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Cited by in CrossRef
For: Ravikulan A, Rostami K. Leveraging machine learning for early recurrence prediction in hepatocellular carcinoma: A step towards precision medicine. World J Gastroenterol 2024; 30(5): 424-428 [PMID: 38414588 DOI: 10.3748/wjg.v30.i5.424]
URL: https://www.wjgnet.com/1948-5204/full/v30/i5/424.htm
Number Citing Articles
1
Man Wang, Fei Yu, Yuan Zhang. Current status and prospects of artificial intelligence in liver cancer managementIntelligent Oncology 2025;  doi: 10.1016/j.intonc.2025.06.003
2
Ghada Mostafa, Hamdi Mahmoud, Tarek Abd El-Hafeez, Mohamed E. ElAraby. Feature reduction for hepatocellular carcinoma prediction using machine learning algorithmsJournal of Big Data 2024; 11(1) doi: 10.1186/s40537-024-00944-3
3
Andrzej S Tarnawski. Editor-in-Chief articles of choice and comments from January to June 2024World Journal of Gastroenterology 2024; 30(34): 3875-3882 doi: 10.3748/wjg.v30.i34.3875
4
Jiapei Lin, Yilin Li, Dongrui Li, Liyong Zhuo, Jian Wei, Jingwei Wei. Application of large models in imaging diagnosis and prognostic analysis in hepatocellular carcinomaClinical Surgical Oncology 2025; 4(2): 100083 doi: 10.1016/j.cson.2025.100083
5
Liang Hao, Zhao-Nan Zhang, Shuang Han, Shan-Shan Li, Si-Xiang Lin, Yan-Dong Miao. New frontiers in hepatocellular carcinoma: Precision imaging for microvascular invasion predictionWorld Journal of Gastroenterology 2025; 31(8): 102224 doi: 10.3748/wjg.v31.i8.102224
6
Zhilong Cai, Shuoyu Rui, Nanqu Huang, Fei Feng, Yong Luo. The role of vitamin D in sleep regulation: mechanisms, clinical advances, and future directionsFrontiers in Nutrition 2025; 12 doi: 10.3389/fnut.2025.1595813
7
Kai-Di Wang, Ming-Jing Guan, Zi-Yang Bao, Zhe-Jin Shi, Hai-Hang Tong, Zun-Qiang Xiao, Lei Liang, Jun-Wei Liu, Guo-Liang Shen. Radiomics analysis based on dynamic contrast-enhanced MRI for predicting early recurrence after hepatectomy in hepatocellular carcinoma patientsScientific Reports 2025; 15(1) doi: 10.1038/s41598-025-02291-6