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For: Barry B, Buch K, Soto JA, Jara H, Nakhmani A, Anderson SW. Quantifying liver fibrosis through the application of texture analysis to diffusion weighted imaging. Magnetic Resonance Imaging 2014;32:84-90. [DOI: 10.1016/j.mri.2013.04.006] [Cited by in Crossref: 44] [Cited by in F6Publishing: 38] [Article Influence: 5.5] [Reference Citation Analysis]
Number Citing Articles
1 Tsai A, Buch K, Fujita A, Qureshi MM, Kuno H, Chapman MN, Li B, Oda M, Truong MT, Sakai O. Using CT texture analysis to differentiate between nasopharyngeal carcinoma and age-matched adenoid controls. European Journal of Radiology 2018;108:208-14. [DOI: 10.1016/j.ejrad.2018.09.012] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 1.8] [Reference Citation Analysis]
2 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]
3 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]
4 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]
5 Kawashima Y, Fujita A, Buch K, Li B, Qureshi MM, Chapman MN, Sakai O. Using texture analysis of head CT images to differentiate osteoporosis from normal bone density. European Journal of Radiology 2019;116:212-8. [DOI: 10.1016/j.ejrad.2019.05.009] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 3.3] [Reference Citation Analysis]
6 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]
7 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]
8 Razek AA, Massoud SM, Azziz MR, El-Bendary MM, Zalata K, Motawea EM. Prediction of esophageal varices in cirrhotic patients with apparent diffusion coefficient of the spleen. Abdom Imaging. 2015;40:1465-1469. [PMID: 25732406 DOI: 10.1007/s00261-015-0391-2] [Cited by in Crossref: 31] [Cited by in F6Publishing: 30] [Article Influence: 5.2] [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: 3.9] [Reference Citation Analysis]
10 Ni P, Lin Y, Zhong Q, Chen Z, Sandrasegaran K, Lin C. Technical advancements and protocol optimization of diffusion-weighted imaging (DWI) in liver. Abdom Radiol 2016;41:189-202. [DOI: 10.1007/s00261-015-0602-x] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 1.3] [Reference Citation Analysis]
11 Ito K, Muraoka H, Hirahara N, Sawada E, Okada S, Kaneda T. Quantitative assessment of normal submandibular glands and submandibular sialadenitis using CT texture analysis: A retrospective study. Oral Surg Oral Med Oral Pathol Oral Radiol 2021;132:112-7. [PMID: 33214092 DOI: 10.1016/j.oooo.2020.10.007] [Reference Citation Analysis]
12 Guan Y, Li W, Jiang Z, Zhang B, Chen Y, Huang X, Zhang J, Liu S, He J, Zhou Z, Ge Y. Value of whole-lesion apparent diffusion coefficient (ADC) first-order statistics and texture features in clinical staging of cervical cancers. Clinical Radiology 2017;72:951-8. [DOI: 10.1016/j.crad.2017.06.115] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 2.2] [Reference Citation Analysis]
13 Li J, Wang D, Chen TW, Xie F, Li R, Zhang XM, Jing ZL, Yang JQ, Ou J, Cao JM. Magnetic Resonance Diffusion Kurtosis Imaging for Evaluating Stage of Liver Fibrosis in a Rabbit Model. Acad Radiol 2019;26:e90-7. [PMID: 30072289 DOI: 10.1016/j.acra.2018.06.018] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
14 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]
15 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]
16 Simpson AL, Adams LB, Allen PJ, D'Angelica MI, DeMatteo RP, Fong Y, Kingham TP, Leung U, Miga MI, Parada EP, Jarnagin WR, Do RK. Texture analysis of preoperative CT images for prediction of postoperative hepatic insufficiency: a preliminary study. J Am Coll Surg. 2015;220:339-346. [PMID: 25537305 DOI: 10.1016/j.jamcollsurg.2014.11.027] [Cited by in Crossref: 31] [Cited by in F6Publishing: 29] [Article Influence: 3.9] [Reference Citation Analysis]
