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Using Natural Language Processing of Free-Text Radiology Reports to Identify Type 1 Modic Endplate Changes

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Abstract

Electronic medical record (EMR) systems provide easy access to radiology reports and offer great potential to support quality improvement efforts and clinical research. Harnessing the full potential of the EMR requires scalable approaches such as natural language processing (NLP) to convert text into variables used for evaluation or analysis. Our goal was to determine the feasibility of using NLP to identify patients with Type 1 Modic endplate changes using clinical reports of magnetic resonance (MR) imaging examinations of the spine. Identifying patients with Type 1 Modic change who may be eligible for clinical trials is important as these findings may be important targets for intervention. Four annotators identified all reports that contained Type 1 Modic change, using N = 458 randomly selected lumbar spine MR reports. We then implemented a rule-based NLP algorithm in Java using regular expressions. The prevalence of Type 1 Modic change in the annotated dataset was 10%. Results were recall (sensitivity) 35/50 = 0.70 (95% confidence interval (C.I.) 0.52–0.82), specificity 404/408 = 0.99 (0.97–1.0), precision (positive predictive value) 35/39 = 0.90 (0.75–0.97), negative predictive value 404/419 = 0.96 (0.94–0.98), and F1-score 0.79 (0.43–1.0). Our evaluation shows the efficacy of rule-based NLP approach for identifying patients with Type 1 Modic change if the emphasis is on identifying only relevant cases with low concern regarding false negatives. As expected, our results show that specificity is higher than recall. This is due to the inherent difficulty of eliciting all possible keywords given the enormous variability of lumbar spine reporting, which decreases recall, while availability of good negation algorithms improves specificity.

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References

  1. Cai T et al.: Natural Language Processing Technologies in Radiology Research and Clinical Applications. Radiographics 36:176–191, 2016

    Article  PubMed  PubMed Central  Google Scholar 

  2. Langlotz CP: Structured radiology reporting: are we there yet? Radiology 253:23–25, 2009

    Article  PubMed  Google Scholar 

  3. Burnside ES et al.: The ACR BI-RADS experience: learning from history. J Am Coll Radiol 6:851–860, 2009

    Article  PubMed  PubMed Central  Google Scholar 

  4. Hirschberg J, Manning CD: Advances in natural language processing. Science 349:261–266, 2015

    Article  CAS  PubMed  Google Scholar 

  5. Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG: A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform 34:301–310, 2001

    Article  CAS  PubMed  Google Scholar 

  6. Harkema H, Dowling JN, Thornblade T, Chapman WW: ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports. J Biomed Inform 42:839–851, 2009

    Article  PubMed  PubMed Central  Google Scholar 

  7. Modic MT, Steinberg PM, Ross JS, Masaryk TJ, Carter JR: Degenerative disk disease: assessment of changes in vertebral body marrow with MR imaging. Radiology 166:193–199, 1988

    Article  CAS  PubMed  Google Scholar 

  8. Jensen TS, Karppinen J, Sorensen JS, Niinimäki J, Leboeuf-Yde C: Vertebral endplate signal changes (Modic change): a systematic literature review of prevalence and association with non-specific low back pain. Eur Spine J 17:1407–1422, 2008

    Article  PubMed  PubMed Central  Google Scholar 

  9. Jarvik JG et al.: Back pain in seniors: the back pain outcomes using longitudinal data (BOLD) cohort baseline data. BMC Musculoskelet Disord 15:134, 2014

    Article  PubMed  PubMed Central  Google Scholar 

  10. Jarvik JG et al.: Lumbar imaging with reporting of epidemiology (LIRE)—protocol for a pragmatic cluster randomized trial. Contemp Clin Trials 45:157–163, 2015

    Article  PubMed  PubMed Central  Google Scholar 

  11. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG: Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 42:377–381, 2009

    Article  PubMed  Google Scholar 

  12. Pons E, Braun LM, Hunink MG, Kors JA: Natural language processing in radiology: a systematic review. Radiology 279:329–343, 2016

    Article  PubMed  Google Scholar 

  13. R Core Team: R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing, 2013

    Google Scholar 

  14. Landis JR, Koch GG: The measurement of observer agreement for categorical data. Biometrics 33:159–174, 1977

    Article  CAS  PubMed  Google Scholar 

  15. Fardon DF, Williams AL, Dohring EJ, Murtagh FR, Gabriel Rothman SL, Sze GK: Lumbar disc nomenclature: version 2.0: Recommendations of the combined task forces of the North American Spine Society, the American Society of Spine Radiology and the American Society of Neuroradiology. Spine J 14:2525–2545, 2014

    Article  PubMed  Google Scholar 

  16. Cheng LT, Zheng J, Savova GK, Erickson BJ: Discerning tumor status from unstructured MRI reports—completeness of information in existing reports and utility of automated natural language processing. J Digit Imaging 23:119–132, 2010

    Article  PubMed  Google Scholar 

  17. Lakhani P, Kim W, Langlotz CP: Automated detection of critical results in radiology reports. J Digit Imaging 25:30–36, 2012

    Article  PubMed  Google Scholar 

  18. Wei Q, Dunbrack RL: The role of balanced training and testing data sets for binary classifiers in bioinformatics. PLoS One 8:e67863, 2013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work is supported by the National Institutes of Health (NIH) Common Fund, through a cooperative agreement (5UH3AR06679) from the Office of Strategic Coordination within the Office of the NIH Director. The views presented here are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health.

BOLD funding through AHRQ grant no. 1R01HS022972.

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Correspondence to Jeffrey G. Jarvik.

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Huhdanpaa, H.T., Tan, W.K., Rundell, S.D. et al. Using Natural Language Processing of Free-Text Radiology Reports to Identify Type 1 Modic Endplate Changes. J Digit Imaging 31, 84–90 (2018). https://doi.org/10.1007/s10278-017-0013-3

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