BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: 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: 32] [Cited by in F6Publishing: 33] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Zou L, Zhang H, Wang Q, Zhong W, Du Y, Liu H, Xing W. Simultaneous liver steatosis, fibrosis and iron deposition quantification with mDixon quant based on radiomics analysis in a rabbit model. Magn Reson Imaging 2022:S0730-725X(22)00147-3. [PMID: 35988836 DOI: 10.1016/j.mri.2022.08.013] [Reference Citation Analysis]
2 Hof S, Marcus C, Kuebart A, Schulz J, Truse R, Raupach A, Bauer I, Flögel U, Picker O, Herminghaus A, Temme S. A Toolbox to Investigate the Impact of Impaired Oxygen Delivery in Experimental Disease Models. Front Med 2022;9:869372. [DOI: 10.3389/fmed.2022.869372] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Hu R, Li H, Horng H, Thomasian NM, Jiao Z, Zhu C, Zou B, Bai HX. Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI. Sci Rep 2022;12:7924. [PMID: 35562532 DOI: 10.1038/s41598-022-11997-w] [Reference Citation Analysis]
4 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]
5 Liang P, Li S, Xu C, Li J, Tan F, Hu D, Kamel I, Li Z. Assessment of renal function using magnetic resonance quantitative histogram analysis based on spatial labeling with multiple inversion pulses. Ann Transl Med 2021;9:1614. [PMID: 34926658 DOI: 10.21037/atm-21-2299] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
6 Alnazer I, Falou O, Urruty T, Bourdon P, Guillevin C, Naudin M, Khalil M, Shahin A, Fernandez-maloigne C. Usefulness of Functional MRI Textures in the Evaluation of Renal Function. 2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME) 2021. [DOI: 10.1109/icabme53305.2021.9604879] [Reference Citation Analysis]
7 Zhong X, Guan T, Tang D, Li J, Lu B, Cui S, Tang H. Differentiation of small (≤ 3 cm) hepatocellular carcinomas from benign nodules in cirrhotic liver: the added additive value of MRI-based radiomics analysis to LI-RADS version 2018 algorithm. BMC Gastroenterol 2021;21:155. [PMID: 33827440 DOI: 10.1186/s12876-021-01710-y] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
8 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]
9 Liu L, Lu F, Pang P, Shao G. Can computed tomography-based radiomics potentially discriminate between anterior mediastinal cysts and type B1 and B2 thymomas? Biomed Eng Online 2020;19:89. [PMID: 33246468 DOI: 10.1186/s12938-020-00833-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
10 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: 9] [Cited by in F6Publishing: 10] [Article Influence: 3.0] [Reference Citation Analysis]
11 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]
12 Tomita H, Kuno H, Sekiya K, Otani K, Sakai O, Li B, Hiyama T, Nomura K, Mimura H, Kobayashi T. Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions. Int J Endocrinol 2020;2020:5484671. [PMID: 32256574 DOI: 10.1155/2020/5484671] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 1.7] [Reference Citation Analysis]
13 Oda M, Staziaki PV, Qureshi MM, Andreu-Arasa VC, Li B, Takumi K, Chapman MN, Wang A, Salama AR, Sakai O. Using CT texture analysis to differentiate cystic and cystic-appearing odontogenic lesions. Eur J Radiol 2019;120:108654. [PMID: 31539792 DOI: 10.1016/j.ejrad.2019.108654] [Cited by in Crossref: 10] [Cited by in F6Publishing: 13] [Article Influence: 2.5] [Reference Citation Analysis]
14 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]
15 Zhou Z, Liu L, Xue K, Ma Y, Liu J, Zhang M. Assessment of Pathological Grading of Bladder Cancer Using Texture Features from MRI. 2019 IEEE International Conference on Mechatronics and Automation (ICMA) 2019. [DOI: 10.1109/icma.2019.8816242] [Reference Citation Analysis]
