Publication Name	World Journal of Gastroenterology
Manuscript ID	57895
Country	South Korea

Category	Pathology
Manuscript Type	Basic Study
Article Title	Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning
Manuscript Source	Invited Manuscript
All Author List	Hyun-Jong Jang, Ahwon Lee, J Kang, In Hye Song and Sung Hak Lee
Funding Agency and Grant Number	Funding Agency Grant Number Research Fund of Seoul St. Mary’s Hospital made in the program year of 2018
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.
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

Received	2020-06-28 07:33
Peer-Review Started	2020-06-28 07:34
To Make the First Decision
Return for Revision	2020-07-28 21:19
Revised	2020-08-09 04:55
Second Decision	2020-09-24 12:26
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2020-09-25 05:30
Articles in Press	2020-09-25 05:30
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2020-10-20 10:00

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/
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