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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: 65] [Cited by in F6Publishing: 68] [Article Influence: 8.1] [Reference Citation Analysis]
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18 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: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
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22 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] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 6.0] [Reference Citation Analysis]
23 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: 0.7] [Reference Citation Analysis]
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28 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: 6] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
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30 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] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
31 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: 24] [Cited by in F6Publishing: 26] [Article Influence: 8.0] [Reference Citation Analysis]
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34 Chaki J, Dey N. Statistical Texture Features. Texture Feature Extraction Techniques for Image Recognition 2020. [DOI: 10.1007/978-981-15-0853-0_2] [Reference Citation Analysis]
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36 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 (NY) 2019;44:3099-106. [PMID: 31250179 DOI: 10.1007/s00261-019-02110-3] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 1.5] [Reference Citation Analysis]
37 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: 0.5] [Reference Citation Analysis]
38 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. Acad Radiol 2019;26:1001-7. [PMID: 30393055 DOI: 10.1016/j.acra.2018.10.004] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.3] [Reference Citation Analysis]
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42 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: 11] [Cited by in F6Publishing: 12] [Article Influence: 2.2] [Reference Citation Analysis]
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