Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers.

Journal: Briefings in bioinformatics
Published Date: May 19, 2023

Abstract

PURPOSE: Evaluation of genetic mutations in cancers is important because distinct mutational profiles help determine individualized drug therapy. However, molecular analyses are not routinely performed in all cancers because they are expensive, time-consuming and not universally available. Artificial intelligence (AI) has shown the potential to determine a wide range of genetic mutations on histologic image analysis. Here, we assessed the status of mutation prediction AI models on histologic images by a systematic review.

Authors

  • Mohammad Rizwan Alam
    Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.
  • Kyung Jin Seo
    Department of Hospital Pathology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Jamshid Abdul-Ghafar
    Department of Pathology and Clinical Laboratory, French Medical Institute for Mothers and Children (FMIC), Kabul, Afghanistan. jamshid.jalal@fmic.org.af.
  • Kwangil Yim
    Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of 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.
  • 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.
  • Chan Kwon Jung
    Department of Biomedicine and Health Sciences, The Catholic University of Korea, Seoul, 06591, Republic of Korea. ckjung@catholic.ac.kr.
  • Yosep Chong
    Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, 271, Cheonbo-ro, Uijeongbu, 11765, Gyeonggi-do, Republic of Korea. ychong@catholic.ac.kr.

Keywords

External Resources

Popular Topics
Recent Journals