Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach.

Journal: World journal of gastroenterology
PMID: 34908807

Abstract

BACKGROUND: 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.

Authors

  • Hyun-Jong Jang
    Department of Physiology, Department of Biomedicine & Health Sciences, Catholic Neuroscience Institute, College of Medicine, The Catholic University of Korea, Seoul, 06591, South Korea.
  • Ahwon Lee
    Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, South Korea.
  • Jun Kang
    Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, South Korea.
  • In Hye Song
    Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Sung Hak Lee
    Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, South Korea. hakjjang@catholic.ac.kr.

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