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Articles Published Processes
10/28/2020 8:26:09 AM | Browse: 927 | Download: 2576
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Received |
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2020-06-28 07:33 |
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Peer-Review Started |
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2020-06-28 07:34 |
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To Make the First Decision |
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Return for Revision |
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2020-07-28 21:19 |
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Revised |
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2020-08-09 04:55 |
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Second Decision |
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2020-09-24 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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2020-09-25 05:30 |
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Articles in Press |
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2020-09-25 05:30 |
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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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2020-10-20 10:00 |
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Publish the Manuscript Online |
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2020-10-28 08:26 |
| 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 |
Pathology |
| Manuscript Type |
Basic Study |
| Article Title |
Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Hyun-Jong Jang, Ahwon Lee, J Kang, In Hye Song and Sung Hak Lee |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Research Fund of Seoul St. Mary’s Hospital made in the program year of 2018 |
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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 |
Colorectal cancer; Mutation; Deep learning; Computational pathology; Computer-aided diagnosis; Digital pathology |
| Core Tip |
Identifying genetic mutations in cancer patients have been increasingly important because distinctive mutational patterns can be very informative to determine the optimal therapy. This study aimed to investigate the feasibility of mutation prediction for the frequently occurring actionable mutations with colorectal cancer (CRC) whole-slide images. The area under the curves for receiver operating characteristic curves ranged from 0.693 to 0.809 for APC, KRAS, PIK3CA, SMAD4, and TP53, showing the potential for deep learning-based mutation prediction in the CRC pathology images. Furthermore, the prediction performance can be enhanced with the expansion of datasets. |
| Publish Date |
2020-10-28 08:26 |
| Citation |
Jang HJ, Lee A, Kang J, Song IH, Lee SH. Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning. World J Gastroenterol 2020; 26(40): 6207-6223 |
| URL |
https://www.wjgnet.com/1007-9327/full/v26/i40/6207.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v26.i40.6207 |
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