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Articles Published Processes
11/25/2021 7:42:46 AM | Browse: 560 | Download: 1639
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Received |
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2021-07-29 02:33 |
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Peer-Review Started |
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2021-07-29 02:37 |
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To Make the First Decision |
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Return for Revision |
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2021-08-19 08:59 |
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Revised |
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2021-09-05 14:46 |
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Second Decision |
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2021-11-15 03:34 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2021-11-15 06:52 |
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Articles in Press |
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2021-11-15 06:52 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2021-11-18 03:21 |
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Publish the Manuscript Online |
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2021-11-25 07:42 |
| 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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
©The Author(s) 2021. 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 |
Pathology |
| Manuscript Type |
Basic Study |
| Article Title |
Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Hyun-Jong Jang, Ahwon Lee, Jun Kang, In Hye Song and Sung Hak Lee |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) |
2019R1F1A1062367 |
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| Corresponding Author |
Sung Hak Lee, MD, PhD, Associate Professor, Department of Hospital Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, South Korea. hakjjang@catholic.ac.kr |
| Key Words |
Gastric cancer; Mutation; Deep learning; Digital pathology; Formalin-fixed paraffin-embedded |
| Core Tip |
Recently, deep learning approach has been implemented to predict the mutational status from hematoxylin and eosin (H and E)-stained tissue images of diverse tumors. The aim of our study was to evaluate the feasibility of classifiers for mutations in the CDH1, ERBB2, KRAS, PIK3CA, and TP53 genes in gastric cancer tissues. The area under the curves for receiver operating characteristic curves ranged from 0.727 to 0.862 for the The Cancer Genome Atlas (TCGA) frozen tissues and 0.661 to 0.858 for the TCGA formalin-fixed paraffin-embedded tissues. This study confirmed that deep learning-based classifiers can predict major mutations from the H and E-stained gastric cancer whole slide images when they are trained with appropriate data. |
| Publish Date |
2021-11-25 07:42 |
| Citation |
Jang HJ, Lee A, Kang J, Song IH, Lee SH. Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach. World J Gastroenterol 2021; 27(44): 7687-7704 |
| URL |
https://www.wjgnet.com/1007-9327/full/v27/i44/7687.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v27.i44.7687 |
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