Open AccessPrediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach
Author(s) -
HyunJong Jang,
Ahwon Lee,
Jun Kang,
In Hye Song,
Sung Hak Lee
Publication year - 2021
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.v27.i44.7687
Subject(s) - kras , receiver operating characteristic , mutation , artificial intelligence , classifier (uml) , cancer , colorectal cancer , computational biology , biology , computer science , gene , genetics , machine learning
Studies correlating specific genetic mutations and treatment response are ongoing to establish an effective treatment strategy for gastric cancer (GC). To facilitate this research, a cost- and time-effective method to analyze the mutational status is necessary. Deep learning (DL) has been successfully applied to analyze hematoxylin and eosin (H and E)-stained tissue slide images.