Basic Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 28, 2020; 26(40): 6207-6223
Published online Oct 28, 2020. doi: 10.3748/wjg.v26.i40.6207
Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning
Hyun-Jong Jang, Ahwon Lee, J Kang, In Hye Song, Sung Hak Lee
Hyun-Jong Jang, Department of Physiology, Department of Biomedicine and Health Sciences, Catholic Neuroscience Institute, The Catholic University of Korea, Seoul 06591, South Korea
Ahwon Lee, J Kang, In Hye Song, Sung Hak Lee, Department of Hospital Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, South Korea
Author contributions: Jang HJ and Lee SH designed research; Lee SH collected material and clinical data from patients; Lee A, Kang J, Song IH and Lee SH performed the assays; Jang HJ, Lee A, Kang J, Song IH and Lee SH analyzed data; Jang HJ and Lee SH wrote the paper.
Supported by Research Fund of Seoul St. Mary’s Hospital made in the program year of 2018.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Board of the College of Medicine at the Catholic University of Korea, No. KC19SESI0787.
Conflict-of-interest statement: The authors declare that they have no conflicts of interest.
Data sharing statement: No additional data are available.
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/
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
Received: June 28, 2020
Peer-review started: June 28, 2020
First decision: July 28, 2020
Revised: August 9, 2020
Accepted: September 25, 2020
Article in press: September 25, 2020
Published online: October 28, 2020
Processing time: 122 Days and 0.9 Hours
Core Tip

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.