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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: 42] [Article Influence: 4.1] [Reference Citation Analysis]
Number Citing Articles
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2 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] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 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: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
4 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: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
5 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] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
6 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] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
7 Gu LH, Gu GX, Wan P, Li FH, Xia Q. The utility of two-dimensional shear wave elastography and texture analysis for monitoring liver fibrosis in rat model. Hepatobiliary Pancreat Dis Int 2021;20:46-52. [PMID: 32536521 DOI: 10.1016/j.hbpd.2020.05.008] [Reference Citation Analysis]
8 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: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
9 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: 12.0] [Reference Citation Analysis]
10 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: 18] [Cited by in F6Publishing: 18] [Article Influence: 9.0] [Reference Citation Analysis]
11 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: 12] [Cited by in F6Publishing: 13] [Article Influence: 4.0] [Reference Citation Analysis]
12 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: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
13 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: 1.7] [Reference Citation Analysis]
14 Huang Z, Li M, He D, Wei Y, Yu H, Wang Y, Yuan F, Song B. Two-dimensional Texture Analysis Based on CT Images to Differentiate Pancreatic Lymphoma and Pancreatic Adenocarcinoma: A Preliminary Study. Acad Radiol 2019;26:e189-95. [PMID: 30193819 DOI: 10.1016/j.acra.2018.07.021] [Cited by in Crossref: 20] [Cited by in F6Publishing: 21] [Article Influence: 6.7] [Reference Citation Analysis]
15 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: 15] [Cited by in F6Publishing: 16] [Article Influence: 5.0] [Reference Citation Analysis]
16 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: 16] [Cited by in F6Publishing: 16] [Article Influence: 5.3] [Reference Citation Analysis]
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18 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: 58] [Cited by in F6Publishing: 63] [Article Influence: 14.5] [Reference Citation Analysis]
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