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For: Wang K, Lu X, Zhou H, Gao Y, Zheng J, Tong M, Wu C, Liu C, Huang L, Jiang T, Meng F, Lu Y, Ai H, Xie XY, Yin LP, Liang P, Tian J, Zheng R. Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut. 2019;68:729-741. [PMID: 29730602 DOI: 10.1136/gutjnl-2018-316204] [Cited by in Crossref: 130] [Cited by in F6Publishing: 125] [Article Influence: 32.5] [Reference Citation Analysis]
Number Citing Articles
1 Hu C, Anjur V, Saboo K, Reddy KR, O'leary J, Tandon P, Wong F, Garcia-tsao G, Kamath PS, Lai JC, Biggins SW, Fallon MB, Thuluvath P, Subramanian RM, Maliakkal B, Vargas H, Thacker LR, Iyer RK, Bajaj JS. Low Predictability of Readmissions and Death Using Machine Learning in Cirrhosis. Am J Gastroenterol 2021;116:336-46. [DOI: 10.14309/ajg.0000000000000971] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
2 Liu Q, Nie H, Xu H, Wang P, Lei P. Noninvasive Imaging for the Evaluation of Non-Alcoholic Fatty Liver Disease Spectrum. Korean J Radiol 2021;22:155-8. [PMID: 32767866 DOI: 10.3348/kjr.2020.0492] [Reference Citation Analysis]
3 Lupsor-Platon M, Serban T, Silion AI, Tirpe A, Florea M. Hepatocellular Carcinoma and Non-Alcoholic Fatty Liver Disease: A Step Forward for Better Evaluation Using Ultrasound Elastography. Cancers (Basel) 2020;12:E2778. [PMID: 32998257 DOI: 10.3390/cancers12102778] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
4 Soroida Y, Nakatsuka T, Sato M, Nakagawa H, Tanaka M, Yamauchi N, Wake T, Nakagomi R, Kinoshita MN, Minami T, Uchino K, Enooku K, Asaoka Y, Tanaka Y, Endo M, Nakamura A, Kobayashi T, Kurihara M, Hikita H, Sato M, Gotoh H, Iwai T, Fukayama M, Ikeda H, Tateishi R, Yatomi Y, Koike K. A Novel Non-invasive Method for Predicting Liver Fibrosis by Quantifying the Hepatic Vein Waveform. Ultrasound in Medicine & Biology 2019;45:2363-71. [DOI: 10.1016/j.ultrasmedbio.2019.05.028] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
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6 Ahn JC, Connell A, Simonetto DA, Hughes C, Shah VH. Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases. Hepatology 2021;73:2546-63. [PMID: 33098140 DOI: 10.1002/hep.31603] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
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8 Anteby R, Klang E, Horesh N, Nachmany I, Shimon O, Barash Y, Kopylov U, Soffer S. Deep learning for noninvasive liver fibrosis classification: A systematic review. Liver Int 2021. [PMID: 34008300 DOI: 10.1111/liv.14966] [Reference Citation Analysis]
9 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]
10 Xing X, Yan Y, Shen Y, Xue M, Wang X, Luo X, Yang L. Liver fibrosis with two-dimensional shear-wave elastography in patients with autoimmune hepatitis. Expert Rev Gastroenterol Hepatol 2020;14:631-8. [PMID: 32510248 DOI: 10.1080/17474124.2020.1779589] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Zhou W, Yang Y, Yu C, Liu J, Duan X, Weng Z, Chen D, Liang Q, Fang Q, Zhou J, Ju H, Luo Z, Guo W, Ma X, Xie X, Wang R, Zhou L. Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images. Nat Commun 2021;12:1259. [PMID: 33627641 DOI: 10.1038/s41467-021-21466-z] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
12 Boldrini L, Corradini S, Gani C, Henke L, Hosni A, Romano A, Dawson L. MR-Guided Radiotherapy for Liver Malignancies. Front Oncol 2021;11:616027. [PMID: 33869001 DOI: 10.3389/fonc.2021.616027] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
