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Cited by in F6Publishing
For: 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: 37] [Cited by in F6Publishing: 35] [Article Influence: 9.3] [Reference Citation Analysis]
Number Citing Articles
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2 Zhu X. Editorial for "Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T1-Weighted Imaging: Comparison of Different Radiomics Models". J Magn Reson Imaging 2021;53:1090-1. [PMID: 33135268 DOI: 10.1002/jmri.27425] [Reference Citation Analysis]
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7 Nitsch J, Sack J, Halle MW, Moltz JH, Wall A, Rutherford AE, Kikinis R, Meine H. MRI-based radiomic feature analysis of end-stage liver disease for severity stratification. Int J Comput Assist Radiol Surg 2021;16:457-66. [PMID: 33646521 DOI: 10.1007/s11548-020-02295-9] [Reference Citation Analysis]
8 Wei J, Jiang H, Gu D, Niu M, Fu F, Han Y, Song B, Tian J. Radiomics in liver diseases: Current progress and future opportunities. Liver Int 2020;40:2050-63. [PMID: 32515148 DOI: 10.1111/liv.14555] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
9 Kim TH, Jeong CW, Kim JE, Kim JW, Jo HG, Kim YR, Lee YH, Yoon KH. Assessment of Liver Fibrosis Stage Using Integrative Analysis of Hepatic Heterogeneity and Nodularity in Routine MRI with FIB-4 Index as Reference Standard. J Clin Med 2021;10:1697. [PMID: 33920804 DOI: 10.3390/jcm10081697] [Reference Citation Analysis]
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11 Chen G, Jiang J, Wang X, Yang M, Xie Y, Guo H, Tang H, Zhou L, Hu D, Kamel IR, Chen Z, Li Z. Evaluation of hepatic steatosis before liver transplantation in ex vivo by volumetric quantitative PDFF-MRI. Magn Reson Med 2021;85:2805-14. [PMID: 33197060 DOI: 10.1002/mrm.28592] [Reference Citation Analysis]
12 Amorim VB, Parente DB, Paiva FF, Oliveira Neto JA, Miranda AA, Moreira CC, Fernandes FF, Campos CFF, Leite NC, Perez RM, Rodrigues RS. Can gadoxetic acid–enhanced magnetic resonance imaging be used to avoid liver biopsy in patients with nonalcoholic fatty liver disease? World J Hepatol 2020; 12(9): 661-671 [PMID: 33033571 DOI: 10.4254/wjh.v12.i9.661] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
13 Nowak S, Mesropyan N, Faron A, Block W, Reuter M, Attenberger UI, Luetkens JA, Sprinkart AM. Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning. Eur Radiol 2021. [PMID: 33974149 DOI: 10.1007/s00330-021-07858-1] [Reference Citation Analysis]
14 Yang Q, Wei J, Hao X, Kong D, Yu X, Jiang T, Xi J, Cai W, Luo Y, Jing X, Yang Y, Cheng Z, Wu J, Zhang H, Liao J, Zhou P, Song Y, Zhang Y, Han Z, Cheng W, Tang L, Liu F, Dou J, Zheng R, Yu J, Tian J, Liang P. Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study. EBioMedicine. 2020;56:102777. [PMID: 32485640 DOI: 10.1016/j.ebiom.2020.102777] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 3.5] [Reference Citation Analysis]
15 Vernuccio F, Cannella R, Bartolotta TV, Galia M, Tang A, Brancatelli G. Advances in liver US, CT, and MRI: moving toward the future. Eur Radiol Exp 2021;5:52. [PMID: 34873633 DOI: 10.1186/s41747-021-00250-0] [Reference Citation Analysis]
16 Moura Cunha G, Navin PJ, Fowler KJ, Venkatesh SK, Ehman RL, Sirlin CB. Quantitative magnetic resonance imaging for chronic liver disease. Br J Radiol 2021;94:20201377. [PMID: 33635729 DOI: 10.1259/bjr.20201377] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Zhang L, Xing R, Huang Z, Ding L, Zhang L, Li M, Li X, Wang P, Mao J. Synovial Fibrosis Involvement in Osteoarthritis. Front Med (Lausanne) 2021;8:684389. [PMID: 34124114 DOI: 10.3389/fmed.2021.684389] [Reference Citation Analysis]
