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Articles Published Processes
11/9/2026 4:15:46 AM | Browse: 1 | Download: 1
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Received |
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2026-06-04 08:25 |
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Peer-Review Started |
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2026-06-04 08:25 |
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First Decision by Editorial Office Director |
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Return for Revision |
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2026-06-18 03:09 |
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Revised |
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2026-06-29 01:34 |
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Publication Fee Transferred |
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2026-07-25 14:35 |
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Second Decision by Editor |
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2026-09-09 02:32 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-09-09 10:08 |
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Articles in Press |
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2026-09-09 10:08 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-10-20 03:50 |
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Publish the Manuscript Online |
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2026-11-09 04:15 |
| 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 |
Gastroenterology & Hepatology |
| Manuscript Type |
Meta-Analysis |
| Article Title |
Artificial intelligence for imaging-based detection of gallbladder cancer: A systematic review and diagnostic test accuracy meta-analysis
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Jia-Yun Zhu, Hao Lin, Jing-Wei Zhao and Zi-Yi Yang |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Interdisciplinary Program of Shanghai Jiao Tong University |
No. YG2024QNA19 |
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| Corresponding Author |
Zi-Yi Yang, MD, Department of General Surgery, Xinhua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai 200092, China. yangziyi@xinhuamed.com.cn |
| Key Words |
Gallbladder cancer; Artificial intelligence; Deep learning; Radiomics; Diagnostic accuracy; Meta-analysis; Summary receiver operating characteristic |
| Core Tip |
Early diagnosis of gallbladder cancer remains challenging because malignant lesions often mimic benign gallbladder diseases. This study provides the first diagnostic test accuracy meta-analysis focused exclusively on patient-level validation evidence for artificial intelligence-based imaging diagnosis of gallbladder cancer. By separating clinical validation cohorts from image-level development studies, we found that only five datasets were suitable for quantitative synthesis despite 32 eligible publications. Artificial intelligence-assisted imaging achieved high diagnostic performance and may serve as an effective adjunct for lesion triage and radiological decision support, although robust prospective multicenter validation is still required before routine clinical implementation. |
| Publish Date |
2026-11-09 04:15 |
| Citation |
Zhu JY, Lin H, Zhao JW, Yang ZY. Artificial intelligence for imaging-based detection of gallbladder cancer: A systematic review and diagnostic test accuracy meta-analysis. World J Gastroenterol 2027; 33(1): 123989
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| URL |
https://www.wjgnet.com/1007-9327/full/v33/i1/123989.htm |
| DOI |
https://doi.org/10.3748/wjg.123989 |
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.
