 Hu W, Yang H, Xu H, Mao Y. Radiomics based on artificial intelligence in liver diseases: where we are? Gastroenterol Rep (Oxf) 2020;8:90-7. [PMID: 32280468 DOI: 10.1093/gastro/goaa011] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 4.0] [Reference Citation Analysis]
23 He D, Zhang C, Qiu W, Xie Q. Diagnosis of liver fibrosis in patients with hepatitis B-related liver disease using ultrasound with wave-number domain attenuation coefficient. Turk J Gastroenterol 2020;31:923-9. [PMID: 33626006 DOI: 10.5152/tjg.2020.20139] [Reference Citation Analysis]
24 Ding S, Yang W, Sun X, Guo Y, Zhao G, Yang J, Zhang L, Lv G, Kim KG. Computed Tomography-Based Radiomic Analysis for Preoperatively Predicting the Macrovesicular Steatosis Grade in Cadaveric Donor Liver Transplantation. BioMed Research International 2022;2022:1-9. [DOI: 10.1155/2022/2491023] [Reference Citation Analysis]
25 Corrias G, Micheletti G, Barberini L, Suri JS, Saba L. Texture analysis imaging "what a clinical radiologist needs to know". Eur J Radiol 2022;146:110055. [PMID: 34902669 DOI: 10.1016/j.ejrad.2021.110055] [Reference Citation Analysis]
26 Fu S, Chen S, Liang C, Liu Z, Zhu Y, Li Y, Lu L. Texture analysis of intermediate-advanced hepatocellular carcinoma: prognosis and patients' selection of transcatheter arterial chemoembolization and sorafenib. Oncotarget 2017;8:37855-65. [PMID: 27911268 DOI: 10.18632/oncotarget.13675] [Cited by in Crossref: 24] [Cited by in F6Publishing: 22] [Article Influence: 6.0] [Reference Citation Analysis]
27 Buch K, Li B, Qureshi MM, Kuno H, Anderson SW, Sakai O. Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom. AJNR Am J Neuroradiol 2017;38:981-5. [PMID: 28341714 DOI: 10.3174/ajnr.A5139] [Cited by in Crossref: 28] [Cited by in F6Publishing: 18] [Article Influence: 5.6] [Reference Citation Analysis]
28 Li J, Qureshi M, Gupta A, Anderson SW, Soto J, Li B. Quantification of Degree of Liver Fibrosis Using Fibrosis Area Fraction Based on Statistical Chi-Square Analysis of Heterogeneity of Liver Tissue Texture on Routine Ultrasound Images. Academic Radiology 2019;26:1001-7. [DOI: 10.1016/j.acra.2018.10.004] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
29 Li C, Li R, Zhang W. Progress in non-invasive detection of liver fibrosis. Cancer Biol Med 2018;15:124-36. [PMID: 29951337 DOI: 10.20892/j.issn.2095-3941.2018.0018] [Cited by in Crossref: 31] [Cited by in F6Publishing: 30] [Article Influence: 7.8] [Reference Citation Analysis]
30 Liu S, Pan X, Liu R, Zheng H, Chen L, Guan W, Wang H, Sun Y, Tang L, Guan Y, Ge Y, He J, Zhou Z. Texture analysis of CT images in predicting malignancy risk of gastrointestinal stromal tumours. Clin Radiol. 2018;73:266-274. [PMID: 28969853 DOI: 10.1016/j.crad.2017.09.003] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 2.4] [Reference Citation Analysis]
31 Wang J, Li H, Zhou X, Gao X, Wang M. A study of hepatic fibrosis staging methods using diffraction enhanced imaging. J Image Video Proc 2020;2020. [DOI: 10.1186/s13640-020-00520-8] [Reference Citation Analysis]
32 MacCurtain BM, Quirke NP, Thorpe SD, Gallagher TK. Pancreatic Ductal Adenocarcinoma: Relating Biomechanics and Prognosis. J Clin Med 2021;10:2711. [PMID: 34205335 DOI: 10.3390/jcm10122711] [Reference Citation Analysis]
33 Hu P, Hu X, Lin Y, Yu X, Tao X, Sun J, Wu X. A Combination Model of Radiomics Features and Clinical Biomarkers as a Nomogram to Differentiate Nonadvanced From Advanced Liver Fibrosis: A Retrospective Study. Acad Radiol 2021:S1076-6332(20)30510-9. [PMID: 34023199 DOI: 10.1016/j.acra.2020.08.029] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
34 Lubner MG, Jones D, Said A, Kloke J, Lee S, Pickhardt PJ. Accuracy of liver surface nodularity quantification on MDCT for staging hepatic fibrosis in patients with hepatitis C virus. Abdom Radiol (NY) 2018;43:2980-6. [PMID: 29572714 DOI: 10.1007/s00261-018-1572-6] [Cited by in Crossref: 14] [Cited by in F6Publishing: 12] [Article Influence: 7.0] [Reference Citation Analysis]
35 Shin HJ, Kwak JY, Lee E, Lee M, Yoon H, Han K, Kim M. Texture Analysis to Differentiate Malignant Renal Tumors in Children Using Gray-Scale Ultrasonography Images. Ultrasound in Medicine & Biology 2019;45:2205-12. [DOI: 10.1016/j.ultrasmedbio.2019.03.017] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
