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Cited by in CrossRef
For: Cen YY, Nong HY, Huang XX, Lu XX, Pu CH, Huang LH, Zheng XJ, Pan ZL, Huang Y, Ding K, Huang DY. Computed tomography-based deep learning and multi-instance learning for predicting microvascular invasion and prognosis in hepatocellular carcinoma. World J Gastroenterol 2025; 31(30): 109186 [PMID: 40933208 DOI: 10.3748/wjg.v31.i30.109186]
URL: https://www.wjgnet.com/1007-9327/full/v31/i30/109186.htm
Number Citing Articles
1
Jiale Zeng, Jie Feng, Qiye Xu, Xin Feng, Yanru Pei, Xiang Zhang, Huijun Hu. Multiparametric dual-energy computed tomography radiomics for predicting microvascular invasion in hepatocellular carcinomaBMC Medical Imaging 2025; 25(1) doi: 10.1186/s12880-025-02124-y
2
罗航 徐. Clinical Application Value and Challenges of Combined Analysis of Intratumoral and Peritumoral Ultrasound Radiomics in Hepatocellular CarcinomaAdvances in Clinical Medicine 2026; 16(03): 1225 doi: 10.12677/acm.2026.163899
3
Xiaoxiao Huang, Yurun Xie, Haiyang Nong, Xiaoxin Huang, Donglian Gu, Kui Wang, Deyou Huang, Guanqiao Jin. A multicenter multimodel habitat radiomics model for predicting immunotherapy response in advanced NSCLCiScience 2026; 29(2): 114522 doi: 10.1016/j.isci.2025.114522
4
Wei Feng, Bo Qu, Shuo Han. Accuracy of Medical Image–Based Deep Learning for Detecting Microvascular Invasion in Hepatocellular Carcinoma: Systematic Review and Meta-AnalysisJournal of Medical Internet Research 2026; 28: e82000 doi: 10.2196/82000