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For: Mokrane FZ, Lu L, Vavasseur A, Otal P, Peron JM, Luk L, Yang H, Ammari S, Saenger Y, Rousseau H, Zhao B, Schwartz LH, Dercle L. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. Eur Radiol. 2020;30:558-570. [PMID: 31444598 DOI: 10.1007/s00330-019-06347-w] [Cited by in Crossref: 31] [Cited by in F6Publishing: 35] [Article Influence: 10.3] [Reference Citation Analysis]
Number Citing Articles
1 Haj-mirzaian A, Kadivar A, Kamel IR, Zaheer A. Updates on Imaging of Liver Tumors. Curr Oncol Rep 2020;22. [DOI: 10.1007/s11912-020-00907-w] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
2 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]
3 Ibrahim A, Widaatalla Y, Refaee T, Primakov S, Miclea RL, Öcal O, Fabritius MP, Ingrisch M, Ricke J, Hustinx R, Mottaghy FM, Woodruff HC, Seidensticker M, Lambin P. Reproducibility of CT-Based Hepatocellular Carcinoma Radiomic Features across Different Contrast Imaging Phases: A Proof of Concept on SORAMIC Trial Data. Cancers (Basel) 2021;13:4638. [PMID: 34572870 DOI: 10.3390/cancers13184638] [Reference Citation Analysis]
4 Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Iannicelli E, Laghi A. Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI. Cancers (Basel) 2021;13:2522. [PMID: 34063937 DOI: 10.3390/cancers13112522] [Reference Citation Analysis]
5 Wang LZ, Hu XX, Shen XC, Wang TC, Zhou S. Intraarterial Lidocaine Administration for Pain Control by Water-in-Oil Technique in Transarterial Chemoembolization: in vivo and Randomized Clinical Trial. J Hepatocell Carcinoma 2021;8:1221-32. [PMID: 34676180 DOI: 10.2147/JHC.S331779] [Reference Citation Analysis]
6 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: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Lu L, Wang D, Wang L, E L, Guo P, Li Z, Xiang J, Yang H, Li H, Yin S, Schwartz LH, Xie C, Zhao B. A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarcinoma. Eur Radiol 2020;30:3614-23. [PMID: 32086583 DOI: 10.1007/s00330-020-06663-6] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
8 Kocak B, Kus EA, Kilickesmez O. How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts. Eur Radiol 2021;31:1819-30. [PMID: 33006018 DOI: 10.1007/s00330-020-07324-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
9 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]
10 Sparchez Z, Craciun R, Caraiani C, Horhat A, Nenu I, Procopet B, Sparchez M, Stefanescu H, Mocan T. Ultrasound or Sectional Imaging Techniques as Screening Tools for Hepatocellular Carcinoma: Fall Forward or Move Forward? J Clin Med 2021;10:903. [PMID: 33668839 DOI: 10.3390/jcm10050903] [Reference Citation Analysis]
11 Gong XQ, Tao YY, Wu YK, Liu N, Yu X, Wang R, Zheng J, Liu N, Huang XH, Li JD, Yang G, Wei XQ, Yang L, Zhang XM. Progress of MRI Radiomics in Hepatocellular Carcinoma. Front Oncol 2021;11:698373. [PMID: 34616673 DOI: 10.3389/fonc.2021.698373] [Reference Citation Analysis]
12 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]
13 Yang H, Hu B. Early gastrointestinal cancer: The application of artificial intelligence. Artif Intell Gastrointest Endosc 2021; 2(4): 185-197 [DOI: 10.37126/aige.v2.i4.185] [Reference Citation Analysis]
14 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]
15 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]
16 Lai Q, Spoletini G, Mennini G, Larghi Laureiro Z, Tsilimigras DI, Pawlik TM, Rossi M. Prognostic role of artificial intelligence among patients with hepatocellular cancer: A systematic review. World J Gastroenterol 2020; 26(42): 6679-6688 [PMID: 33268955 DOI: 10.3748/wjg.v26.i42.6679] [Cited by in CrossRef: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
17 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]
18 Parikh ND, Mehta AS, Singal AG, Block T, Marrero JA, Lok AS. Biomarkers for the Early Detection of Hepatocellular Carcinoma.Cancer Epidemiol Biomarkers Prev. 2020;29:2495-2503. [PMID: 32238405 DOI: 10.1158/1055-9965.EPI-20-0005] [Cited by in Crossref: 14] [Cited by in F6Publishing: 12] [Article Influence: 7.0] [Reference Citation Analysis]
19 Harding-Theobald E, Louissaint J, Maraj B, Cuaresma E, Townsend W, Mendiratta-Lala M, Singal AG, Su GL, Lok AS, Parikh ND. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma. Aliment Pharmacol Ther 2021;54:890-901. [PMID: 34390014 DOI: 10.1111/apt.16563] [Reference Citation Analysis]
20 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]
21 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]
22 Jin ZC, Zhong BY. Application of radiomics in hepatocellular carcinoma: A review. Artif Intell Med Imaging 2021; 2(3): 64-72 [DOI: 10.35711/aimi.v2.i3.64] [Reference Citation Analysis]
