Automated extraction of information of lung cancer staging from unstructured reports of PET-CT interpretation: natural language processing with deep-learning.

Journal: BMC medical informatics and decision making
Published Date: Sep 1, 2022

Abstract

BACKGROUND: Extracting metastatic information from previous radiologic-text reports is important, however, laborious annotations have limited the usability of these texts. We developed a deep-learning model for extracting primary lung cancer sites and metastatic lymph nodes and distant metastasis information from PET-CT reports for determining lung cancer stages.

Authors

  • Hyung Jun Park
    Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Namu Park
    Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA, USA.
  • Jang Ho Lee
    Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea.
  • Myeong Geun Choi
    Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Mokdong Hospital, Ewha Womans University, Seoul, Republic of Korea.
  • Jin-Sook Ryu
    Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Min Song
    Library and Information Science, Yonsei University, Seoul, South Korea.
  • Chang-Min Choi
    Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

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