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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: 58] [Cited by in F6Publishing: 63] [Article Influence: 11.6] [Reference Citation Analysis]
Number Citing Articles
1 Zha J, Huang S, Xia T, Chen Z, Zheng T, Yu Q, Zhou J, Cao P, Wang Y, Tang T, Song Y, Xu J, Song B, Liu Y, Ju S. A fully automated hybrid approach to assessing liver fibrosis and necroinflammation on conventional MRI: A multi-center cohort Study.. [DOI: 10.21203/rs.3.rs-2475668/v1] [Reference Citation Analysis]
2 Leng Y, Wang X, Zheng T, Peng F, Xiong L, Wang Y, Gong L. Radiomics based on enhanced CT for the preoperative prediction of metastasis in epithelial ovarian cancer.. [DOI: 10.21203/rs.3.rs-2490195/v1] [Reference Citation Analysis]
3 Zhang WY, Sun HY, Zhang WL, Feng R. Effect of type 2 diabetes on liver images of GD-EOB-DTPA-enhanced MRI during the hepatobiliary phase. Sci Rep 2023;13:543. [PMID: 36631556 DOI: 10.1038/s41598-023-27730-0] [Reference Citation Analysis]
4 Kalapala R, Rughwani H, Reddy DN. Artificial Intelligence in Hepatology- Ready for the Primetime. J Clin Exp Hepatol 2023;13:149-61. [PMID: 36647407 DOI: 10.1016/j.jceh.2022.06.009] [Reference Citation Analysis]
5 Yang Y, Zhang X, Zhao L, Mao H, Cai TN, Guo WL. Development of an MRI-Based Radiomics-Clinical Model to Diagnose Liver Fibrosis Secondary to Pancreaticobiliary Maljunction in Children. J Magn Reson Imaging 2022. [PMID: 36583731 DOI: 10.1002/jmri.28586] [Reference Citation Analysis]
6 Zerunian M, Pucciarelli F, Masci B, Siciliano F, Polici M, Bracci B, Guido G, Polidori T, De Santis D, Laghi A, Caruso D. Updates on Quantitative MRI of Diffuse Liver Disease: A Narrative Review. Biomed Res Int 2022;2022:1147111. [PMID: 36619303 DOI: 10.1155/2022/1147111] [Reference Citation Analysis]
7 Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022;15. [PMID: 36612061 DOI: 10.3390/cancers15010063] [Reference Citation Analysis]
8 Ge X, Lan Z, Lan Q, Lin H, Wang G, Chen J. Diagnostic accuracy of ultrasound-based multimodal radiomics modeling for fibrosis detection in chronic kidney disease. Eur Radiol 2022. [DOI: 10.1007/s00330-022-09268-3] [Reference Citation Analysis]
9 Sim KC, Kim MJ, Cho Y, Kim HJ, Park BJ, Sung DJ, Han NY, Han YE, Kim TH, Lee YJ. Radiomics Analysis of Magnetic Resonance Proton Density Fat Fraction for the Diagnosis of Hepatic Steatosis in Patients With Suspected Non-Alcoholic Fatty Liver Disease. J Korean Med Sci 2022;37:e339. [PMID: 36536543 DOI: 10.3346/jkms.2022.37.e339] [Reference Citation Analysis]
10 Sack J, Nitsch J, Meine H, Kikinis R, Halle M, Rutherford A. Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen. J Imaging 2022;8:277. [DOI: 10.3390/jimaging8100277] [Reference Citation Analysis]
11 Zhang D, Cao Y, Sun Y, Zhao X, Peng C, Zhao J, Bao X, Wang L, Zhang C. Radiomics nomograms based on R2* mapping and clinical biomarkers for staging of liver fibrosis in patients with chronic hepatitis B: a single-center retrospective study. Eur Radiol 2022. [PMID: 36149481 DOI: 10.1007/s00330-022-09137-z] [Reference Citation Analysis]
12 Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2022;47:2986-3002. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Cited by in Crossref: 13] [Cited by in F6Publishing: 12] [Article Influence: 13.0] [Reference Citation Analysis]
13 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]
14 Wang L, Zhang L, Jiang B, Zhao K, Zhang Y, Xie X. Clinical application of deep learning and radiomics in hepatic disease imaging: a systematic scoping review. Br J Radiol 2022;95:20211136. [PMID: 35816550 DOI: 10.1259/bjr.20211136] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
15 Meng Y, Yu J, Zhu M, Zhou J, Li N, Liu F, Zhang H, Fang X, Li J, Feng X, Wang L, Jiang H, Lu J, Shao C, Bian Y. CT radiomics signature: a potential biomarker for fibroblast activation protein expression in patients with pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 2022;47:2822-34. [PMID: 35451626 DOI: 10.1007/s00261-022-03512-6] [Reference Citation Analysis]
16 Bao J, Feng X, Ma Y, Wang Y, Qi J, Qin C, Tan X, Tian Y. The latest application progress of radiomics in prediction and diagnosis of liver diseases. Expert Rev Gastroenterol Hepatol 2022;:1-13. [PMID: 35880549 DOI: 10.1080/17474124.2022.2104711] [Reference Citation Analysis]