17 Chen Y, Liu Z, Mo Y, Li B, Zhou Q, Peng S, Li S, Kuang M. Prediction of Post-hepatectomy Liver Failure in Patients With Hepatocellular Carcinoma Based on Radiomics Using Gd-EOB-DTPA-Enhanced MRI: The Liver Failure Model. Front Oncol 2021;11:605296. [PMID: 33777748 DOI: 10.3389/fonc.2021.605296] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Virarkar M, Morani AC, Taggart MW, Bhosale P. Liver Fibrosis Assessment. Semin Ultrasound CT MR 2021;42:381-9. [PMID: 34130850 DOI: 10.1053/j.sult.2021.03.003] [Reference Citation Analysis]
19 Ito K, Muraoka H, Hirahara N, Sawada E, Tokunaga S, Kaneda T. Quantitative assessment of the parotid gland using computed tomography texture analysis to detect parotid sialadenitis. Oral Surg Oral Med Oral Pathol Oral Radiol 2021:S2212-4403(21)00721-5. [PMID: 34953759 DOI: 10.1016/j.oooo.2021.10.022] [Reference Citation Analysis]
20 Li B, Jara H, Yu H, O'Brien M, Soto J, Anderson SW. Enhanced Laws textures: A potential MRI surrogate marker of hepatic fibrosis in a murine model. Magn Reson Imaging 2017;37:33-40. [PMID: 27856399 DOI: 10.1016/j.mri.2016.11.008] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 1.2] [Reference Citation Analysis]
21 Fujita A, Buch K, Li B, Kawashima Y, Qureshi MM, Sakai O. Difference Between HPV-Positive and HPV-Negative Non-Oropharyngeal Head and Neck Cancer: Texture Analysis Features on CT. Journal of Computer Assisted Tomography 2016;40:43-7. [DOI: 10.1097/rct.0000000000000320] [Cited by in Crossref: 48] [Cited by in F6Publishing: 21] [Article Influence: 8.0] [Reference Citation Analysis]
22 Brenet Defour L, Mulé S, Tenenhaus A, Piardi T, Sommacale D, Hoeffel C, Thiéfin G. Hepatocellular carcinoma: CT texture analysis as a predictor of survival after surgical resection. Eur Radiol 2019;29:1231-9. [PMID: 30159621 DOI: 10.1007/s00330-018-5679-5] [Cited by in Crossref: 21] [Cited by in F6Publishing: 23] [Article Influence: 5.3] [Reference Citation Analysis]
23 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]
24 Becker AS, Schneider MA, Wurnig MC, Wagner M, Clavien PA, Boss A. Radiomics of liver MRI predict metastases in mice. Eur Radiol Exp 2018;2:11. [PMID: 29882527 DOI: 10.1186/s41747-018-0044-7] [Cited by in Crossref: 19] [Cited by in F6Publishing: 16] [Article Influence: 4.8] [Reference Citation Analysis]
25 Shao XN, Sun YJ, Xiao KT, Zhang Y, Zhang WB, Kou ZF, Cheng JL. Texture analysis of magnetic resonance T1 mapping with dilated cardiomyopathy: A machine learning approach. Medicine (Baltimore) 2018;97:e12246. [PMID: 30212958 DOI: 10.1097/MD.0000000000012246] [Cited by in Crossref: 14] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
26 Guan Y, Li W, Jiang Z, Chen Y, Liu S, He J, Zhou Z, Ge Y. Whole-Lesion Apparent Diffusion Coefficient-Based Entropy-Related Parameters for Characterizing Cervical Cancers. Academic Radiology 2016;23:1559-67. [DOI: 10.1016/j.acra.2016.08.010] [Cited by in Crossref: 25] [Cited by in F6Publishing: 27] [Article Influence: 4.2] [Reference Citation Analysis]
27 Zheng Y, Xu YS, Liu Z, Liu HF, Zhai YN, Mao XR, Lei JQ. Whole-Liver Apparent Diffusion Coefficient Histogram Analysis for the Diagnosis and Staging of Liver Fibrosis. J Magn Reson Imaging 2020;51:1745-54. [PMID: 31729811 DOI: 10.1002/jmri.26987] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
28 Meng J, Zhu L, Zhu L, Xie L, Wang H, Liu S, Yan J, Liu B, Guan Y, He J, Ge Y, Zhou Z, Yang X. Whole-lesion ADC histogram and texture analysis in predicting recurrence of cervical cancer treated with CCRT. Oncotarget 2017;8:92442-53. [PMID: 29190929 DOI: 10.18632/oncotarget.21374] [Cited by in Crossref: 18] [Cited by in F6Publishing: 18] [Article Influence: 3.6] [Reference Citation Analysis]
29 Melo RCN, Raas MWD, Palazzi C, Neves VH, Malta KK, Silva TP. Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders. Front Med (Lausanne) 2019;6:310. [PMID: 31970160 DOI: 10.3389/fmed.2019.00310] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