13 Nishida N, Yamakawa M, Shiina T, Kudo M. Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology. Hepatol Int 2019;13:416-21. [PMID: 30790230 DOI: 10.1007/s12072-019-09937-4] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
14 Zhou B, Yang X, Curran WJ, Liu T. Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey. J Ultrasound Med 2021. [PMID: 34467542 DOI: 10.1002/jum.15819] [Reference Citation Analysis]
15 Zheng X, Yao Z, Huang Y, Yu Y, Wang Y, Liu Y, Mao R, Li F, Xiao Y, Wang Y, Hu Y, Yu J, Zhou J. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun 2020;11:1236. [PMID: 32144248 DOI: 10.1038/s41467-020-15027-z] [Cited by in Crossref: 31] [Cited by in F6Publishing: 40] [Article Influence: 15.5] [Reference Citation Analysis]
16 Wei W, Yang X. Utility of convolutional neural network-based algorithm in medical images for liver fibrosis assessment. Chin Med J (Engl) 2021;134:2255-7. [PMID: 34553704 DOI: 10.1097/CM9.0000000000001536] [Reference Citation Analysis]
17 Li G, Cao Y. Backward Mach cone of shear waves induced by a moving force in soft anisotropic materials. Journal of the Mechanics and Physics of Solids 2020;138:103896. [DOI: 10.1016/j.jmps.2020.103896] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
18 Li M, Li W, Zhao L. Ultrasound Elastography under Deep Learning Algorithm to Analyze the Therapeutic Effect of Clustered Regularly Interspaced Short Palindromic Repeats Short Hairpin Ribonucleic Acid Nanoparticles on Cervical Cancer. J Healthc Eng 2021;2021:7538984. [PMID: 34880980 DOI: 10.1155/2021/7538984] [Reference Citation Analysis]
19 Dong Y, Wang QM, Li Q, Li LY, Zhang Q, Yao Z, Dai M, Yu J, Wang WP. Preoperative Prediction of Microvascular Invasion of Hepatocellular Carcinoma: Radiomics Algorithm Based on Ultrasound Original Radio Frequency Signals. Front Oncol 2019;9:1203. [PMID: 31799183 DOI: 10.3389/fonc.2019.01203] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
20 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]
21 Vasconcelos L, Kijanka P, Urban MW. Viscoelastic parameter estimation using simulated shear wave motion and convolutional neural networks. Comput Biol Med 2021;133:104382. [PMID: 33872971 DOI: 10.1016/j.compbiomed.2021.104382] [Reference Citation Analysis]
22 Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, Treeprasertsuk S. Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis. BMC Gastroenterol 2021;21:10. [PMID: 33407169 DOI: 10.1186/s12876-020-01585-5] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
23 Wang S, Shi J, Ye Z, Dong D, Yu D, Zhou M, Liu Y, Gevaert O, Wang K, Zhu Y, Zhou H, Liu Z, Tian J. Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. Eur Respir J 2019;53:1800986. [PMID: 30635290 DOI: 10.1183/13993003.00986-2018] [Cited by in Crossref: 101] [Cited by in F6Publishing: 76] [Article Influence: 33.7] [Reference Citation Analysis]
24 Zhou J, Tan H, Li W, Liu Z, Wu Y, Bai Y, Fu F, Jia X, Feng A, Liu H, Wang M. Radiomics Signatures Based on Multiparametric MRI for the Preoperative Prediction of the HER2 Status of Patients with Breast Cancer. Acad Radiol 2021;28:1352-60. [PMID: 32709582 DOI: 10.1016/j.acra.2020.05.040] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
25 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]
26 Yu F, Hang J, Deng J, Yang B, Wang J, Ye X, Liu Y. Radiomics features on ultrasound imaging for the prediction of disease-free survival in triple negative breast cancer: a multi-institutional study. Br J Radiol 2021;94:20210188. [PMID: 34478336 DOI: 10.1259/bjr.20210188] [Reference Citation Analysis]
