Open AccessAutomatic extraction of imaging observation and assessment categories from breast magnetic resonance imaging reports with natural language processing
Author(s) -
Yi Liu,
Lina Zhu,
Qing Liu,
Chao Han,
Xiaodong Zhang,
Xiaoying Wang
Publication year - 2019
Publication title -
chinese medical journal/chinese medical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 63
eISSN - 2542-5641
pISSN - 0366-6999
DOI - 10.1097/cm9.0000000000000301
Subject(s) - breast imaging , artificial intelligence , computer science , natural language processing , breast mri , magnetic resonance imaging , bi rads , lexicon , data extraction , precision and recall , mammography , medicine , machine learning , information retrieval , radiology , breast cancer , medline , cancer , political science , law
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.