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For: Liu D, Liu F, Xie X, Su L, Liu M, Kuang M, Huang G, Wang Y, Zhou H, Wang K, Lin M, Tian J. Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound. Eur Radiol. 2020;30:2365-2376. [PMID: 31900703 DOI: 10.1007/s00330-019-06553-6] [Cited by in Crossref: 27] [Cited by in F6Publishing: 22] [Article Influence: 13.5] [Reference Citation Analysis]
Number Citing Articles
1 Müller L, Stoehr F, Mähringer-Kunz A, Hahn F, Weinmann A, Kloeckner R. Current Strategies to Identify Patients That Will Benefit from TACE Treatment and Future Directions a Practical Step-by-Step Guide. J Hepatocell Carcinoma 2021;8:403-19. [PMID: 34012930 DOI: 10.2147/JHC.S285735] [Reference Citation Analysis]
2 Eisenbrey JR, Gabriel H, Savsani E, Lyshchik A. Contrast-enhanced ultrasound (CEUS) in HCC diagnosis and assessment of tumor response to locoregional therapies.Abdom Radiol (NY). 2021;46:3579-3595. [PMID: 33825927 DOI: 10.1007/s00261-021-03059-y] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
3 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]
4 Fu J, Tang J, Luo H, Wu W. The value of contrast-enhanced ultrasound in predicting postoperative recurrence of hepatocellular carcinoma: A protocol for systematic review and meta-analysis. Medicine (Baltimore) 2021;100:e25984. [PMID: 34087841 DOI: 10.1097/MD.0000000000025984] [Reference Citation Analysis]
5 Peng J, Huang J, Huang G, Zhang J. Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning. Front Oncol 2021;11:730282. [PMID: 34745952 DOI: 10.3389/fonc.2021.730282] [Reference Citation Analysis]
6 Tipaldi MA, Ronconi E, Lucertini E, Krokidis M, Zerunian M, Polidori T, Begini P, Marignani M, Mazzuca F, Caruso D, Rossi M, Laghi A. Hepatocellular Carcinoma Drug-Eluting Bead Transarterial Chemoembolization (DEB-TACE): Outcome Analysis Using a Model Based On Pre-Treatment CT Texture Features. Diagnostics (Basel) 2021;11:956. [PMID: 34073545 DOI: 10.3390/diagnostics11060956] [Reference Citation Analysis]
7 Moldogazieva NT, Mokhosoev IM, Zavadskiy SP, Terentiev AA. Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine. Biomedicines 2021;9:159. [PMID: 33562077 DOI: 10.3390/biomedicines9020159] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
8 Zou ZM, Chang DH, Liu H, Xiao YD. Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know? Insights Imaging 2021;12:31. [PMID: 33675433 DOI: 10.1186/s13244-021-00977-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
9 Yi PS, Hu CJ, Li CH, Yu F. Clinical value of artificial intelligence in hepatocellular carcinoma: Current status and prospect. Artif Intell Gastroenterol 2021; 2(2): 42-55 [DOI: 10.35712/aig.v2.i2.42] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Vosshenrich J, Zech CJ, Heye T, Boldanova T, Fucile G, Wieland S, Heim MH, Boll DT. Response prediction of hepatocellular carcinoma undergoing transcatheter arterial chemoembolization: unlocking the potential of CT texture analysis through nested decision tree models. Eur Radiol 2021;31:4367-76. [PMID: 33274405 DOI: 10.1007/s00330-020-07511-3] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
11 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]
12 Sato M, Tateishi R, Yatomi Y, Koike K. Artificial intelligence in the diagnosis and management of hepatocellular carcinoma.J Gastroenterol Hepatol. 2021;36:551-560. [PMID: 33709610 DOI: 10.1111/jgh.15413] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
13 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]
14 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]
15 Veerankutty FH, Jayan G, Yadav MK, Manoj KS, Yadav A, Nair SRS, Shabeerali TU, Yeldho V, Sasidharan M, Rather SA. Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research. World J Hepatol 2021; 13(12): 1977-1990 [DOI: 10.4254/wjh.v13.i12.1977] [Reference Citation Analysis]