27 Zhou P, Zeng R, Yu L, Feng Y, Chen C, Li F, Liu Y, Huang Y, Huang Z; Alzheimer's Disease Neuroimaging Initiative. Deep-Learning Radiomics for Discrimination Conversion of Alzheimer's Disease in Patients With Mild Cognitive Impairment: A Study Based on 18F-FDG PET Imaging. Front Aging Neurosci 2021;13:764872. [PMID: 34764864 DOI: 10.3389/fnagi.2021.764872] [Reference Citation Analysis]
28 Li L, Han Z, Qiu L, Kang D, Zhan Z, Tu H, Chen J. Evaluation of breast carcinoma regression after preoperative chemotherapy by label‐free multiphoton imaging and image analysis. J Biophotonics 2020;13. [DOI: 10.1002/jbio.201900216] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
29 Gatos I, Tsantis S, Spiliopoulos S, Karnabatidis D, Theotokas I, Zoumpoulis P, Loupas T, Hazle JD, Kagadis GC. Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment. Med Phys. 2019;46:2298-2309. [PMID: 30929260 DOI: 10.1002/mp.13521] [Cited by in Crossref: 18] [Cited by in F6Publishing: 19] [Article Influence: 6.0] [Reference Citation Analysis]
30 Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021;11:1719. [PMID: 34574060 DOI: 10.3390/diagnostics11091719] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
31 Sullivan P, Gupta S, Powers PD, Marya NB. Artificial Intelligence Research and Development for Application in Video Capsule Endoscopy. Gastrointest Endosc Clin N Am 2021;31:387-97. [PMID: 33743933 DOI: 10.1016/j.giec.2020.12.009] [Reference Citation Analysis]
32 Peng J, Kang S, Ning Z, Deng H, Shen J, Xu Y, Zhang J, Zhao W, Li X, Gong W, Huang J, Liu L. Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging. Eur Radiol. 2020;30:413-424. [PMID: 31332558 DOI: 10.1007/s00330-019-06318-1] [Cited by in Crossref: 29] [Cited by in F6Publishing: 22] [Article Influence: 9.7] [Reference Citation Analysis]
33 Yu FH, Wang JX, Ye XH, Deng J, Hang J, Yang B. Ultrasound-based radiomics nomogram: A potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer. Eur J Radiol 2019;119:108658. [PMID: 31521878 DOI: 10.1016/j.ejrad.2019.108658] [Cited by in Crossref: 25] [Cited by in F6Publishing: 24] [Article Influence: 8.3] [Reference Citation Analysis]
34 Liu W, Liu X, Peng M, Chen GQ, Liu PH, Cui XW, Jiang F, Dietrich CF. Artificial intelligence for hepatitis evaluation. World J Gastroenterol 2021; 27(34): 5715-5726 [PMID: 34629796 DOI: 10.3748/wjg.v27.i34.5715] [Reference Citation Analysis]
35 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: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
36 Chen Y, Chen J, Zhang Y, Lin Z, Wang M, Huang L, Huang M, Tang M, Zhou X, Peng Z, Huang B, Feng ST. Preoperative Prediction of Cytokeratin 19 Expression for Hepatocellular Carcinoma with Deep Learning Radiomics Based on Gadoxetic Acid-Enhanced Magnetic Resonance Imaging. J Hepatocell Carcinoma 2021;8:795-808. [PMID: 34327180 DOI: 10.2147/JHC.S313879] [Reference Citation Analysis]
37 Maruyama H, Yamaguchi T, Nagamatsu H, Shiina S. AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound. Diagnostics (Basel) 2021;11:292. [PMID: 33673229 DOI: 10.3390/diagnostics11020292] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
38 Taouli B, Alves FC. Imaging biomarkers of diffuse liver disease: current status. Abdom Radiol (NY) 2020;45:3381-5. [PMID: 32583139 DOI: 10.1007/s00261-020-02619-y] [Reference Citation Analysis]
39 Li W, Lv XZ, Zheng X, Ruan SM, Hu HT, Chen LD, Huang Y, Li X, Zhang CQ, Xie XY, Kuang M, Lu MD, Zhuang BW, Wang W. Machine Learning-Based Ultrasomics Improves the Diagnostic Performance in Differentiating Focal Nodular Hyperplasia and Atypical Hepatocellular Carcinoma. Front Oncol 2021;11:544979. [PMID: 33842303 DOI: 10.3389/fonc.2021.544979] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
