Machine-learning models integrating preoperative clinical factors and circulating tumor DNA features predict lymph node metastasis in esophageal carcinoma

doi:10.4251/wjgo.v18.i6.117851

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World Journal of Gastrointestinal Oncology

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1948-5204

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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 Gastrointest Oncol. Jun 15, 2026; 18(6): 117851
Published online Jun 15, 2026. doi: 10.4251/wjgo.v18.i6.117851

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Figure 1 Patient selection and data analysis flow of patients with esophageal cancer. CT: Computed tomography; ESCC: Esophageal squamous cell carcinoma; ESD: Endoscopic submucosal dissection.

Citation: Gu RT, Li X, Cheng W, Wang XW, Jin H, Liu T. Machine-learning models integrating preoperative clinical factors and circulating tumor DNA features predict lymph node metastasis in esophageal carcinoma. World J Gastrointest Oncol 2026; 18(6): 117851
URL: https://www.wjgnet.com/1948-5204/full/v18/i6/117851.htm
DOI: https://dx.doi.org/10.4251/wjgo.v18.i6.117851

Gu RT, Li X, Cheng W, Wang XW, Jin H, Liu T. Machine-learning models integrating preoperative clinical factors and circulating tumor DNA features predict lymph node metastasis in esophageal carcinoma. World J Gastrointest Oncol 2026; 18(6): 117851 [DOI: 10.4251/wjgo.v18.i6.117851]

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