18 Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol 2021;36:569-80. [PMID: 33709606 DOI: 10.1111/jgh.15415] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
19 Li X, Liang D, Meng J, Zhou J, Chen Z, Huang S, Lu B, Qiu Y, Baker ME, Ye Z, Cao Q, Wang M, Yuan C, Chen Z, Feng S, Zhang Y, Iacucci M, Ghosh S, Rieder F, Sun C, Chen M, Li Z, Mao R, Huang B, Feng ST. Development and Validation of a Novel Computed-Tomography Enterography Radiomic Approach for Characterization of Intestinal Fibrosis in Crohn's Disease. Gastroenterology 2021;160:2303-2316.e11. [PMID: 33609503 DOI: 10.1053/j.gastro.2021.02.027] [Reference Citation Analysis]
20 Choi B, Choi IY, Cha SH, Yeom SK, Chung HH, Lee SH, Cha J, Lee JH. Feasibility of computed tomography texture analysis of hepatic fibrosis using dual-energy spectral detector computed tomography. Jpn J Radiol 2020;38:1179-89. [PMID: 32666182 DOI: 10.1007/s11604-020-01020-5] [Reference Citation Analysis]
21 Bari H, Wadhwani S, Dasari BVM. Role of artificial intelligence in hepatobiliary and pancreatic surgery. World J Gastrointest Surg 2021; 13(1): 7-18 [PMID: 33552391 DOI: 10.4240/wjgs.v13.i1.7] [Cited by in CrossRef: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
22 Cui E, Long W, Wu J, Li Q, Ma C, Lei Y, Lin F. Predicting the stages of liver fibrosis with multiphase CT radiomics based on volumetric features. Abdom Radiol (NY) 2021;46:3866-76. [PMID: 33751193 DOI: 10.1007/s00261-021-03051-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
23 Chen ZW, Tang K, Zhao YF, Chen YZ, Tang LJ, Li G, Huang OY, Wang XD, Targher G, Byrne CD, Zheng XW, Zheng MH. Radiomics based on fluoro-deoxyglucose positron emission tomography predicts liver fibrosis in biopsy-proven MAFLD: a pilot study. Int J Med Sci 2021;18:3624-30. [PMID: 34790034 DOI: 10.7150/ijms.64458] [Reference Citation Analysis]
24 Hu P, Hu X, Lin Y, Yu X, Tao X, Sun J, Wu X. A Combination Model of Radiomics Features and Clinical Biomarkers as a Nomogram to Differentiate Nonadvanced From Advanced Liver Fibrosis: A Retrospective Study. Acad Radiol 2021:S1076-6332(20)30510-9. [PMID: 34023199 DOI: 10.1016/j.acra.2020.08.029] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
25 Crombé A, Kind M, Ray-Coquard I, Isambert N, Chevreau C, André T, Lebbe C, Cesne AL, Bompas E, Piperno-Neumann S, Saada E, Bouhamama A, Blay JY, Italiano A; French Sarcoma Group. Progressive Desmoid Tumor: Radiomics Compared With Conventional Response Criteria for Predicting Progression During Systemic Therapy-A Multicenter Study by the French Sarcoma Group. AJR Am J Roentgenol 2020;215:1539-48. [PMID: 32991215 DOI: 10.2214/AJR.19.22635] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 4.5] [Reference Citation Analysis]
26 Son J, Lee SE, Kim EK, Kim S. Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis. Sci Rep 2020;10:21566. [PMID: 33299040 DOI: 10.1038/s41598-020-78681-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 5] [Article Influence: 0.5] [Reference Citation Analysis]
27 Cannella R, Taibbi A, Porrello G, Dioguardi Burgio M, Cabibbo G, Bartolotta TV. Hepatocellular carcinoma with macrovascular invasion: multimodality imaging features for the diagnosis. Diagn Interv Radiol 2020;26:531-40. [PMID: 32990243 DOI: 10.5152/dir.2020.19569] [Cited by in Crossref: 4] [Article Influence: 2.0] [Reference Citation Analysis]
28 Elkilany A, Fehrenbach U, Auer TA, Müller T, Schöning W, Hamm B, Geisel D. A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI. Sci Rep 2021;11:10778. [PMID: 34031487 DOI: 10.1038/s41598-021-90257-9] [Reference Citation Analysis]