16 Kromrey ML, Ittermann T, Berning M, Kolb C, Hoffmann RT, Lerch MM, Völzke H, Kühn JP. Accuracy of ultrasonography in the assessment of liver fat compared with MRI. Clin Radiol 2019;74:539-46. [PMID: 30955836 DOI: 10.1016/j.crad.2019.02.014] [Cited by in Crossref: 20] [Cited by in F6Publishing: 15] [Article Influence: 5.0] [Reference Citation Analysis]
17 Kuno H, Garg N, Qureshi MM, Chapman MN, Li B, Meibom SK, Truong MT, Takumi K, Sakai O. CT Texture Analysis of Cervical Lymph Nodes on Contrast-Enhanced [18F] FDG-PET/CT Images to Differentiate Nodal Metastases from Reactive Lymphadenopathy in HIV-Positive Patients with Head and Neck Squamous Cell Carcinoma. AJNR Am J Neuroradiol 2019;40:543-50. [PMID: 30792253 DOI: 10.3174/ajnr.A5974] [Cited by in Crossref: 6] [Cited by in F6Publishing: 11] [Article Influence: 1.5] [Reference Citation Analysis]
18 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]
19 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: 8] [Cited by in F6Publishing: 8] [Article Influence: 1.6] [Reference Citation Analysis]
20 Mathew RP, Venkatesh SK. Imaging of Hepatic Fibrosis. Curr Gastroenterol Rep 2018;20. [DOI: 10.1007/s11894-018-0652-7] [Cited by in Crossref: 24] [Cited by in F6Publishing: 21] [Article Influence: 4.8] [Reference Citation Analysis]
21 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: 24] [Cited by in F6Publishing: 29] [Article Influence: 4.8] [Reference Citation Analysis]
22 El Hamrani D, Chepied A, Même W, Mesnil M, Defamie N, Même S. Gestational and lactational exposure to dichlorinated bisphenol A induces early alterations of hepatic lipid composition in mice. Magn Reson Mater Phy 2018;31:565-76. [DOI: 10.1007/s10334-018-0679-7] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.2] [Reference Citation Analysis]
23 Hu F, Yang R, Huang Z, Wang M, Zhang H, Yan X, Song B. Liver fibrosis: in vivo evaluation using intravoxel incoherent motion-derived histogram metrics with histopathologic findings at 3.0 T. Abdom Radiol (NY) 2017;42:2855-63. [PMID: 28624925 DOI: 10.1007/s00261-017-1208-2] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 2.2] [Reference Citation Analysis]
24 Kuno H, Qureshi MM, Chapman MN, Li B, Andreu-Arasa VC, Onoue K, Truong MT, Sakai O. CT Texture Analysis Potentially Predicts Local Failure in Head and Neck Squamous Cell Carcinoma Treated with Chemoradiotherapy. AJNR Am J Neuroradiol 2017;38:2334-40. [PMID: 29025727 DOI: 10.3174/ajnr.A5407] [Cited by in Crossref: 49] [Cited by in F6Publishing: 54] [Article Influence: 8.2] [Reference Citation Analysis]
25 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: 94] [Cited by in F6Publishing: 76] [Article Influence: 15.7] [Reference Citation Analysis]
26 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;42:2037-53. [PMID: 28624924 DOI: 10.1007/s00261-017-1211-7] [Cited by in Crossref: 77] [Cited by in F6Publishing: 80] [Article Influence: 12.8] [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: 37] [Cited by in F6Publishing: 40] [Article Influence: 6.2] [Reference Citation Analysis]
28 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: 117] [Cited by in F6Publishing: 129] [Article Influence: 16.7] [Reference Citation Analysis]
29 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: 1.0] [Reference Citation Analysis]
30 Molina D, Pérez-Beteta J, Martínez-González A, Martino J, Velásquez C, Arana E, Pérez-García VM. Influence of gray level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images. Comput Biol Med 2016;78:49-57. [PMID: 27658261 DOI: 10.1016/j.compbiomed.2016.09.011] [Cited by in Crossref: 35] [Cited by in F6Publishing: 37] [Article Influence: 5.0] [Reference Citation Analysis]
31 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: 19] [Cited by in F6Publishing: 19] [Article Influence: 2.7] [Reference Citation Analysis]
32 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]