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Articles Published Processes
7/13/2026 9:40:25 AM | Browse: 70 | Download: 363
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Received |
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2026-01-14 03:17 |
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Peer-Review Started |
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2026-01-14 03:18 |
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First Decision by Editorial Office Director |
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2026-02-13 07:57 |
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Return for Revision |
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2026-02-14 14:59 |
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Revised |
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2026-02-26 13:18 |
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Publication Fee Transferred |
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2026-03-01 02:44 |
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Second Decision by Editor |
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2026-03-31 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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2026-03-31 08:27 |
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Articles in Press |
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2026-03-31 08:27 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-06-15 01:47 |
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Publish the Manuscript Online |
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2026-07-13 09:40 |
| ISSN |
1948-5204 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/ |
| 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 |
Medicine, General & Internal |
| Manuscript Type |
Retrospective Study |
| Article Title |
Endoscopic ultrasonography lesion thickness predicts deep invasion in gastric cancer: Development and temporal validation of a preoperative model
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Meng-Yu Cao, Yu-Fan Chen, Yun-Xiu Liao, Qing Yang, Shi-Yong Lin, Yin Li, Jun Weng, Xin-Xin Huang, Xiao-Yan Gao, Guo-Bao Wang, Hong-Bo Shan and Jian-Jun Li |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Natural Science Foundation of Tibet Autonomous Region for Group Medical Assistance Project |
XZ2019ZR-ZY60(Z) |
| Guangzhou Clinical Specialty Technology Construction Project |
2026P-TS016 |
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| Corresponding Author |
Hong-Bo Shan, Chief Physician, MD, PhD, Senior Research Fellow, Department of Endoscopy, Sun Yat-sen University Cancer Center, No. 651 Dongfengdong Street, Yuexiu District, Guangzhou 510060, Guangdong Province, China. shanhb@sysucc.org.cn |
| Key Words |
Gastric cancer; Endoscopic ultrasonography; Lesion thickness; Tumour invasion depth; Preoperative prediction; Risk model |
| Core Tip |
Preoperative identification of deep gastric wall invasion remains challenging. Endoscopic ultrasonography provides both a categorical T stage and a measurable lesion thickness. We developed and internally validated a logistic model that treats lesion thickness as a continuous predictor, together with routinely available clinical variables, to estimate individualised risk of pathological ≥ pT3 disease. The model showed good discrimination and decision-curve utility, and outperformed uT-based benchmarks. Temporal testing in a later same-institution cohort preserved discrimination but showed calibration drift, supporting future recalibration, external validation, and context-specific threshold selection. |
| Publish Date |
2026-07-13 09:40 |
| Citation |
Cao MY, Chen YF, Liao YX, Yang Q, Lin SY, Li Y, Weng J, Huang XX, Gao XY, Wang GB, Shan HB, Li JJ. Endoscopic ultrasonography lesion thickness predicts deep invasion in gastric cancer: Development and temporal validation of a preoperative model. World J Gastrointest Oncol 2026; 18(7): 118867
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| URL |
https://www.wjgnet.com/1948-5204/full/v18/i7/118867.htm |
| DOI |
https://doi.org/10.4251/wjgo.v18.i7.118867 |
Copyright © 1993-2026 Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA. All rights reserved, including rights relating to text and data mining, AI training, and similar technologies. For open-access content, the applicable copyright and licensing terms govern.
