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Cited by in CrossRef
For: Zhang C, Zhong H, Zhao F, Ma ZY, Dai ZJ, Pang GD. Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: Machine learning model based on contrast-enhanced computed tomography. World J Gastrointest Oncol 2024; 16(3): 857-874 [PMID: 38577448 DOI: 10.4251/wjgo.v16.i3.857]
URL: https://www.wjgnet.com/1948-5204/full/v16/i3/857.htm
Number Citing Articles
1
Litao Ruan, Jingtong Yu, Xingqi Lu, Kazushi Numata, Dong Zhang, Xi Liu, Xiaojing Li, Mingwei Zhang, Feiqian Wang. A Nomogram Based on Features of Ultrasonography and Contrast-Enhanced CT to Predict Vessels Encapsulating Tumor Clusters Pattern of Hepatocellular CarcinomaUltrasound in Medicine & Biology 2024; 50(12): 1919 doi: 10.1016/j.ultrasmedbio.2024.08.020
2
Yanhua Huang, Hongwei Qian. Advancing Hepatocellular Carcinoma Management Through Peritumoral Radiomics: Enhancing Diagnosis, Treatment, and PrognosisJournal of Hepatocellular Carcinoma 2024; : 2159 doi: 10.2147/JHC.S493227
3
Yingliang Xie, Tao Zhang, Zixin Liu, Zuyi Yan, Yixing Yu, Qi Qu, Chunyan Gu, Chengyu Ding, Xueqin Zhang. MRI-Based Models Using Habitat Imaging for Predicting Distinct Vascular Patterns in Hepatocellular CarcinomaAcademic Radiology 2025;  doi: 10.1016/j.acra.2025.07.010
4
Mengting Gu, Wenjie Zou, Huilin Chen, Ruilin He, Xingyu Zhao, Ningyang Jia, Wanmin Liu, Peijun Wang. Multilayer perceptron deep learning radiomics model based on Gd-BOPTA MRI to identify vessels encapsulating tumor clusters in hepatocellular carcinoma: a multi-center studyCancer Imaging 2025; 25(1) doi: 10.1186/s40644-025-00895-9