Radiomics and deep learning predict neoadjuvant immunochemotherapy response in locally advanced esophageal squamous cell carcinoma

doi:10.3748/wjg.124166

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastroenterol. Dec 14, 2026; 32(46): 124166
Published online Dec 14, 2026. doi: 10.3748/wjg.124166

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Figure 6 Decision curve analysis of the best-performing models across different feature types in the training, internal validation, and external validation cohorts. A: Training; B: Internal validation; C: External validation. RD: Radiomics-deep learning; CRD: Clinical-radiomics-deep learning.

Citation: Zhao BX, Zhang M, Fu WQ, Li XY, Han ML, Zhang J, Gao W, Guo TH, Li HY, Ai SW, Wang HJ, Zhang BY, Wang Q. Radiomics and deep learning predict neoadjuvant immunochemotherapy response in locally advanced esophageal squamous cell carcinoma. World J Gastroenterol 2026; 32(46): 124166
URL: https://www.wjgnet.com/1007-9327/full/v32/i46/124166.htm
DOI: https://dx.doi.org/10.3748/wjg.124166

Zhao BX, Zhang M, Fu WQ, Li XY, Han ML, Zhang J, Gao W, Guo TH, Li HY, Ai SW, Wang HJ, Zhang BY, Wang Q. Radiomics and deep learning predict neoadjuvant immunochemotherapy response in locally advanced esophageal squamous cell carcinoma. World J Gastroenterol 2026; 32(46): 124166 [DOI: 10.3748/wjg.124166]

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