30 Li M, Fu S, Zhu Y, Liu Z, Chen S, Lu L, Liang C. Computed tomography texture analysis to facilitate therapeutic decision making in hepatocellular carcinoma. Oncotarget 2016;7:13248-59. [PMID: 26910890 DOI: 10.18632/oncotarget.7467] [Cited by in Crossref: 23] [Cited by in F6Publishing: 24] [Article Influence: 4.6] [Reference Citation Analysis]
31 Ito K, Muraoka H, Hirahara N, Sawada E, Hirohata S, Otsuka K, Okada S, Kaneda T. Quantitative assessment of mandibular bone marrow using computed tomography texture analysis for detect stage 0 medication-related osteonecrosis of the jaw. Eur J Radiol 2021;145:110030. [PMID: 34798536 DOI: 10.1016/j.ejrad.2021.110030] [Reference Citation Analysis]
32 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]
33 Yu H, Scalera J, Khalid M, Touret AS, Bloch N, Li B, Qureshi MM, Soto JA, Anderson SW. Texture analysis as a radiomic marker for differentiating renal tumors. Abdom Radiol (NY) 2017;42:2470-8. [PMID: 28421244 DOI: 10.1007/s00261-017-1144-1] [Cited by in Crossref: 77] [Cited by in F6Publishing: 63] [Article Influence: 19.3] [Reference Citation Analysis]
34 Fernandez-Lozano C, Seoane JA, Gestal M, Gaunt TR, Dorado J, Pazos A, Campbell C. Texture analysis in gel electrophoresis images using an integrative kernel-based approach. Sci Rep 2016;6:19256. [PMID: 26758643 DOI: 10.1038/srep19256] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
35 Alksas A, Shehata M, Saleh GA, Shaffie A, Soliman A, Ghazal M, Khelifi A, Khalifeh HA, Razek AA, Giridharan GA, El-Baz A. A novel computer-aided diagnostic system for accurate detection and grading of liver tumors. Sci Rep 2021;11:13148. [PMID: 34162893 DOI: 10.1038/s41598-021-91634-0] [Reference Citation Analysis]
36 Shehata M, Alksas A, Abouelkheir RT, Elmahdy A, Shaffie A, Soliman A, Ghazal M, Abu Khalifeh H, Salim R, Abdel Razek AAK, Alghamdi NS, El-Baz A. A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors. Sensors (Basel) 2021;21:4928. [PMID: 34300667 DOI: 10.3390/s21144928] [Reference Citation Analysis]
37 Hoffman DH, Ream JM, Hajdu CH, Rosenkrantz AB. Utility of whole-lesion ADC histogram metrics for assessing the malignant potential of pancreatic intraductal papillary mucinous neoplasms (IPMNs). Abdom Radiol (NY). 2017;42:1222-1228. [PMID: 27900458 DOI: 10.1007/s00261-016-1001-7] [Cited by in Crossref: 21] [Cited by in F6Publishing: 18] [Article Influence: 5.3] [Reference Citation Analysis]
38 Yu H, Touret A, Li B, O'brien M, Qureshi MM, Soto JA, Jara H, Anderson SW. Application of texture analysis on parametric T1 and T2 maps for detection of hepatic fibrosis: Texture Analysis on Parametric T1 and T2. J Magn Reson Imaging 2017;45:250-9. [DOI: 10.1002/jmri.25328] [Cited by in Crossref: 17] [Cited by in F6Publishing: 14] [Article Influence: 2.8] [Reference Citation Analysis]
39 Olthof SC, Krumm P, Weichold O, Eigentler T, Bösmüller H, la Fougère C, Pfannenberg C, Martus P, Klumpp B. CT texture analysis compared to Positron Emission Tomography (PET) and mutational status in resected melanoma metastases. Eur J Radiol 2020;131:109242. [PMID: 32942199 DOI: 10.1016/j.ejrad.2020.109242] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
40 Razek AAKA, Hafez MM, Mahmoud W, Ismail AR, Ali KM, Barakat TE. Diffusion tensor imaging of the spleen in prediction and grading of esophageal varices in cirrhotic children with portal hypertension. Jpn J Radiol 2021;39:907-13. [PMID: 33914254 DOI: 10.1007/s11604-021-01123-7] [Reference Citation Analysis]
41 Ito K, Kondo T, Andreu-Arasa VC, Li B, Hirahara N, Muraoka H, Sakai O, Kaneda T. Quantitative assessment of the maxillary sinusitis using computed tomography texture analysis: odontogenic vs non-odontogenic etiology. Oral Radiol 2021. [PMID: 34327595 DOI: 10.1007/s11282-021-00558-y] [Reference Citation Analysis]