40 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] [Reference Citation Analysis]
41 Lauric A, Ludwig CG, Malek AM. Enhanced Radiomics for Prediction of Rupture Status in Cerebral Aneurysms. World Neurosurg 2021:S1878-8750(21)01739-3. [PMID: 34823040 DOI: 10.1016/j.wneu.2021.11.038] [Reference Citation Analysis]
42 Uchida D, Takaki A, Oyama A, Adachi T, Wada N, Onishi H, Okada H. Oxidative Stress Management in Chronic Liver Diseases and Hepatocellular Carcinoma. Nutrients 2020;12:E1576. [PMID: 32481552 DOI: 10.3390/nu12061576] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 5.5] [Reference Citation Analysis]
43 Pohlman RM, Varghese T. Physiological Motion Reduction Using Lagrangian Tracking for Electrode Displacement Elastography. Ultrasound Med Biol 2020;46:766-81. [PMID: 31806499 DOI: 10.1016/j.ultrasmedbio.2019.11.001] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
44 Masuzaki R, Kanda T, Sasaki R, Matsumoto N, Nirei K, Ogawa M, Moriyama M. Application of artificial intelligence in hepatology: Minireview. Artif Intell Gastroenterol 2020; 1(1): 5-11 [DOI: 10.35712/aig.v1.i1.5] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
45 Ning Z, Pan W, Chen Y, Xiao Q, Zhang X, Luo J, Wang J, Zhang Y, Schwartz R. Integrative analysis of cross-modal features for the prognosis prediction of clear cell renal cell carcinoma. Bioinformatics 2020;36:2888-95. [DOI: 10.1093/bioinformatics/btaa056] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
46 Zhou LQ, Wang JY, Yu SY, Wu GG, Wei Q, Deng YB, Wu XL, Cui XW, Dietrich CF. Artificial intelligence in medical imaging of the liver. World J Gastroenterol 2019; 25(6): 672-682 [PMID: 30783371 DOI: 10.3748/wjg.v25.i6.672] [Cited by in CrossRef: 40] [Cited by in F6Publishing: 25] [Article Influence: 13.3] [Reference Citation Analysis]
47 Ning Z, Luo J, Xiao Q, Cai L, Chen Y, Yu X, Wang J, Zhang Y. Multi-modal magnetic resonance imaging-based grading analysis for gliomas by integrating radiomics and deep features. Ann Transl Med 2021;9:298. [PMID: 33708925 DOI: 10.21037/atm-20-4076] [Reference Citation Analysis]
48 Song C, Wang M, Luo Y, Chen J, Peng Z, Wang Y, Zhang H, Li ZP, Shen J, Huang B, Feng ST. Predicting the recurrence risk of pancreatic neuroendocrine neoplasms after radical resection using deep learning radiomics with preoperative computed tomography images. Ann Transl Med 2021;9:833. [PMID: 34164467 DOI: 10.21037/atm-21-25] [Reference Citation Analysis]
49 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]
50 Wang Y, Shao J, Wang P, Chen L, Ying M, Chai S, Ruan S, Tian W, Cheng Y, Zhang H, Zhang X, Wang X, Ding Y, Liang W, Wu L. Deep Learning Radiomics to Predict Regional Lymph Node Staging for Hilar Cholangiocarcinoma. Front Oncol 2021;11:721460. [PMID: 34765542 DOI: 10.3389/fonc.2021.721460] [Reference Citation Analysis]
51 Wong GL, Yuen PC, Ma AJ, Chan AW, Leung HH, Wong VW. Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis. J Gastroenterol Hepatol 2021;36:543-50. [PMID: 33709607 DOI: 10.1111/jgh.15385] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 8.0] [Reference Citation Analysis]
52 Li Q, Li JF, Mao XR. Application of artificial intelligence in liver diseases: From diagnosis to treatment. Artif Intell Gastroenterol 2021; 2(5): 133-140 [DOI: 10.35712/aig.v2.i5.133] [Reference Citation Analysis]
