Automatic extraction of imaging observation and assessment categories from breast magnetic resonance imaging reports with natural language processing.

Journal: Chinese medical journal
Published Date: Jul 20, 2019

Abstract

BACKGROUND: Structured reports are not widely used and thus most reports exist in the form of free text. The process of data extraction by experts is time-consuming and error-prone, whereas data extraction by natural language processing (NLP) is a potential solution that could improve diagnosis efficiency and accuracy. The purpose of this study was to evaluate an NLP program that determines American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors and final assessment categories from breast magnetic resonance imaging (MRI) reports.

Authors

  • Yi Liu
    Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China.
  • Li-Na Zhu
    Department of Chemistry, Tianjin University, Tianjin 300072, PR China; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300071, PR China. Electronic address: linazhu@tju.edu.cn.
  • Qing Liu
    School of Chemistry and Chemical Engineering, Shandong University of Technology, 255049, Zibo, PR China.
  • Chao Han
    School of Software Engineering, South China University of Technology, Guangzhou, P. R. China.
  • Xiao-Dong Zhang
    Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China. zxdon11@163.com.
  • Xiao-Ying Wang

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