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Articles Published Processes
6/9/2026 9:23:52 AM | Browse: 2 | Download: 0
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Received |
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2025-12-18 02:28 |
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Peer-Review Started |
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2025-12-18 02:28 |
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First Decision by Editorial Office Director |
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2026-01-19 07:50 |
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Return for Revision |
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2026-01-19 07:50 |
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Revised |
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2026-01-31 23:19 |
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Publication Fee Transferred |
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2026-02-03 10:18 |
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Second Decision by Editor |
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2026-03-19 02:35 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-03-19 08:49 |
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Articles in Press |
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2026-03-19 08:49 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-05-26 08:23 |
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Publish the Manuscript Online |
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2026-06-09 09:23 |
| ISSN |
1948-5204 (online) |
| Open Access |
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. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| 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
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Ren-Tong Gu, Xin Li, Wen Cheng, Xiao-Wei Wang, Hai Jin and Tao Liu |
| ORCID |
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| Funding Agency and Grant Number |
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| 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. |
| Publish Date |
2026-06-09 09:23 |
| 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
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| URL |
https://www.wjgnet.com/1948-5204/full/v18/i6/117851.htm |
| DOI |
https://doi.org/10.4251/wjgo.v18.i6.117851 |
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