Journal of Digital Imaging

, Volume 31, Issue 2, pp 185–192 | Cite as

Automated Radiology Report Summarization Using an Open-Source Natural Language Processing Pipeline

Article

Abstract

Diagnostic radiologists are expected to review and assimilate findings from prior studies when constructing their overall assessment of the current study. Radiology information systems facilitate this process by presenting the radiologist with a subset of prior studies that are more likely to be relevant to the current study, usually by comparing anatomic coverage of both the current and prior studies. It is incumbent on the radiologist to review the full text report and/or images from those prior studies, a process that is time-consuming and confers substantial risk of overlooking a relevant prior study or finding. This risk is compounded when patients have dozens or even hundreds of prior imaging studies. Our goal is to assess the feasibility of natural language processing techniques to automatically extract asserted and negated disease entities from free-text radiology reports as a step towards automated report summarization. We compared automatically extracted disease mentions to a gold-standard set of manual annotations for 50 radiology reports from CT abdomen and pelvis examinations. The automated report summarization pipeline found perfect or overlapping partial matches for 86% of the manually annotated disease mentions (sensitivity 0.86, precision 0.66, accuracy 0.59, F1 score 0.74). The performance of the automated pipeline was good, and the overall accuracy was similar to the interobserver agreement between the two manual annotators.

Keywords

NLP Report summarization Data extraction Radiology report 

References

  1. 1.
    Cai et al.: NLP technologies in radiology research and clinical applications. Radiographics 36(1):176–191, 2016CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Bozkurt S, Lipson JA, Senol U, Rubin DL: Automatic abstraction of imaging observations with their characteristics from mammography reports. J Am Med Inform Assoc 22(e1):e81–e92, 2015.  https://doi.org/10.1136/amiajnl-2014-003009 Erratum in: J Am Med Inform Assoc. 2015 Sep;22(5):1112. PubMedGoogle Scholar
  3. 3.
    Pham AD, Névéol A, Lavergne T, Yasunaga D, Clément O, Meyer G, Morello R, Burgun A: Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings. BMC Bioinformatics 15:266, 2014.  https://doi.org/10.1186/1471-2105-15-266 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Hassanpour S, Langlotz CP: Information extraction from multi-institutional radiology reports. Artif Intell Med 66:29–39, 2016CrossRefPubMedGoogle Scholar
  5. 5.
    Albright D, Lanfranchi A, Fredriksen A et al.: Towards comprehensive syntactic and semantic annotations of the clinical narrative. J Am Med Inform Assoc 20:922–930, 2013CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Zheng J, Chapman WW, Miller TA, Lin C, Crowley RS, Savova GK: A system for coreference resolution for the clinical narrative. J Am Med Inform Assoc 19:660–667, 2012CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Savova GK, Masanz JJ, Ogren PV et al.: Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 17:507–513, 2010CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Stenetorp P, Pyysalo S, Topić G, Ohta T, Ananiadou S, Tsujii J. BRAT: a web-based tool for NLP-assisted text annotation. In: 13th Conference of the European Chapter of the Association for Computational Linguistics. Avignon, France: Association for Computational Linguistics, 2012:102–107Google Scholar
  9. 9.
    Wu ST, Sohn S, Ravikumar KE et al.: Automated chart review for asthma cohort identification using natural language processing: an exploratory study. Ann Allergy Asthma Immunol 111:364–369, 2013CrossRefPubMedGoogle Scholar
  10. 10.
    Ni Y, Wright J, Perentesis J et al.: Increasing the efficiency of trial-patient matching: automated clinical trial eligibility pre-screening for pediatric oncology patients. BMC Med Inform Decis Mak 15:28, 2015CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Mehrabi S, Krishnan A, Sohn S, Roch AM, Schmidt H, Kesterson J, Beesley C, Dexter P, Max Schmidt C, Liu H, Palakal M: DEEPEN: a negation detection system for clinical text incorporating dependency relation into NegEx. J Biomed Inform. 54:213–219, 2015CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2017

Authors and Affiliations

  1. 1.Department of RadiologyUniversity of California Davis Health SystemSacramentoUSA

Personalised recommendations