36 Buch K, Kuno H, Qureshi MM, Li B, Sakai O. Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model. J Appl Clin Med Phys 2018;19:253-64. [PMID: 30369010 DOI: 10.1002/acm2.12482] [Cited by in Crossref: 27] [Cited by in F6Publishing: 23] [Article Influence: 6.8] [Reference Citation Analysis]
37 Budai BK, Tóth A, Borsos P, Frank VG, Shariati S, Fejér B, Folhoffer A, Szalay F, Bérczi V, Kaposi PN. Three-dimensional CT texture analysis of anatomic liver segments can differentiate between low-grade and high-grade fibrosis.BMC Med Imaging. 2020;20:108. [PMID: 32957949 DOI: 10.1186/s12880-020-00508-w] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
38 Lubner MG, Pickhardt PJ. Multidetector computed tomography for assessment of hepatic fibrosis. Clin Liver Dis (Hoboken) 2018;11:156-61. [PMID: 30992808 DOI: 10.1002/cld.715] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 0.8] [Reference Citation Analysis]
39 Esposito A, Palmisano A, Antunes S, Colantoni C, Rancoita PMV, Vignale D, Baratto F, Della Bella P, Del Maschio A, De Cobelli F. Assessment of Remote Myocardium Heterogeneity in Patients with Ventricular Tachycardia Using Texture Analysis of Late Iodine Enhancement (LIE) Cardiac Computed Tomography (cCT) Images. Mol Imaging Biol 2018;20:816-25. [PMID: 29536321 DOI: 10.1007/s11307-018-1175-1] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 2.3] [Reference Citation Analysis]
40 Pak LM, Chakraborty J, Gonen M, Chapman WC, Do RKG, Groot Koerkamp B, Verhoef K, Lee SY, Massani M, van der Stok EP, Simpson AL; Memorial Sloan Kettering Cancer Center Hepatopancreatobiliary Service. Quantitative Imaging Features and Postoperative Hepatic Insufficiency: A Multi-Institutional Expanded Cohort. J Am Coll Surg 2018;226:835-43. [PMID: 29454098 DOI: 10.1016/j.jamcollsurg.2018.02.001] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
41 Yeung J, Ganeshan B, Endozo R, Hall A, Wan S, Groves A, Taylor SA, Bandula S. Equilibrium CT Texture Analysis for the Evaluation of Hepatic Fibrosis: Preliminary Evaluation against Histopathology and Extracellular Volume Fraction. J Pers Med 2020;10:E46. [PMID: 32485820 DOI: 10.3390/jpm10020046] [Reference Citation Analysis]
42 Sung P, Lee JM, Joo I, Lee S, Kim TH, Ganeshan B. Evaluation of the Impact of Iterative Reconstruction Algorithms on Computed Tomography Texture Features of the Liver Parenchyma Using the Filtration-Histogram Method. Korean J Radiol 2019;20:558-68. [PMID: 30887738 DOI: 10.3348/kjr.2018.0368] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
43 Obmann VC, Marx C, Hrycyk J, Berzigotti A, Ebner L, Mertineit N, Gräni C, Heverhagen JT, Christe A, Huber AT. Liver segmental volume and attenuation ratio (LSVAR) on portal venous CT scans improves the detection of clinically significant liver fibrosis compared to liver segmental volume ratio (LSVR). Abdom Radiol (NY) 2021;46:1912-21. [PMID: 33156949 DOI: 10.1007/s00261-020-02834-7] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
44 Lubner MG, Malecki K, Kloke J, Ganeshan B, Pickhardt PJ. Texture analysis of the liver at MDCT for assessing hepatic fibrosis. Abdom Radiol (NY). 2017;42:2069-2078. [PMID: 28314916 DOI: 10.1007/s00261-017-1096-5] [Cited by in Crossref: 44] [Cited by in F6Publishing: 36] [Article Influence: 8.8] [Reference Citation Analysis]
45 Cannella R, Borhani AA, Tublin M, Behari J, Furlan A. Diagnostic value of MR-based texture analysis for the assessment of hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD). Abdom Radiol (NY) 2019;44:1816-24. [PMID: 30788556 DOI: 10.1007/s00261-019-01931-6] [Cited by in Crossref: 14] [Cited by in F6Publishing: 10] [Article Influence: 7.0] [Reference Citation Analysis]
46 Ding J, Xing Z, Jiang Z, Zhou H, Di J, Chen J, Qiu J, Yu S, Zou L, Xing W. Evaluation of renal dysfunction using texture analysis based on DWI, BOLD, and susceptibility-weighted imaging. Eur Radiol 2019;29:2293-301. [PMID: 30560361 DOI: 10.1007/s00330-018-5911-3] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 1.3] [Reference Citation Analysis]
47 Park J, Kim JH, Kim JE, Park SJ, Yi NJ, Han JK. Prediction of liver regeneration in recipients after living-donor liver transplantation in using preoperative CT texture analysis and clinical features. Abdom Radiol (NY) 2020;45:3763-74. [PMID: 32296898 DOI: 10.1007/s00261-020-02518-2] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]