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] |
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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 Surgery. Clinical Medicine Insights: Oncology 2024; 18 doi: 10.1177/11795549241245698
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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 images. Discover Oncology 2023; 14(1) doi: 10.1007/s12672-023-00801-4
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3 |
Ping Yang, Jiamei Wu, Mengqi Liu, Yineng Zheng, Xiaofang Zhao, Yun Mao. Preoperative CT‐based radiomics and deep learning model for predicting risk stratification of gastric gastrointestinal stromal tumors. Medical Physics 2024; doi: 10.1002/mp.17276
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4 |
Zide Liu, Jiaxin Gao, Chunyan Zeng, Youxiang Chen. Development and validation of a preoperative risk nomogram prediction model for gastric gastrointestinal stromal tumors. Surgical Endoscopy 2024; 38(4): 1933 doi: 10.1007/s00464-024-10674-5
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