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Articles Published Processes
9/24/2020 6:20:22 AM | Browse: 703 | Download: 1557
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Received |
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2020-05-24 06:16 |
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Peer-Review Started |
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2020-05-24 06:17 |
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To Make the First Decision |
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Return for Revision |
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2020-07-29 04:21 |
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Revised |
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2020-08-02 11:48 |
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Second Decision |
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2020-08-28 12:26 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2020-08-29 02:15 |
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Articles in Press |
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2020-08-29 02:15 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2020-09-09 21:07 |
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Typeset the Manuscript |
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2020-09-17 14:17 |
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Publish the Manuscript Online |
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2020-09-24 06:20 |
ISSN |
1007-9327 (print) and 2219-2840 (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 |
© The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. |
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 |
Minireviews |
Article Title |
Artificial intelligence in gastric cancer: Application and future perspectives
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Manuscript Source |
Invited Manuscript |
All Author List |
Peng-Hui Niu, Lu-Lu Zhao, Hong-Liang Wu, Dong-Bing Zhao and Ying-Tai Chen |
ORCID |
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Funding Agency and Grant Number |
Funding Agency |
Grant Number |
National Key R&D Program of China |
2017YFC0908300 |
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Corresponding Author |
Ying-Tai Chen, MD, Professor, Department of Pancreatic and Gastric Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China. yingtaichen@126.com |
Key Words |
Gastric cancer; Image-based diagnosis; Prognosis prediction; Artificial intelligence; Machine learning; Deep learning |
Core Tip |
Recently, several applications of artificial intelligence (AI) have emerged in the gastric cancer field based on its efficient computational power and learning capacities, such as image-based diagnosis and prognosis prediction. In this review, we search the relevant study works published up to April 2020 from databases of PubMed, Embase, Web of Science, and the Cochrane Library, thus comprehensively summarize the current status of AI-applications in gastric cancer. Besides, challenges and future directions that target at the field are also discussed to improve the accuracy and applicability of AI-models in clinical practice. |
Publish Date |
2020-09-24 06:20 |
Citation |
Niu PH, Zhao LL, Wu HL, Zhao DB, Chen YT. Artificial intelligence in gastric cancer: Application and future perspectives. World J Gastroenterol 2020; 26(36): 5408-5419 |
URL |
https://www.wjgnet.com/1007-9327/full/v26/i36/5408.htm |
DOI |
https://dx.doi.org/10.3748/wjg.v26.i36.5408 |
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