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Cited by in F6Publishing
For: Frøkjær JB, Lisitskaya MV, Jørgensen AS, Østergaard LR, Hansen TM, Drewes AM, Olesen SS. Pancreatic magnetic resonance imaging texture analysis in chronic pancreatitis: a feasibility and validation study. Abdom Radiol (NY) 2020;45:1497-506. [PMID: 32266506 DOI: 10.1007/s00261-020-02512-8] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Zhong J, Hu Y, Xing Y, Ge X, Ding D, Zhang H, Yao W. A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability. Insights Imaging 2022;13. [DOI: 10.1186/s13244-022-01279-4] [Reference Citation Analysis]
2 Yan G, Yan G, Li H, Liang H, Peng C, Bhetuwal A, Mcclure MA, Li Y, Yang G, Li Y, Zhao L, Fan X. Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review. Front Med 2022;9:922299. [DOI: 10.3389/fmed.2022.922299] [Reference Citation Analysis]
3 Abunahel BM, Pontre B, Ko J, Petrov MS. Towards developing a robust radiomics signature in diffuse diseases of the pancreas: Accuracy and stability of features derived from T1-weighted magnetic resonance imaging. Journal of Medical Imaging and Radiation Sciences 2022. [DOI: 10.1016/j.jmir.2022.04.002] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
4 Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2021. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
5 Chen PT, Chang D, Wu T, Wu MS, Wang W, Liao WC. Applications of artificial intelligence in pancreatic and biliary diseases. J Gastroenterol Hepatol 2021;36:286-94. [PMID: 33624891 DOI: 10.1111/jgh.15380] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
6 Zheng J, Yu H, Batur J, Shi Z, Tuerxun A, Abulajiang A, Lu S, Kong J, Huang L, Wu S, Wu Z, Qiu Y, Lin T, Zou X. A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning. Kidney Int 2021:S0085-2538(21)00587-1. [PMID: 34129883 DOI: 10.1016/j.kint.2021.05.031] [Cited by in F6Publishing: 6] [Reference Citation Analysis]
7 Chu LC, Park S, Kawamoto S, Yuille AL, Hruban RH, Fishman EK. Pancreatic Cancer Imaging: A New Look at an Old Problem. Curr Probl Diagn Radiol 2021;50:540-50. [PMID: 32988674 DOI: 10.1067/j.cpradiol.2020.08.002] [Cited by in Crossref: 3] [Cited by in F6Publishing: 7] [Article Influence: 1.5] [Reference Citation Analysis]