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Cited by in F6Publishing
For: Guo D, Gu D, Wang H, Wei J, Wang Z, Hao X, Ji Q, Cao S, Song Z, Jiang J, Shen Z, Tian J, Zheng H. Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation. Eur J Radiol. 2019;117:33-40. [PMID: 31307650 DOI: 10.1016/j.ejrad.2019.05.010] [Cited by in Crossref: 26] [Cited by in F6Publishing: 24] [Article Influence: 8.7] [Reference Citation Analysis]
Number Citing Articles
1 Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14(4): 765-793 [DOI: 10.4251/wjgo.v14.i4.765] [Reference Citation Analysis]
2 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]
3 Liu X, Khalvati F, Namdar K, Fischer S, Lewis S, Taouli B, Haider MA, Jhaveri KS. Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning?Eur Radiol. 2021;31:244-255. [PMID: 32749585 DOI: 10.1007/s00330-020-07119-7] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
4 Wang X, Wang S, Yin X, Zheng Y. MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma. Comput Biol Med 2021;:105058. [PMID: 34836622 DOI: 10.1016/j.compbiomed.2021.105058] [Reference Citation Analysis]
5 Fang S, Lai L, Zhu J, Zheng L, Xu Y, Chen W, Wu F, Wu X, Chen M, Weng Q, Ji J, Zhao Z, Tu J. A Radiomics Signature-Based Nomogram to Predict the Progression-Free Survival of Patients With Hepatocellular Carcinoma After Transcatheter Arterial Chemoembolization Plus Radiofrequency Ablation. Front Mol Biosci 2021;8:662366. [PMID: 34532340 DOI: 10.3389/fmolb.2021.662366] [Reference Citation Analysis]
6 Huang X, Long L, Wei J, Li Y, Xia Y, Zuo P, Chai X. Radiomics for diagnosis of dual-phenotype hepatocellular carcinoma using Gd-EOB-DTPA-enhanced MRI and patient prognosis. J Cancer Res Clin Oncol. 2019;145:2995-3003. [PMID: 31664520 DOI: 10.1007/s00432-019-03062-3] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 3.7] [Reference Citation Analysis]
7 Arora S, Harmath C, Catania R, Mandler A, Fowler KJ, Borhani AA. Hepatocellular carcinoma: metastatic pathways and extra-hepatic findings. Abdom Radiol (NY) 2021;46:3698-707. [PMID: 34091729 DOI: 10.1007/s00261-021-03151-3] [Reference Citation Analysis]
8 Lewis S, Hectors S, Taouli B. Radiomics of hepatocellular carcinoma. Abdom Radiol (NY). 2021;46:111-123. [PMID: 31925492 DOI: 10.1007/s00261-019-02378-5] [Cited by in Crossref: 12] [Cited by in F6Publishing: 17] [Article Influence: 12.0] [Reference Citation Analysis]
9 Cusumano D, Boldrini L, Yadav P, Casà C, Lee SL, Romano A, Piras A, Chiloiro G, Placidi L, Catucci F, Votta C, Mattiucci GC, Indovina L, Gambacorta MA, Bassetti M, Valentini V. Delta Radiomics Analysis for Local Control Prediction in Pancreatic Cancer Patients Treated Using Magnetic Resonance Guided Radiotherapy. Diagnostics (Basel) 2021;11:72. [PMID: 33466307 DOI: 10.3390/diagnostics11010072] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
10 Sun K, Shi L, Qiu J, Pan Y, Wang X, Wang H. Multi-phase contrast-enhanced magnetic resonance image-based radiomics-combined machine learning reveals microscopic ultra-early hepatocellular carcinoma lesions. Eur J Nucl Med Mol Imaging 2022. [PMID: 35230493 DOI: 10.1007/s00259-022-05742-8] [Reference Citation Analysis]
11 Wang XH, Long LH, Cui Y, Jia AY, Zhu XG, Wang HZ, Wang Z, Zhan CM, Wang ZH, Wang WH. MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma. Br J Cancer 2020;122:978-85. [PMID: 31937925 DOI: 10.1038/s41416-019-0706-0] [Cited by in Crossref: 14] [Cited by in F6Publishing: 18] [Article Influence: 7.0] [Reference Citation Analysis]
12 Wei L, Owen D, Rosen B, Guo X, Cuneo K, Lawrence TS, Ten Haken R, El Naqa I. A deep survival interpretable radiomics model of hepatocellular carcinoma patients. Phys Med 2021;82:295-305. [PMID: 33714190 DOI: 10.1016/j.ejmp.2021.02.013] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
13 Casadei-Gardini A, Orsi G, Caputo F, Ercolani G. Developments in predictive biomarkers for hepatocellular carcinoma therapy. Expert Rev Anticancer Ther 2020;20:63-74. [PMID: 31910040 DOI: 10.1080/14737140.2020.1712198] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
14 Sheen H, Kim JS, Lee JK, Choi SY, Baek SY, Kim JY. A radiomics nomogram for predicting transcatheter arterial chemoembolization refractoriness of hepatocellular carcinoma without extrahepatic metastasis or macrovascular invasion. Abdom Radiol (NY) 2021;46:2839-49. [PMID: 33388805 DOI: 10.1007/s00261-020-02884-x] [Reference Citation Analysis]
15 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]
16 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]
17 Miranda Magalhaes Santos JM, Clemente Oliveira B, Araujo-Filho JAB, Assuncao-Jr AN, de M Machado FA, Carlos Tavares Rocha C, Horvat JV, Menezes MR, Horvat N. State-of-the-art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations. Abdom Radiol (NY). 2020;45:342-353. [PMID: 31707435 DOI: 10.1007/s00261-019-02299-3] [Cited by in Crossref: 15] [Cited by in F6Publishing: 11] [Article Influence: 15.0] [Reference Citation Analysis]
18 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]
19 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]
20 Yao S, Ye Z, Wei Y, Jiang HY, Song B. Radiomics in hepatocellular carcinoma: A state-of-the-art review. World J Gastrointest Oncol 2021; 13(11): 1599-1615 [PMID: 34853638 DOI: 10.4251/wjgo.v13.i11.1599] [Reference Citation Analysis]
21 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]
22 Ke Z, Li L, Wang L, Liu H, Lu X, Zeng F, Zha Y. Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19). Acta Radiol 2021;:284185121994695. [PMID: 33601893 DOI: 10.1177/0284185121994695] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
23 Zhang L, Hu J, Hou J, Jiang X, Guo L, Tian L. Radiomics-based model using gadoxetic acid disodium-enhanced MR images: associations with recurrence-free survival of patients with hepatocellular carcinoma treated by surgical resection. Abdom Radiol (NY) 2021;46:3845-54. [PMID: 33733337 DOI: 10.1007/s00261-021-03034-7] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
24 Bell M, Turkbey EB, Escorcia FE. Radiomics, Radiogenomics, and Next-Generation Molecular Imaging to Augment Diagnosis of Hepatocellular Carcinoma. Cancer J. 2020;26:108-115. [PMID: 32205534 DOI: 10.1097/ppo.0000000000000435] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
25 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]
26 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]
27 Finotti M, Vitale A, Volk M, Cillo U. A 2020 update on liver transplant for hepatocellular carcinoma. Expert Rev Gastroenterol Hepatol 2020;14:885-900. [PMID: 32662680 DOI: 10.1080/17474124.2020.1791704] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
28 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]
29 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]
30 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]