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Discerning Tumor Status from Unstructured MRI Reports—Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing

Journal of digital imaging, 2010-04, Vol.23 (2), p.119-132 [Peer Reviewed Journal]

Society for Imaging Informatics in Medicine 2009 ;Society for Imaging Informatics in Medicine 2010 ;ISSN: 0897-1889 ;EISSN: 1618-727X ;DOI: 10.1007/s10278-009-9215-7 ;PMID: 19484309

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  • Title:
    Discerning Tumor Status from Unstructured MRI Reports—Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing
  • Author: Cheng, Lionel T. E. ; Zheng, Jiaping ; Savova, Guergana K. ; Erickson, Bradley J.
  • Subjects: Automatic Data Processing - utilization ; Female ; Humans ; Imaging ; Information Storage and Retrieval - methods ; Magnetic Resonance Imaging - methods ; Magnetic Resonance Imaging - standards ; Male ; Medical Records Systems, Computerized ; Medicine ; Medicine & Public Health ; Natural Language Processing ; Neoplasms - diagnosis ; Radiology ; Radiology Information Systems ; Reproducibility of Results ; Sensitivity and Specificity
  • Is Part Of: Journal of digital imaging, 2010-04, Vol.23 (2), p.119-132
  • Description: Information in electronic medical records is often in an unstructured free-text format. This format presents challenges for expedient data retrieval and may fail to convey important findings. Natural language processing (NLP) is an emerging technique for rapid and efficient clinical data retrieval. While proven in disease detection , the utility of NLP in discerning disease progression from free-text reports is untested. We aimed to (1) assess whether unstructured radiology reports contained sufficient information for tumor status classification; (2) develop an NLP-based data extraction tool to determine tumor status from unstructured reports; and (3) compare NLP and human tumor status classification outcomes. Consecutive follow-up brain tumor magnetic resonance imaging reports (2000–­2007) from a tertiary center were manually annotated using consensus guidelines on tumor status. Reports were randomized to NLP training (70%) or testing (30%) groups. The NLP tool utilized a support vector machines model with statistical and rule-based outcomes. Most reports had sufficient information for tumor status classification, although 0.8% did not describe status despite reference to prior examinations. Tumor size was unreported in 68.7% of documents, while 50.3% lacked data on change magnitude when there was detectable progression or regression. Using retrospective human classification as the gold standard, NLP achieved 80.6% sensitivity and 91.6% specificity for tumor status determination (mean positive predictive value, 82.4%; negative predictive value, 92.0%). In conclusion, most reports contained sufficient information for tumor status determination, though variable features were used to describe status. NLP demonstrated good accuracy for tumor status classification and may have novel application for automated disease status classification from electronic databases.
  • Publisher: New York: Springer-Verlag
  • Language: English
  • Identifier: ISSN: 0897-1889
    EISSN: 1618-727X
    DOI: 10.1007/s10278-009-9215-7
    PMID: 19484309
  • Source: GFMER Free Medical Journals
    MEDLINE
    PubMed Central
    Alma/SFX Local Collection
    ProQuest Central
    Springer Nature OA Free Journals
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