16 Castaldo A, De Lucia DR, Pontillo G, Gatti M, Cocozza S, Ugga L, Cuocolo R. State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma. Diagnostics (Basel) 2021;11:1194. [PMID: 34209197 DOI: 10.3390/diagnostics11071194] [Reference Citation Analysis]
17 Nishida N, Kudo M. Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor. Front Oncol 2020;10:594580. [PMID: 33409151 DOI: 10.3389/fonc.2020.594580] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
18 Mendiratta-Lala M. HCC Treatment Response DFP report. Abdom Radiol (NY) 2021;46:423-4. [PMID: 33416932 DOI: 10.1007/s00261-020-02915-7] [Reference Citation Analysis]
19 Lang Q, Zhong C, Liang Z, Zhang Y, Wu B, Xu F, Cong L, Wu S, Tian Y. Six application scenarios of artificial intelligence in the precise diagnosis and treatment of liver cancer. Artif Intell Rev 2021;54:5307-46. [DOI: 10.1007/s10462-021-10023-1] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Jiménez Pérez M, Grande RG. Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review. World J Gastroenterol 2020; 26(37): 5617-5628 [PMID: 33088156 DOI: 10.3748/wjg.v26.i37.5617] [Cited by in CrossRef: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
21 Spieler B, Sabottke C, Moawad AW, Gabr AM, Bashir MR, Do RKG, Yaghmai V, Rozenberg R, Gerena M, Yacoub J, Elsayes KM. Artificial intelligence in assessment of hepatocellular carcinoma treatment response. Abdom Radiol (NY) 2021;46:3660-71. [PMID: 33786653 DOI: 10.1007/s00261-021-03056-1] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
22 Svecic A, Mansour R, Tang A, Kadoury S. Prediction of post transarterial chemoembolization MR images of hepatocellular carcinoma using spatio-temporal graph convolutional networks. PLoS One 2021;16:e0259692. [PMID: 34874934 DOI: 10.1371/journal.pone.0259692] [Reference Citation Analysis]
23 Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F; Special Interest Group (SIG) Artificial Intelligence and Liver Diseases; Italian Association for the Study of the Liver (AISF). The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2021:S1590-8658(21)00317-0. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Reference Citation Analysis]
24 Oezdemir I, Wessner CE, Shaw C, Eisenbrey JR, Hoyt K. Tumor Vascular Networks Depicted in Contrast-Enhanced Ultrasound Images as a Predictor for Transarterial Chemoembolization Treatment Response. Ultrasound Med Biol 2020;46:2276-86. [PMID: 32561069 DOI: 10.1016/j.ultrasmedbio.2020.05.010] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 3.5] [Reference Citation Analysis]
25 Song KD. Current status of deep learning applications in abdominal ultrasonography. Ultrasonography 2021;40:177-82. [PMID: 33242931 DOI: 10.14366/usg.20085] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
26 Shen YT, Chen L, Yue WW, Xu HX. Artificial intelligence in ultrasound. Eur J Radiol 2021;139:109717. [PMID: 33962110 DOI: 10.1016/j.ejrad.2021.109717] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 Guo Z, Zhong N, Xu X, Zhang Y, Luo X, Zhu H, Zhang X, Wu D, Qiu Y, Tu F. Prediction of Hepatocellular Carcinoma Response to Transcatheter Arterial Chemoembolization: A Real-World Study Based on Non-Contrast Computed Tomography Radiomics and General Image Features. J Hepatocell Carcinoma 2021;8:773-82. [PMID: 34277508 DOI: 10.2147/JHC.S316117] [Reference Citation Analysis]
28 Wang B, Perronne L, Burke C, Adler RS. Artificial Intelligence for Classification of Soft-Tissue Masses at US. Radiol Artif Intell 2021;3:e200125. [PMID: 33937855 DOI: 10.1148/ryai.2020200125] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]