17 Zheng W, Guo W, Xiong M, Chen X, Gao L, Song Y, Cao D. Clinic-radiological features and radiomics signatures based on Gd-BOPTA-enhanced MRI for predicting advanced liver fibrosis. Eur Radiol. [DOI: 10.1007/s00330-022-08992-0] [Reference Citation Analysis]
18 Duan YY, Qin J, Qiu WQ, Li SY, Li C, Liu AS, Chen X, Zhang CX. Performance of a generative adversarial network using ultrasound images to stage liver fibrosis and predict cirrhosis based on a deep-learning radiomics nomogram. Clin Radiol 2022:S0009-9260(22)00286-0. [PMID: 35811157 DOI: 10.1016/j.crad.2022.06.003] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
19 Zhao R, Zhao H, Ge Y, Zhou F, Wang L, Yu H, Gong X, Granito A. Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis. Canadian Journal of Gastroenterology and Hepatology 2022;2022:1-9. [DOI: 10.1155/2022/2249447] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Ren Q, Zhu P, Li C, Yan M, Liu S, Zheng C, Xia X. Pretreatment Computed Tomography-Based Machine Learning Models to Predict Outcomes in Hepatocellular Carcinoma Patients who Received Combined Treatment of Trans-Arterial Chemoembolization and Tyrosine Kinase Inhibitor. Front Bioeng Biotechnol 2022;10:872044. [DOI: 10.3389/fbioe.2022.872044] [Reference Citation Analysis]
21 Sim KC, Kim MJ, Cho Y, Kim HJ, Park BJ, Sung DJ, Han YE, Han NY, Kim TH, Lee YJ. Diagnostic Feasibility of Magnetic Resonance Elastography Radiomics Analysis for the Assessment of Hepatic Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. J Comput Assist Tomogr 2022. [PMID: 35483092 DOI: 10.1097/RCT.0000000000001308] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
22 Cheng S, Yu X, Chen X, Jin Z, Xue H, Wang Z, Xie P. CT-based radiomics model for preoperative prediction of hepatic encephalopathy after transjugular intrahepatic portosystemic shunt. Br J Radiol 2022;95:20210792. [PMID: 35019776 DOI: 10.1259/bjr.20210792] [Reference Citation Analysis]
23 Wang J, Tang S, Mao Y, Wu J, Xu S, Yue Q, Chen J, He J, Yin Y. Radiomics analysis of contrast-enhanced CT for staging liver fibrosis: an update for image biomarker. Hepatol Int. [DOI: 10.1007/s12072-022-10326-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
24 Shen P, Huang W, Dai Y, Lo C, Yang J, Su Y, Wang Y, Lu C, Lin C. Radiomics-Based Predictive Model of Radiation-Induced Liver Disease in Hepatocellular Carcinoma Patients Receiving Stereo-Tactic Body Radiotherapy. Biomedicines 2022;10:597. [DOI: 10.3390/biomedicines10030597] [Reference Citation Analysis]
25 Beleù A, Autelitano D, Geraci L, Aluffi G, Cardobi N, De Robertis R, Martone E, Conci S, Ruzzenente A, D'onofrio M. Radiofrequency ablation of hepatocellular carcinoma: CT texture analysis of the ablated area to predict local recurrence. European Journal of Radiology 2022. [DOI: 10.1016/j.ejrad.2022.110250] [Reference Citation Analysis]
26 Yin Y, Yakar D, Dierckx RAJO, Mouridsen KB, Kwee TC, de Haas RJ. Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging. Diagnostics (Basel) 2022;12:550. [PMID: 35204639 DOI: 10.3390/diagnostics12020550] [Reference Citation Analysis]
27 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]
28 Ma W, Chen C, Zheng S, Qin J, Zhang H, Dou Q. Test-Time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift. Lecture Notes in Computer Science 2022. [DOI: 10.1007/978-3-031-16437-8_30] [Reference Citation Analysis]
29 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] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
30 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] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
31 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;28 Suppl 1:S45-54. [PMID: 34023199 DOI: 10.1016/j.acra.2020.08.029] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
32 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] [Cited by in CrossRef: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
33 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;46:4800-16. [PMID: 34189612 DOI: 10.1007/s00261-021-03159-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
34 Sabottke CF, Spieler BM, Moawad AW, Elsayes KM. Artificial Intelligence in Imaging of Chronic Liver Diseases: Current Update and Future Perspectives. Magn Reson Imaging Clin N Am 2021;29:451-63. [PMID: 34243929 DOI: 10.1016/j.mric.2021.05.011] [Reference Citation Analysis]
35 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: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
