Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports |
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Authors: | Hakan Bulu Dorothy A Sippo Janie M Lee Elizabeth S Burnside Daniel L Rubin |
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Institution: | 1.Department of Radiology and Department of Biomedical Data Science, Medical School Office Building (MSOB),Stanford University,Stanford,USA;2.Department of Radiology, Avon Comprehensive Breast Evaluation Center,Massachusetts General Hospital,Boston,USA;3.Department of Radiology, Seattle Cancer Care Alliance,University of Washington,Seattle,USA;4.Department of Radiology, E3/311 Clinical Science Center,University of Wisconsin School of Medicine and Public Health,Madison,USA |
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Abstract: | After years of development, the RadLex terminology contains a large set of controlled terms for the radiology domain, but gaps still exist. We developed a data-driven approach to discover new terms for RadLex by mining a large corpus of radiology reports using natural language processing (NLP) methods. Our system, developed for mammography, discovers new candidate terms by analyzing noun phrases in free-text reports to extend the mammography part of RadLex. Our NLP system extracts noun phrases from free-text mammography reports and classifies these noun phrases as “Has Candidate RadLex Term” or “Does Not Have Candidate RadLex Term.” We tested the performance of our algorithm using 100 free-text mammography reports. An expert radiologist determined the true positive and true negative RadLex candidate terms. We calculated precision/positive predictive value and recall/sensitivity metrics to judge the system’s performance. Finally, to identify new candidate terms for enhancing RadLex, we applied our NLP method to 270,540 free-text mammography reports obtained from three academic institutions. Our method demonstrated precision/positive predictive value of 0.77 (159/206 terms) and a recall/sensitivity of 0.94 (159/170 terms). The overall accuracy of the system is 0.80 (235/293 terms). When we ran our system on the set of 270,540 reports, it found 31,800 unique noun phrases that are potential candidates for RadLex. Our data-driven approach to mining radiology reports can identify new candidate terms for expanding the breast imaging lexicon portion of RadLex and may be a useful approach for discovering new candidate terms from other radiology domains. |
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