29 Ma S, Xie H, Wang H, Yang J, Han C, Wang X, Zhang X. Preoperative Prediction of Extracapsular Extension: Radiomics Signature Based on Magnetic Resonance Imaging to Stage Prostate Cancer. Mol Imaging Biol 2020;22:711-21. [PMID: 31321651 DOI: 10.1007/s11307-019-01405-7] [Cited by in Crossref: 8] [Cited by in F6Publishing: 10] [Article Influence: 8.0] [Reference Citation Analysis]
30 Song J, Yu X, Song W, Guo D, Li C, Liu H, Zhang H, Zhou J, Liu Y. MRI ‐Based Radiomics Models Developed With Features of the Whole Liver and Right Liver Lobe: Assessment of Hepatic Inflammatory Activity in Chronic Hepatic Disease. J Magn Reson Imaging 2020;52:1668-78. [DOI: 10.1002/jmri.27197] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
31 Park HJ, Park B, Lee SS. Radiomics and Deep Learning: Hepatic Applications. Korean J Radiol. 2020;21:387-401. [PMID: 32193887 DOI: 10.3348/kjr.2019.0752] [Cited by in Crossref: 17] [Cited by in F6Publishing: 13] [Article Influence: 8.5] [Reference Citation Analysis]
32 Wang JC, Fu R, Tao XW, Mao YF, Wang F, Zhang ZC, Yu WW, Chen J, He J, Sun BC. A radiomics-based model on non-contrast CT for predicting cirrhosis: make the most of image data.Biomark Res. 2020;8:47. [PMID: 32963787 DOI: 10.1186/s40364-020-00219-y] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
33 Zhang Z, Chen J, Jiang H, Wei Y, Zhang X, Cao L, Duan T, Ye Z, Yao S, Pan X, Song B. Gadoxetic acid-enhanced MRI radiomics signature: prediction of clinical outcome in hepatocellular carcinoma after surgical resection. Ann Transl Med 2020;8:870. [PMID: 32793714 DOI: 10.21037/atm-20-3041] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
34 Sung YS, Park B, Park HJ, Lee SS. Radiomics and deep learning in liver diseases. J Gastroenterol Hepatol 2021;36:561-8. [PMID: 33709608 DOI: 10.1111/jgh.15414] [Reference Citation Analysis]
35 Zhou Z, Yang J, Wang S, Li W, Xie L, Li Y, Zhang C. The diagnostic value of a non-contrast computed tomography scan-based radiomics model for acute aortic dissection. Medicine (Baltimore) 2021;100:e26212. [PMID: 34087897 DOI: 10.1097/MD.0000000000026212] [Reference Citation Analysis]
36 Zheng R, Shi C, Wang C, Shi N, Qiu T, Chen W, Shi Y, Wang H. Imaging-Based Staging of Hepatic Fibrosis in Patients with Hepatitis B: A Dynamic Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI. Biomolecules 2021;11:307. [PMID: 33670596 DOI: 10.3390/biom11020307] [Reference Citation Analysis]
37 Costa G, Cavinato L, Masci C, Fiz F, Sollini M, Politi LS, Chiti A, Balzarini L, Aghemo A, di Tommaso L, Ieva F, Torzilli G, Viganò L. Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases. Cancers (Basel) 2021;13:3077. [PMID: 34203103 DOI: 10.3390/cancers13123077] [Reference Citation Analysis]
38 Bian Y, Liu YF, Jiang H, Meng Y, Liu F, Cao K, Zhang H, Fang X, Li J, Yu J, Feng X, Li Q, Wang L, Lu J, Shao C. Machine learning for MRI radiomics: a study predicting tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 2021. [PMID: 34189612 DOI: 10.1007/s00261-021-03159-9] [Reference Citation Analysis]
39 Ma S, Xie H, Wang H, Han C, Yang J, Lin Z, Li Y, He Q, Wang R, Cui Y, Zhang X, Wang X. MRI-Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer. J Magn Reson Imaging 2019;50:1914-25. [PMID: 31062459 DOI: 10.1002/jmri.26777] [Cited by in Crossref: 20] [Cited by in F6Publishing: 19] [Article Influence: 6.7] [Reference Citation Analysis]
40 Hill CE, Biasiolli L, Robson MD, Grau V, Pavlides M. Emerging artificial intelligence applications in liver magnetic resonance imaging. World J Gastroenterol 2021; 27(40): 6825-6843 [PMID: 34790009 DOI: 10.3748/wjg.v27.i40.6825] [Reference Citation Analysis]