53 Xian MF, Zheng X, Xu JB, Li X, Chen LD, Wang W. Prediction of lymph node metastasis in rectal cancer: comparison between shear-wave elastography based ultrasomics and MRI. Diagn Interv Radiol 2021;27:424-31. [PMID: 34003129 DOI: 10.5152/dir.2021.20031] [Reference Citation Analysis]
54 Wei H, Jiang HY, Li M, Zhang T, Song B. Two-dimensional shear wave elastography for significant liver fibrosis in patients with chronic hepatitis B: A systematic review and meta-analysis. Eur J Radiol 2020;124:108839. [PMID: 31981878 DOI: 10.1016/j.ejrad.2020.108839] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
55 Amano H, Kanda T, Mochizuki H, Kojima Y, Suzuki Y, Hosoda K, Ashizawa H, Miura Y, Tsunoda S, Hirotsu Y, Ohyama H, Kato N, Moriyama M, Obi S, Omata M. The Use of Electronic Medical Records-Based Big-Data Informatics to Describe ALT Elevations Higher than 1000 IU/L in Patients with or without Hepatitis B Virus Infection. Viruses 2021;13:2216. [PMID: 34835022 DOI: 10.3390/v13112216] [Reference Citation Analysis]
56 Pohlman RM, Varghese T. Dictionary Representations for Electrode Displacement Elastography. IEEE Trans Ultrason Ferroelectr Freq Control 2018;65:2381-9. [PMID: 30296219 DOI: 10.1109/TUFFC.2018.2874181] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
57 Cao W, An X, Cong L, Lyu C, Zhou Q, Guo R. Application of Deep Learning in Quantitative Analysis of 2‐Dimensional Ultrasound Imaging of Nonalcoholic Fatty Liver Disease. J Ultrasound Med 2019;39:51-9. [DOI: 10.1002/jum.15070] [Cited by in Crossref: 16] [Cited by in F6Publishing: 18] [Article Influence: 5.3] [Reference Citation Analysis]
58 Ozturk A, Olson MC, Samir AE, Venkatesh SK. Liver fibrosis assessment: MR and US elastography. Abdom Radiol (NY) 2021. [PMID: 34687329 DOI: 10.1007/s00261-021-03269-4] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
59 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]
60 Liang ZN, Yang W. Advances in diagnostic application of ultrasomics in liver lesions. Shijie Huaren Xiaohua Zazhi 2020; 28(12): 460-466 [DOI: 10.11569/wcjd.v28.i12.460] [Reference Citation Analysis]
61 Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Koda H, Miyano A, Fumihara D. Current status of artificial intelligence analysis for endoscopic ultrasonography. Dig Endosc. 2021;33:298-305. [PMID: 33098123 DOI: 10.1111/den.13880] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
62 Wu LL, Wang JL, Huang W, Liu X, Huang YY, Zeng J, Cui CY, Lu JB, Lin P, Long H, Zhang LJ, Wei J, Lu Y, Ma GW. Prognostic Modeling of Patients Undergoing Surgery Alone for Esophageal Squamous Cell Carcinoma: A Histopathological and Computed Tomography Based Quantitative Analysis. Front Oncol 2021;11:565755. [PMID: 33912439 DOI: 10.3389/fonc.2021.565755] [Reference Citation Analysis]
63 Xue LY, Ding H. Current ultrasound-related strategies for assessing liver fibrosis in chronic liver disease. Chin Med J (Engl) 2020;133:2762-4. [PMID: 33009023 DOI: 10.1097/CM9.0000000000001136] [Reference Citation Analysis]
64 Cui H, Zhang D, Peng F, Kong H, Guo Q, Wu T, Wen X, Zhang L, Tian J. Identifying ultrasound features of positive expression of Ki67 and P53 in breast cancer using radiomics. Asia Pac J Clin Oncol 2021;17:e176-84. [PMID: 32779399 DOI: 10.1111/ajco.13397] [Reference Citation Analysis]
65 Xue LY, Jiang ZY, Fu TT, Wang QM, Zhu YL, Dai M, Wang WP, Yu JH, Ding H. Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis.Eur Radiol. 2020;30:2973-2983. [PMID: 31965257 DOI: 10.1007/s00330-019-06595-w] [Cited by in Crossref: 16] [Cited by in F6Publishing: 14] [Article Influence: 8.0] [Reference Citation Analysis]
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