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For: Bahl G, Cruite I, Wolfson T, Gamst AC, Collins JM, Chavez AD, Barakat F, Hassanein T, Sirlin CB. Noninvasive classification of hepatic fibrosis based on texture parameters from double contrast-enhanced magnetic resonance images. J Magn Reson Imaging 2012;36:1154-61. [PMID: 22851409 DOI: 10.1002/jmri.23759] [Cited by in Crossref: 41] [Cited by in F6Publishing: 35] [Article Influence: 4.6] [Reference Citation Analysis]
Number Citing Articles
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8 Mundim MB, Dias DR, Costa RM, Leles CR, Azevedo-Marques PM, Ribeiro-Rotta RF. Intraoral radiographs texture analysis for dental implant planning. Comput Methods Programs Biomed 2016;136:89-96. [PMID: 27686706 DOI: 10.1016/j.cmpb.2016.08.012] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 2.4] [Reference Citation Analysis]
9 Yu H, Buch K, Li B, O'brien M, Soto J, Jara H, Anderson SW. Utility of texture analysis for quantifying hepatic fibrosis on proton density MRI: Hepatic Fibrosis on Proton Density MRI. J Magn Reson Imaging 2015;42:1259-65. [DOI: 10.1002/jmri.24898] [Cited by in Crossref: 27] [Cited by in F6Publishing: 24] [Article Influence: 4.5] [Reference Citation Analysis]
10 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: 2.0] [Reference Citation Analysis]
11 Kierans AS, Rusinek H, Lee A, Shaikh MB, Triolo M, Huang WC, Chandarana H. Textural differences in apparent diffusion coefficient between low- and high-stage clear cell renal cell carcinoma. AJR Am J Roentgenol. 2014;203:W637-W644. [PMID: 25415729 DOI: 10.2214/ajr.14.12570] [Cited by in Crossref: 51] [Cited by in F6Publishing: 19] [Article Influence: 8.5] [Reference Citation Analysis]
12 Li Y, Yan C, Weng S, Shi Z, Sun H, Chen J, Xu X, Ye R, Hong J. Texture analysis of multi-phase MRI images to detect expression of Ki67 in hepatocellular carcinoma. Clin Radiol 2019;74:813.e19-27. [PMID: 31362887 DOI: 10.1016/j.crad.2019.06.024] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
13 Aguirre-Reyes DF, Sotelo JA, Arab JP, Arrese M, Tejos R, Irarrazaval P, Tejos C, Uribe SA, Andia ME. Intrahepatic portal vein blood volume estimated by non-contrast magnetic resonance imaging for the assessment of portal hypertension. Magn Reson Imaging 2015;33:970-7. [PMID: 26117696 DOI: 10.1016/j.mri.2015.06.016] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 0.5] [Reference Citation Analysis]
14 Gourtsoyianni S, Santinha J, Matos C, Papanikolaou N. Diffusion-weighted imaging and texture analysis: current role for diffuse liver disease. Abdom Radiol (NY) 2020;45:3523-31. [PMID: 33064169 DOI: 10.1007/s00261-020-02772-4] [Reference Citation Analysis]
15 Huang Y, Chen Y, Zhu H, Li W, Ge Y, Huang X, He J. A liver fibrosis staging method using cross-contrast network. Expert Systems with Applications 2019;130:124-31. [DOI: 10.1016/j.eswa.2019.03.049] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
16 Yokoo T, Wolfson T, Iwaisako K, Peterson MR, Mani H, Goodman Z, Changchien C, Middleton MS, Gamst AC, Mazhar SM, Kono Y, Ho SB, Sirlin CB. Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0T. Biomed Res Int 2015;2015:387653. [PMID: 26421287 DOI: 10.1155/2015/387653] [Cited by in Crossref: 18] [Cited by in F6Publishing: 19] [Article Influence: 3.0] [Reference Citation Analysis]
17 Yan L, Liu Z, Wang G, Huang Y, Liu Y, Yu Y, Liang C. Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images. Acad Radiol. 2015;22:1115-1121. [PMID: 26031228 DOI: 10.1016/j.acra.2015.04.004] [Cited by in Crossref: 58] [Cited by in F6Publishing: 59] [Article Influence: 9.7] [Reference Citation Analysis]
18 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: 8.7] [Reference Citation Analysis]
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20 Thomas JV, Abou Elkassem AM, Ganeshan B, Smith AD. MR Imaging Texture Analysis in the Abdomen and Pelvis. Magn Reson Imaging Clin N Am 2020;28:447-56. [PMID: 32624161 DOI: 10.1016/j.mric.2020.03.009] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
