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Articles Published Processes
9/18/2026 3:45:46 AM | Browse: 1 | Download: 0
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Received |
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2026-03-12 02:22 |
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Peer-Review Started |
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2026-03-12 02:22 |
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First Decision by Editorial Office Director |
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2026-04-03 09:15 |
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Return for Revision |
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2026-04-03 09:15 |
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Revised |
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2026-04-20 16:43 |
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Publication Fee Transferred |
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2026-04-21 15:18 |
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Second Decision by Editor |
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2026-06-03 02:34 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-06-03 07:14 |
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Articles in Press |
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2026-06-03 07:14 |
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Edit the Manuscript by Language Editor |
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2026-06-09 18:43 |
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Typeset the Manuscript |
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2026-09-11 01:22 |
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Publish the Manuscript Online |
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2026-09-18 03:45 |
| ISSN |
1007-9327 (print) and 2219-2840 (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 |
Imaging Science & Photographic Technology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Endoscopic ultrasound-based deep learning for predicting chemotherapy response in unresectable pancreatic ductal adenocarcinoma
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Ze-Hua Li, Jun Weng, Yu-Hong Zeng, Shi-Yong Lin, Shuo Li, Kun-Hao Bai and Guo-Liang Xu |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Natural Science Foundation of China (General Program) |
No. 82403973, No. 82200442, and No. 82373118 |
| Guangdong Basic and Applied Basic Research Foundation |
No. 2023A1515010828 |
| Science and Technology Program of Guangzhou |
No. 2025A04J3768 |
| Guangdong Medical Equipment Association Research Fund |
No. YZXH2025KT07 |
| Hong Kong Scholar, Hong Kong Scholar |
No. XJWQ2025016 |
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| Corresponding Author |
Guo-Liang Xu, Department of Endoscopy, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, No. 651 Dongfeng East Road, Yuexiu District, Guangzhou 510060, Guangdong Province, China. xugl@sysucc.org.cn |
| Key Words |
Pancreatic ductal adenocarcinoma; Endoscopic ultrasound; Convolutional neural network; Deep learning; Chemotherapy response |
| Core Tip |
Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, and predicting chemotherapy response remains challenging. This study developed deep learning models based on pre-treatment endoscopic ultrasound images to predict chemotherapy response in PDAC. Four convolutional neural network architectures were evaluated, with ResNeXt50 demonstrating the best performance in the independent test cohort. The model also enabled effective risk stratification for overall survival, outperforming conventional serum biomarker carbohydrate antigen 19-9. These findings suggest that endoscopic ultrasound-based convolutional neural network models may provide a noninvasive tool to support individualized treatment planning in PDAC. |
| Publish Date |
2026-09-18 03:45 |
| Citation |
Li ZH, Weng J, Zeng YH, Lin SY, Li S, Bai KH, Xu GL. Endoscopic ultrasound-based deep learning for predicting chemotherapy response in unresectable pancreatic ductal adenocarcinoma. World J Gastroenterol 2026; 32(41): 120899
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| URL |
https://www.wjgnet.com/1007-9327/full/v32/i41/120899.htm |
| DOI |
https://doi.org/10.3748/wjg.120899 |
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