Publication Name	World Journal of Gastrointestinal Oncology
Manuscript ID	117851
Country	China

Category	Oncology
Manuscript Type	Retrospective Study
Article Title	Machine-learning models integrating preoperative clinical factors and circulating tumor DNA features predict lymph node metastasis in esophageal carcinoma
Manuscript Source	Unsolicited Manuscript
All Author List	Ren-Tong Gu, Xin Li, Wen Cheng, Xiao-Wei Wang, Hai Jin and Tao Liu
Funding Agency and Grant Number
Corresponding Author	Tao Liu, MD, Department of Thoracic Surgery, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China. liu-ta0@outlook.com
Key Words	Lymph node metastasis; Computed tomography; Circulating tumor DNA; Variant allele frequency; Esophageal cancer
Core Tip	This retrospective study developed machine learning models to predict lymph node metastasis in 206 esophageal cancer patients. The optimal random forest model, using clinical, computed tomography, and pathological features, achieved an area under the curve of 0.79 and 82.26% accuracy, outperforming computed tomography alone. Integrating circulating tumor DNA features from a 57-patient subset further improved area under the curve and F1 score by 9.0% and 14.3%, respectively, demonstrating enhanced predictive capability.
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; In press

Received	2025-12-18 02:28
Peer-Review Started	2025-12-18 02:28
First Decision by Editorial Office Director	2026-01-19 07:50
Return for Revision	2026-01-19 07:50
Revised	2026-01-31 23:19
Publication Fee Transferred	2026-02-03 10:18
Second Decision by Editor	2026-03-19 02:35
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-03-19 08:49
Articles in Press	2026-03-19 08:49
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1948-5204 (online)
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Copyright	©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc.
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