| For: | Xiang YH, Mou H, Qu B, Sun HR. Machine learning-based radiomics score improves prognostic prediction accuracy of stage II/III gastric cancer: A multi-cohort study. World J Gastrointest Surg 2024; 16(2): 345-356 [PMID: 38463348 DOI: 10.4240/wjgs.v16.i2.345] |
|---|---|
| URL: | https://www.wjgnet.com/1948-9366/full/v16/i2/345.htm |
| Number | Citing Articles |
| 1 |
Mengjie Fang, Zipei Wang, Jia Fu, Yinkui Wang, Yunpeng Zhao, Zhuyuan Qin, Chenxi Zhang, Xuebin Xie, Lei Tang, Di Dong. Intelligent Medicine: Fundamentals to Future Perspectives. 2026; : 101 doi: 10.1016/B978-0-443-30142-1.00006-7
|
| 2 |
Chao Zhang, Siyuan Li, Daolai Huang, Bo Wen, Shizhuang Wei, Yaodong Song, Xianghua Wu. Development and Validation of an AI-Based Multimodal Model for Pathological Staging of Gastric Cancer Using CT and Endoscopic Images. Academic Radiology 2025; 32(5): 2604 doi: 10.1016/j.acra.2024.12.029
|
| 3 |
Gianni S.S. Liveraro, Maria E.S. Takahashi, Fabiana Lascala, Luiz R. Lopes, Nelson A. Andreollo, Maria C.S. Mendes, Jun Takahashi, José B.C. Carvalheira. Improving resectable gastric cancer prognosis prediction: A machine learning analysis combining clinical features and body composition radiomics. Informatics in Medicine Unlocked 2025; 52: 101608 doi: 10.1016/j.imu.2024.101608
|
