Open AccessPrediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning
Author(s) -
HyunJong Jang,
Ahwon Lee,
JunMyung Kang,
In Hye Song,
Sung Hak Lee
Publication year - 2020
Publication title -
world journal of gastroenterology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.427
H-Index - 155
eISSN - 2219-2840
pISSN - 1007-9327
DOI - 10.3748/wjg.v26.i40.6207
Subject(s) - kras , subtyping , receiver operating characteristic , digital pathology , colorectal cancer , artificial intelligence , deep learning , mutation , computational biology , machine learning , cancer , computer science , medicine , bioinformatics , biology , gene , genetics , programming language
Identifying genetic mutations in cancer patients have been increasingly important because distinctive mutational patterns can be very informative to determine the optimal therapeutic strategy. Recent studies have shown that deep learning-based molecular cancer subtyping can be performed directly from the standard hematoxylin and eosin (H&E) sections in diverse tumors including colorectal cancers (CRCs). Since H&E-stained tissue slides are ubiquitously available, mutation prediction with the pathology images from cancers can be a time- and cost-effective complementary method for personalized treatment.