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Cited by in CrossRef
For: 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]
URL: https://www.wjgnet.com/1007-9327/full/v27/i32/5341.htm
Number Citing Articles
1
Liyang Wang, Meilong Wu, Chengzhan Zhu, Rui Li, Shiyun Bao, Shizhong Yang, Jiahong Dong. Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgeryFrontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.1019009
2
Thifhelimbilu Luvhengo, Thulo Molefi, Demetra Demetriou, Rodney Hull, Zodwa Dlamini. Artificial Intelligence and Precision Oncology2023; : 49 doi: 10.1007/978-3-031-21506-3_3
3
Jin Jin, Ying Jiang, Yu-Lan Zhao, Pin-Tong Huang. Radiomics-based Machine Learning to Predict the Recurrence of Hepatocellular Carcinoma: A Systematic Review and Meta-analysisAcademic Radiology 2024; 31(2): 467 doi: 10.1016/j.acra.2023.09.008
4
Evangelos Chartampilas, Vasileios Rafailidis, Vivian Georgopoulou, Georgios Kalarakis, Adam Hatzidakis, Panos Prassopoulos. Current Imaging Diagnosis of Hepatocellular CarcinomaCancers 2022; 14(16): 3997 doi: 10.3390/cancers14163997
5
Chao Lv, Nan He, Jie Jie Yang, Jing Jing Xiao, Yan Zhang, Jun Du, Shi Zuo, Hai Yang Li, Huajian Gu. Prediction of 3-year recurrence rate of hepatocellular carcinoma after resection based on contrast-enhanced CT: a single-centre studyThe British Journal of Radiology 2023; 96(1145) doi: 10.1259/bjr.20220702
6
Liuji Sheng, Chongtu Yang, Yidi Chen, Bin Song. Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver TumorsBiomedicines 2023; 12(1): 58 doi: 10.3390/biomedicines12010058
7
Jianan Chen, Weibin Zhang, Jingwen Bao, Kun Wang, Qiannan Zhao, Yuli Zhu, Yanling Chen. Implications of ultrasound-based deep learning model for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma and intrahepatic cholangiocarcinomaAbdominal Radiology 2023; 49(1): 93 doi: 10.1007/s00261-023-04089-4
8
B. Lakshmipriya, Biju Pottakkat, G. Ramkumar. Deep learning techniques in liver tumour diagnosis using CT and MR imaging - A systematic reviewArtificial Intelligence in Medicine 2023; 141: 102557 doi: 10.1016/j.artmed.2023.102557
9
Yang Yan, Zhang Si, Cui Chun, Pen Chao‐qun, Mu Ke, Zhang Dong, Wen Li. Multiphase MRI‐Based Radiomics for Predicting Histological Grade of Hepatocellular CarcinomaJournal of Magnetic Resonance Imaging 2024;  doi: 10.1002/jmri.29289
10
Joseph A. Kavian, Hannah L. Wilkey, Parth A. Patel, Carter J. Boyd. Harvesting the Power of Artificial Intelligence for Surgery: Uses, Implications, and Ethical ConsiderationsThe American Surgeon™ 2023; 89(12): 5102 doi: 10.1177/00031348231175454
11
Xiaomin Shen, Jinxin Wu, Junwei Su, Zhenyu Yao, Wei Huang, Li Zhang, Yiheng Jiang, Wei Yu, Zhao Li. Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH frameworkFrontiers in Genetics 2023; 14 doi: 10.3389/fgene.2023.1004481
12
冰洁 李. Deep Learning in the Diagnosis and Treatment of Liver Cancer: Review and Pro-spectsAdvances in Clinical Medicine 2023; 13(09): 14103 doi: 10.12677/ACM.2023.1391973
13
Ashok Kumar, Anirudh Goyal. Emerging molecules, tools, technology, and future of surgical knife in gastroenterologyWorld Journal of Gastrointestinal Surgery 2024; 16(4): 988-998 doi: 10.4240/wjgs.v16.i4.988
14
Runzhi Yu, Ziyi Cao, Yiqin Huang, Xuechun Zhang, Jie Chen, Yuchen Li. Establishment and Analysis of a Combined Diagnostic Model of Liver Cancer with Random Forest and Artificial Neural NetworkMathematical Problems in Engineering 2022; 2022: 1 doi: 10.1155/2022/5679837