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World J Gastrointest Oncol. Jun 15, 2026; 18(6): 117851
Published online Jun 15, 2026. doi: 10.4251/wjgo.v18.i6.117851
Published online Jun 15, 2026. doi: 10.4251/wjgo.v18.i6.117851
Machine-learning models integrating preoperative clinical factors and circulating tumor DNA features predict lymph node metastasis in esophageal carcinoma
Ren-Tong Gu, Department of Thoracic Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 201800, China
Xin Li, Xiao-Wei Wang, Hai Jin, Department of Thoracic Surgery, Changhai Hospital, Naval Medical University, Shanghai 200433, China
Wen Cheng, Department of Thoracic Surgery, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
Tao Liu, Department of Thoracic Surgery, Peking University First Hospital, Beijing 100034, China
Co-first authors: Ren-Tong Gu and Xin Li.
Co-corresponding authors: Hai Jin and Tao Liu.
Author contributions: Gu RT and Li X have played indispensable roles in the experimental design and data interpretation as co-first authors; Gu RT, Li X, Cheng W and Wang XW were involved in data curation, formal analysis, and writing original draft; Jin H and Liu T were responsible for supervision and writing review and editing as co-corresponding authors; all of the authors read and approved the final version of the manuscript to be published.
Institutional review board statement: This study complied with all relevant national regulations and institutional policies, was conducted in accordance with the tenets of the Helsinki Declaration (as revised in 2013), and was approved by the Institutional Review Board of Changhai Hospital (No. CHEC2020-021).
Informed consent statement: All participants provided informed consent.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
Data sharing statement: The data used in this study may be obtained upon reasonable request from the corresponding authors.
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
Received: December 18, 2025
Revised: January 31, 2026
Accepted: March 19, 2026
Published online: June 15, 2026
Processing time: 173 Days and 21.1 Hours
Revised: January 31, 2026
Accepted: March 19, 2026
Published online: June 15, 2026
Processing time: 173 Days and 21.1 Hours
Core Tip
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 en