21 Park HJ, Lee SS, Park B, Yun J, Sung YS, Shim WH, Shin YM, Kim SY, Lee SJ, Lee MG. Radiomics Analysis of Gadoxetic Acid-enhanced MRI for Staging Liver Fibrosis. Radiology. 2019;290:380-387. [PMID: 30615554 DOI: 10.1148/radiol.2018181197] [Cited by in Crossref: 37] [Cited by in F6Publishing: 35] [Article Influence: 12.3] [Reference Citation Analysis]
22 Petitclerc L, Gilbert G, Nguyen BN, Tang A. Liver Fibrosis Quantification by Magnetic Resonance Imaging. Top Magn Reson Imaging 2017;26:229-41. [PMID: 28858038 DOI: 10.1097/RMR.0000000000000149] [Cited by in Crossref: 17] [Cited by in F6Publishing: 8] [Article Influence: 5.7] [Reference Citation Analysis]
23 Kierans AS, Doshi AM, Dunst D, Popiolek D, Blank SV, Rosenkrantz AB. Retrospective Assessment of Histogram-Based Diffusion Metrics for Differentiating Benign and Malignant Endometrial Lesions: . Journal of Computer Assisted Tomography 2016;40:723-9. [DOI: 10.1097/rct.0000000000000430] [Cited by in Crossref: 11] [Cited by in F6Publishing: 4] [Article Influence: 2.2] [Reference Citation Analysis]
24 Li Y, Xu X, Weng S, Yan C, Chen J, Ye R. CT Image-Based Texture Analysis to Predict Microvascular Invasion in Primary Hepatocellular Carcinoma. J Digit Imaging 2020;33:1365-75. [PMID: 32968880 DOI: 10.1007/s10278-020-00386-2] [Reference Citation Analysis]
25 Lurie Y, Webb M, Cytter-Kuint R, Shteingart S, Lederkremer GZ. Non-invasive diagnosis of liver fibrosis and cirrhosis. World J Gastroenterol 2015; 21(41): 11567-11583 [PMID: 26556987 DOI: 10.3748/wjg.v21.i41.11567] [Cited by in CrossRef: 120] [Cited by in F6Publishing: 105] [Article Influence: 20.0] [Reference Citation Analysis]
26 Masi B, Perles-Barbacaru TA, Bernard M, Viola A. Clinical and Preclinical Imaging of Hepatosplenic Schistosomiasis. Trends Parasitol 2020;36:206-26. [PMID: 31864895 DOI: 10.1016/j.pt.2019.11.007] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 3.5] [Reference Citation Analysis]
27 Mathew RP, Venkatesh SK. Imaging of Hepatic Fibrosis. Curr Gastroenterol Rep 2018;20. [DOI: 10.1007/s11894-018-0652-7] [Cited by in Crossref: 18] [Cited by in F6Publishing: 14] [Article Influence: 6.0] [Reference Citation Analysis]
28 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: 14.0] [Reference Citation Analysis]
29 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: 13.8] [Reference Citation Analysis]
30 Qiu QT, Zhang J, Duan JH, Wu SZ, Ding JL, Yin Y. Development and validation of radiomics model built by incorporating machine learning for identifying liver fibrosis and early-stage cirrhosis. Chin Med J (Engl) 2020;133:2653-9. [PMID: 33009025 DOI: 10.1097/CM9.0000000000001113] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
31 Zhang P, Min X, Feng Z, Kang Z, Li B, Cai W, Fan C, Yin X, Xie J, Lv W, Wang L. Value of Intra-Perinodular Textural Transition Features from MRI in Distinguishing Between Benign and Malignant Testicular Lesions. Cancer Manag Res 2021;13:839-47. [PMID: 33536790 DOI: 10.2147/CMAR.S288378] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
32 Zaworski C, Cheah J, Koff MF, Breighner R, Lin B, Harrison J, Donnelly E, Stein EM. MRI-based Texture Analysis of Trabecular Bone for Opportunistic Screening of Skeletal Fragility. J Clin Endocrinol Metab 2021;106:2233-41. [PMID: 33999148 DOI: 10.1210/clinem/dgab342] [Reference Citation Analysis]
33 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: 14.0] [Reference Citation Analysis]
34 Wáng YX, Idée JM. A comprehensive literatures update of clinical researches of superparamagnetic resonance iron oxide nanoparticles for magnetic resonance imaging. Quant Imaging Med Surg 2017;7:88-122. [PMID: 28275562 DOI: 10.21037/qims.2017.02.09] [Cited by in Crossref: 104] [Cited by in F6Publishing: 101] [Article Influence: 26.0] [Reference Citation Analysis]
35 Kromrey M, Le Bihan D, Ichikawa S, Motosugi U. Diffusion-weighted MRI-based Virtual Elastography for the Assessment of Liver Fibrosis. Radiology 2020;295:127-35. [DOI: 10.1148/radiol.2020191498] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 7.0] [Reference Citation Analysis]