36 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] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
37 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]
38 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: 15] [Cited by in F6Publishing: 19] [Article Influence: 7.5] [Reference Citation Analysis]
39 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] [Cited by in Crossref: 22] [Cited by in F6Publishing: 22] [Article Influence: 11.0] [Reference Citation Analysis]
40 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] [Cited by in Crossref: 8] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
41 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] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
42 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: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
43 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] [Cited by in Crossref: 8] [Cited by in F6Publishing: 11] [Article Influence: 4.0] [Reference Citation Analysis]
44 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] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
45 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] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]
46 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: 19] [Cited by in F6Publishing: 17] [Article Influence: 9.5] [Reference Citation Analysis]
47 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] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
48 Lee HW, Sung JJY, Ahn SH. Artificial intelligence in liver disease. J Gastroenterol Hepatol 2021;36:539-42. [PMID: 33709605 DOI: 10.1111/jgh.15409] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
49 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: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
50 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] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
51 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: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
52 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: 12] [Cited by in F6Publishing: 14] [Article Influence: 4.0] [Reference Citation Analysis]
53 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] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
54 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]
55 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: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
56 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: 14] [Cited by in F6Publishing: 14] [Article Influence: 4.7] [Reference Citation Analysis]
57 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: 0.3] [Reference Citation Analysis]
58 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: 42] [Cited by in F6Publishing: 42] [Article Influence: 14.0] [Reference Citation Analysis]
59 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: 14] [Cited by in F6Publishing: 15] [Article Influence: 4.7] [Reference Citation Analysis]
60 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: 42] [Cited by in F6Publishing: 44] [Article Influence: 14.0] [Reference Citation Analysis]
61 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] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
62 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: 13] [Cited by in F6Publishing: 13] [Article Influence: 4.3] [Reference Citation Analysis]
63 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: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
64 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: 20] [Cited by in F6Publishing: 23] [Article Influence: 6.7] [Reference Citation Analysis]
65 Dreher C, Linde P, Boda-Heggemann J, Baessler B. Radiomics for liver tumours. Strahlenther Onkol 2020;196:888-99. [PMID: 32296901 DOI: 10.1007/s00066-020-01615-x] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 2.7] [Reference Citation Analysis]
66 Yang D, Li D, Li J, Yang Z, Wang Z. Systematic review: The diagnostic efficacy of gadoxetic acid-enhanced MRI for liver fibrosis staging. Eur J Radiol 2020;125:108857. [PMID: 32113153 DOI: 10.1016/j.ejrad.2020.108857] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
67 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: 28] [Cited by in F6Publishing: 30] [Article Influence: 7.0] [Reference Citation Analysis]