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Cited by in CrossRef
For: Sun XF, Zhu HT, Ji WY, Zhang XY, Li XT, Tang L, Sun YS. Preoperative prediction of malignant potential of 2-5 cm gastric gastrointestinal stromal tumors by computerized tomography-based radiomics. World J Gastrointest Oncol 2022; 14(5): 1014-1026 [PMID: 35646280 DOI: 10.4251/wjgo.v14.i5.1014]
URL: https://www.wjgnet.com/1948-5204/full/v14/i5/1014.htm
Number Citing Articles
1
Xianqun Ji, Yu Shang, Lin Tan, Yan Hu, Junjie Liu, Lina Song, Junyan Zhang, Jingxian Wang, Yingjian Ye, Haidong Zhang, Tianfang Peng, Peng An. Prediction of High-Risk Gastrointestinal Stromal Tumor Recurrence Based on Delta-CT Radiomics Modeling: A 3-Year Follow-up Study After SurgeryClinical Medicine Insights: Oncology 2024; 18 doi: 10.1177/11795549241245698
2
Guoxian Chen, Lifang Fan, Jie Liu, Shujian Wu. Machine learning-based predictive model for the differential diagnosis of ≤ 5 cm gastric stromal tumor and gastric schwannoma based on CT imagesDiscover Oncology 2023; 14(1) doi: 10.1007/s12672-023-00801-4
3
Zide Liu, Jiaxin Gao, Chunyan Zeng, Youxiang Chen. Development and validation of a preoperative risk nomogram prediction model for gastric gastrointestinal stromal tumorsSurgical Endoscopy 2024; 38(4): 1933 doi: 10.1007/s00464-024-10674-5