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Cited by in F6Publishing
For: Jiang YQ, Cao SE, Cao S, Chen JN, Wang GY, Shi WQ, Deng YN, Cheng N, Ma K, Zeng KN, Yan XJ, Yang HZ, Huan WJ, Tang WM, Zheng Y, Shao CK, Wang J, Yang Y, Chen GH. Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning.J Cancer Res Clin Oncol. 2021;147:821-833. [PMID: 32852634 DOI: 10.1007/s00432-020-03366-9] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [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 Chong H, Gong Y, Pan X, Liu A, Chen L, Yang C, Zeng M. Peritumoral Dilation Radiomics of Gadoxetate Disodium-Enhanced MRI Excellently Predicts Early Recurrence of Hepatocellular Carcinoma without Macrovascular Invasion After Hepatectomy. J Hepatocell Carcinoma 2021;8:545-63. [PMID: 34136422 DOI: 10.2147/JHC.S309570] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
3 Song D, Wang Y, Wang W, Cai J, Zhu K, Lv M, Gao Q, Zhou J, Fan J, Rao S, Wang M, Wang X. Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters. J Cancer Res Clin Oncol. 2021 epub ahead of print. [PMID: 33839938 DOI: 10.1007/s00432-021-03617-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
4 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]
5 Zhang Y, Lv X, Qiu J, Zhang B, Zhang L, Fang J, Li M, Chen L, Wang F, Liu S, Zhang S. Deep Learning With 3D Convolutional Neural Network for Noninvasive Prediction of Microvascular Invasion in Hepatocellular Carcinoma. J Magn Reson Imaging 2021;54:134-43. [PMID: 33559293 DOI: 10.1002/jmri.27538] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Zhang W, Liu Z, Chen J, Dong S, Cen B, Zheng S, Xu X. A preoperative model for predicting microvascular invasion and assisting in prognostic stratification in liver transplantation for HCC regarding empirical criteria. Transl Oncol 2021;14:101200. [PMID: 34399173 DOI: 10.1016/j.tranon.2021.101200] [Reference Citation Analysis]
7 Ahn JC, Qureshi TA, Singal AG, Li D, Yang JD. Deep learning in hepatocellular carcinoma: Current status and future perspectives. World J Hepatol 2021; 13(12): 2039-2051 [DOI: 10.4254/wjh.v13.i12.2039] [Reference Citation Analysis]
8 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]
9 Liu SC, Lai J, Huang JY, Cho CF, Lee PH, Lu MH, Yeh CC, Yu J, Lin WC. Predicting microvascular invasion in hepatocellular carcinoma: a deep learning model validated across hospitals. Cancer Imaging 2021;21:56. [PMID: 34627393 DOI: 10.1186/s40644-021-00425-3] [Reference Citation Analysis]
10 Wang Q, Li C, Zhang J, Hu X, Fan Y, Ma K, Sparrelid E, Brismar TB. Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment. Cancers (Basel) 2021;13:5864. [PMID: 34831018 DOI: 10.3390/cancers13225864] [Reference Citation Analysis]
11 Nam D, Chapiro J, Paradis V, Seraphin TP, Kather JN. Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction. JHEP Reports 2022. [DOI: 10.1016/j.jhepr.2022.100443] [Reference Citation Analysis]
12 Gong X, Zheng B, Xu G, Chen H, Chen C. Application of machine learning approaches to predict the 5-year survival status of patients with esophageal cancer. J Thorac Dis 2021;13:6240-51. [PMID: 34992804 DOI: 10.21037/jtd-21-1107] [Reference Citation Analysis]
13 Xu L, Jian X, Liu Z, Zhao J, Zhang S, Lin Y, Xie L. Construction and Validation of an Immune Cell Signature Score to Evaluate Prognosis and Therapeutic Efficacy in Hepatocellular Carcinoma. Front Genet 2021;12:741226. [PMID: 34646307 DOI: 10.3389/fgene.2021.741226] [Reference Citation Analysis]
14 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]
15 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]
16 Xia F, Ndhlovu E, Liu Z, Chen X, Zhang B, Zhu P, Liu Z. Alpha-Fetoprotein+Alkaline Phosphatase (A-A) Score Can Predict the Prognosis of Patients with Ruptured Hepatocellular Carcinoma Underwent Hepatectomy. Disease Markers 2022;2022:1-16. [DOI: 10.1155/2022/9934189] [Reference Citation Analysis]