| For: | Zhang J, Wang Q, Guo TH, Gao W, Yu YM, Wang RF, Yu HL, Chen JJ, Sun LL, Zhang BY, Wang HJ. Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer. World J Gastrointest Oncol 2024; 16(10): 4115-4128 [PMID: 39473942 DOI: 10.4251/wjgo.v16.i10.4115] |
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| URL: | https://www.wjgnet.com/1948-5204/full/v16/i10/4115.htm |
| Number | Citing Articles |
| 1 |
Min Fu, Jialing Xu, Yingying Lv, Baijun Jin. Artificial intelligence in advanced gastric cancer: a comprehensive review of applications in precision oncology. Frontiers in Oncology 2025; 15 doi: 10.3389/fonc.2025.1630628
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| 2 |
Yuze Wei, Yanmei Zhu, Qian Dong, Wentao Wang, Tao Yu, Jianjun Zhang, Yue Dong. Interpretable combined models for predicting treatment response and hematologic toxicity in locally advanced gastric cancer treated with PD-1 blockade and neoadjuvant chemotherapy. European Journal of Radiology 2025; 190 doi: 10.1016/j.ejrad.2025.112256
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| 3 |
Carlos M Ardila, Daniel González-Arroyave, Jaime Ramírez-Arbeláez. Artificial intelligence as a predictive tool for gastric cancer: Bridging innovation, clinical translation, and ethical considerations. World Journal of Gastrointestinal Oncology 2025; 17(5): 103275 doi: 10.4251/wjgo.v17.i5.103275
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| 4 |
Chenjiao Ran, Xinyu Chen, Yong Huang, Derui Kong. Predicting response to neoadjuvant chemotherapy combined with immunotherapy in gastric cancer based on habitat imaging and peritumoral radiomics: a two-center study. Journal of Translational Medicine 2026; 24(1) doi: 10.1186/s12967-026-08342-4
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