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Cited by in F6Publishing
For: Sun X, He B, Luo X, Li Y, Cao J, Wang J, Dong J, Sun X, Zhang G. Preliminary Study on Molecular Subtypes of Breast Cancer Based on Magnetic Resonance Imaging Texture Analysis. J Comput Assist Tomogr 2018;42:531-5. [PMID: 29659431 DOI: 10.1097/RCT.0000000000000738] [Cited by in Crossref: 18] [Cited by in F6Publishing: 18] [Article Influence: 4.5] [Reference Citation Analysis]
Number Citing Articles
1 Pinker K, Gullo RL, Eskreis-winkler S, Bitencourt A, Gibbs P, Thakur SB. Artificial Intelligence—Enhanced Breast MRI and DWI: Current Status and Future Applications. Diffusion MRI of the Breast 2023. [DOI: 10.1016/b978-0-323-79702-3.00010-1] [Reference Citation Analysis]
2 Mendez AM, Fang LK, Meriwether CH, Batasin SJ, Loubrie S, Rodríguez-soto AE, Rakow-penner RA. Diffusion Breast MRI: Current Standard and Emerging Techniques. Front Oncol 2022;12:844790. [DOI: 10.3389/fonc.2022.844790] [Reference Citation Analysis]
3 Xu A, Chu X, Zhang S, Zheng J, Shi D, Lv S, Li F, Weng X. Prediction Breast Molecular Typing of Invasive Ductal Carcinoma Based on Dynamic Contrast Enhancement Magnetic Resonance Imaging Radiomics Characteristics: A Feasibility Study. Front Oncol 2022;12:799232. [DOI: 10.3389/fonc.2022.799232] [Reference Citation Analysis]
4 Davey MG, Davey MS, Boland MR, Ryan ÉJ, Lowery AJ, Kerin MJ. Radiomic differentiation of breast cancer molecular subtypes using pre-operative breast imaging - A systematic review and meta-analysis. Eur J Radiol 2021;144:109996. [PMID: 34624649 DOI: 10.1016/j.ejrad.2021.109996] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 6.0] [Reference Citation Analysis]
5 Bitencourt A, Daimiel Naranjo I, Lo Gullo R, Rossi Saccarelli C, Pinker K. AI-enhanced breast imaging: Where are we and where are we heading? Eur J Radiol 2021;142:109882. [PMID: 34392105 DOI: 10.1016/j.ejrad.2021.109882] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 11.0] [Reference Citation Analysis]
6 Moffa G, Galati F, Collalunga E, Rizzo V, Kripa E, D'Amati G, Pediconi F. Can MRI Biomarkers Predict Triple-Negative Breast Cancer? Diagnostics (Basel) 2020;10:E1090. [PMID: 33333733 DOI: 10.3390/diagnostics10121090] [Cited by in Crossref: 14] [Cited by in F6Publishing: 15] [Article Influence: 7.0] [Reference Citation Analysis]
7 Ye DM, Wang HT, Yu T. The Application of Radiomics in Breast MRI: A Review. Technol Cancer Res Treat 2020;19:1533033820916191. [PMID: 32347167 DOI: 10.1177/1533033820916191] [Cited by in Crossref: 25] [Cited by in F6Publishing: 26] [Article Influence: 12.5] [Reference Citation Analysis]
8 Vasileiou G, Costa MJ, Long C, Wetzler IR, Hoyer J, Kraus C, Popp B, Emons J, Wunderle M, Wenkel E, Uder M, Beckmann MW, Jud SM, Fasching PA, Cavallaro A, Reis A, Hammon M. Breast MRI texture analysis for prediction of BRCA-associated genetic risk. BMC Med Imaging 2020;20:86. [PMID: 32727387 DOI: 10.1186/s12880-020-00483-2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
9 Jiang N, Zhong L, Zhang C, Luo X, Zhong P, Li X. Value of Conventional MRI Texture Analysis in the Differential Diagnosis of Phyllodes Tumors and Fibroadenomas of the Breast. Breast Care (Basel) 2021;16:283-90. [PMID: 34248470 DOI: 10.1159/000508456] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
10 Song L, Lu H, Yin J. Preliminary study on discriminating HER2 2+ amplification status of breast cancers based on texture features semi-automatically derived from pre-, post-contrast, and subtraction images of DCE-MRI. PLoS One 2020;15:e0234800. [PMID: 32555662 DOI: 10.1371/journal.pone.0234800] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
11 Leithner D, Mayerhoefer ME, Martinez DF, Jochelson MS, Morris EA, Thakur SB, Pinker K. Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics. J Clin Med 2020;9:E1853. [PMID: 32545851 DOI: 10.3390/jcm9061853] [Cited by in Crossref: 25] [Cited by in F6Publishing: 28] [Article Influence: 12.5] [Reference Citation Analysis]
12 Song L, Jiang Z, Lu H, Yin J. Performance of a semi-automatic machine leaning method for discriminating HER2 2+ status of breast cancers based on DCE-MRI (Preprint).. [DOI: 10.2196/preprints.16226] [Reference Citation Analysis]
13 Meyer HJ, Hamerla G, Höhn AK, Surov A. Whole Lesion Histogram Analysis Derived From Morphological MRI Sequences Might be Able to Predict EGFR- and Her2-Expression in Cervical Cancer. Acad Radiol 2019;26:e208-15. [PMID: 30318289 DOI: 10.1016/j.acra.2018.09.008] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 1.7] [Reference Citation Analysis]
14 Buch K, Kuno H, Qureshi MM, Li B, Sakai O. Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model. J Appl Clin Med Phys 2018;19:253-64. [PMID: 30369010 DOI: 10.1002/acm2.12482] [Cited by in Crossref: 36] [Cited by in F6Publishing: 39] [Article Influence: 9.0] [Reference Citation Analysis]