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11/25/2021 8:04:31 AM | Browse: 37 | Download: 61
Publication Name World Journal of Gastroenterology
Manuscript ID 70291
Country/Territory South Korea
2021-07-29 02:33
Peer-Review Started
2021-07-29 02:37
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Return for Revision
2021-08-19 08:59
2021-09-05 14:46
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2021-11-15 03:34
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Accepted by Company Editor-in-Chief
2021-11-15 06:52
Articles in Press
2021-11-15 06:52
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Edit the Manuscript by Language Editor
Typeset the Manuscript
2021-11-18 03:21
Publish the Manuscript Online
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
Permissions For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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
Manuscript Source Unsolicited Manuscript
All Author List Hyun-Jong Jang, Ahwon Lee, Jun Kang, In Hye Song and Sung Hak Lee
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
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
Full Article (PDF) WJG-27-7687.pdf
Full Article (Word) WJG-27-7687.docx
Manuscript File 70291_Auto_Edited.docx
Answering Reviewers 70291-Answering reviewers.pdf
Audio Core Tip 70291-Audio core tip.m4a
Biostatistics Review Certificate 70291-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form 70291-Conflict-of-interest statement.pdf
Copyright License Agreement 70291-Copyright license agreement.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s) 70291-Grant application form(s).pdf
Institutional Animal Care and Use Committee Approval Form or Document 70291-Institutional animal care and use committee statement.pdf
Institutional Review Board Approval Form or Document 70291-Institutional review board statement.pdf
Non-Native Speakers of English Editing Certificate 70291-Language certificate.pdf
Supplementary Material 70291-Supplementary material.pdf
Peer-review Report 70291-Peer-review(s).pdf
Scientific Misconduct Check 70291-Bing-Wang LL-2.png
Scientific Editor Work List 70291-Scientific editor work list.pdf