| For: | Hirata A, Hayano K, Tochigi T, Kurata Y, Shiraishi T, Sekino N, Nakano A, Matsumoto Y, Toyozumi T, Uesato M, Ohira G. Predicting pathological complete response to chemoradiotherapy using artificial intelligence-based magnetic resonance imaging radiomics in esophageal squamous cell carcinoma. World J Gastroenterol 2025; 31(36): 111293 [PMID: 41025076 DOI: 10.3748/wjg.v31.i36.111293] |
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| URL: | https://www.wjgnet.com/1948-5190/full/v31/i36/111293.htm |
| Number | Citing Articles |
| 1 |
Fangyuan Long, Hongru Zhang, Shungeng Zhang, Zongfu Dong, Xupeng Huang, Ronghang Hu. Machine learning applications in the detection and treatment of esophageal cancer. Discover Oncology 2026; 17(1) doi: 10.1007/s12672-026-05477-0
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| 2 |
Songxia Yu, Meini Gong, Haowen Wang, Hanbo Liu, Min Deng. Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis. Frontiers in Medicine 2026; 13 doi: 10.3389/fmed.2026.1795060
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| 3 |
Qingmiao Shi, Mengjuan Xuan, Chi Lv, Zhibo Zhang, Xiaonan Geng, Di Huang, Xinjun Hu. Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions. Frontiers in Immunology 2026; 17 doi: 10.3389/fimmu.2026.1776472
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| 4 |
Yuqi Yang, Xiao Jia, Xi Wang, Pingdong Cao, Jian Zhu, Chuanxi Wang, Zhe Yang, Qiang Wen. AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health. Journal of Translational Medicine 2026; 24(1) doi: 10.1186/s12967-026-08270-3
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