23 Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27(16): 1664-1690 [PMID: 33967550 DOI: 10.3748/wjg.v27.i16.1664] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Lee S, Summers RM. Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen. Radiol Clin North Am 2021;59:987-1002. [PMID: 34689882 DOI: 10.1016/j.rcl.2021.07.001] [Reference Citation Analysis]
25 Liu JL, Bao D, Xu ZL, Zhuge XJ. Clinical value of contrast-enhanced computed tomography (CECT) combined with contrast-enhanced ultrasound (CEUS) for characterization and diagnosis of small nodular lesions in liver. Pak J Med Sci 2021;37:1843-8. [PMID: 34912405 DOI: 10.12669/pjms.37.7.4306] [Reference Citation Analysis]
26 Feng B, Ma XH, Wang S, Cai W, Liu XB, Zhao XM. Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: Current status and future perspectives. World J Gastroenterol 2021; 27(32): 5341-5350 [PMID: 34539136 DOI: 10.3748/wjg.v27.i32.5341] [Reference Citation Analysis]
27 Masokano IB, Liu W, Xie S, Marcellin DFH, Pei Y, Li W. The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges. Cancer Imaging 2020;20:67. [PMID: 32962762 DOI: 10.1186/s40644-020-00341-y] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
28 Granata V, Grassi R, Fusco R, Belli A, Cutolo C, Pradella S, Grazzini G, La Porta M, Brunese MC, De Muzio F, Ottaiano A, Avallone A, Izzo F, Petrillo A. Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma. Infect Agent Cancer 2021;16:53. [PMID: 34281580 DOI: 10.1186/s13027-021-00393-0] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
29 Tietz E, Truhn D, Müller-Franzes G, Berres ML, Hamesch K, Lang SA, Kuhl CK, Bruners P, Schulze-Hagen M. A Radiomics Approach to Predict the Emergence of New Hepatocellular Carcinoma in Computed Tomography for High-Risk Patients with Liver Cirrhosis. Diagnostics (Basel) 2021;11:1650. [PMID: 34573991 DOI: 10.3390/diagnostics11091650] [Reference Citation Analysis]
30 Khalili K, Lawlor RL, Pourafkari M, Lu H, Tyrrell P, Kim TK, Jang HJ, Johnson SA, Martel AL. Convolutional neural networks versus radiologists in characterization of small hypoattenuating hepatic nodules on CT: a critical diagnostic challenge in staging of colorectal carcinoma. Sci Rep 2020;10:15248. [PMID: 32943654 DOI: 10.1038/s41598-020-71364-5] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
31 Sartoris R, Gregory J, Dioguardi Burgio M, Ronot M, Vilgrain V. HCC advances in diagnosis and prognosis: Digital and Imaging. Liver Int 2021;41 Suppl 1:73-7. [PMID: 34155790 DOI: 10.1111/liv.14865] [Reference Citation Analysis]
32 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]
33 Cheung HMC, Rubin D. Challenges and opportunities for artificial intelligence in oncological imaging. Clin Radiol 2021;76:728-36. [PMID: 33902889 DOI: 10.1016/j.crad.2021.03.009] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
34 Xie T, Wang X, Zhang Z, Zhou Z. CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas. Front Oncol 2021;11:621520. [PMID: 34178619 DOI: 10.3389/fonc.2021.621520] [Reference Citation Analysis]
35 Xie CY, Hu YH, Ho JW, Han LJ, Yang H, Wen J, Lam KO, Wong IY, Law SY, Chiu KW, Fu JH, Vardhanabhuti V. Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma-A Pilot Study. Cancers (Basel) 2021;13:2145. [PMID: 33946826 DOI: 10.3390/cancers13092145] [Reference Citation Analysis]
36 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]
37 Bao H, Chen T, Zhu J, Xie H, Chen F. CEUS-Based Radiomics Can Show Changes in Protein Levels in Liver Metastases After Incomplete Thermal Ablation. Front Oncol 2021;11:694102. [PMID: 34513676 DOI: 10.3389/fonc.2021.694102] [Reference Citation Analysis]
38 Cuocolo R, Ugga L, Solari D, Corvino S, D'Amico A, Russo D, Cappabianca P, Cavallo LM, Elefante A. Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI. Neuroradiology 2020;62:1649-56. [PMID: 32705290 DOI: 10.1007/s00234-020-02502-z] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
39 Lu L, Sun SH, Yang H, E L, Guo P, Schwartz LH, Zhao B. Radiomics Prediction of EGFR Status in Lung Cancer-Our Experience in Using Multiple Feature Extractors and The Cancer Imaging Archive Data. Tomography 2020;6:223-30. [PMID: 32548300 DOI: 10.18383/j.tom.2020.00017] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 8.0] [Reference Citation Analysis]
40 Ivanics T, Patel MS, Erdman L, Sapisochin G. Artificial intelligence in transplantation (machine-learning classifiers and transplant oncology). Curr Opin Organ Transplant 2020;25:426-34. [PMID: 32487887 DOI: 10.1097/MOT.0000000000000773] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
41 Helmberger T. The evolution of interventional oncology in the 21st century. Br J Radiol 2020;93:20200112. [PMID: 32706978 DOI: 10.1259/bjr.20200112] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]