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Data-Driven Decision Support for Radiologists: Re-using the National Lung Screening Trial Dataset for Pulmonary Nodule Management

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Abstract

Real-time mining of large research trial datasets enables development of case-based clinical decision support tools. Several applicable research datasets exist including the National Lung Screening Trial (NLST), a dataset unparalleled in size and scope for studying population-based lung cancer screening. Using these data, a clinical decision support tool was developed which matches patient demographics and lung nodule characteristics to a cohort of similar patients. The NLST dataset was converted into Structured Query Language (SQL) tables hosted on a web server, and a web-based JavaScript application was developed which performs real-time queries. JavaScript is used for both the server-side and client-side language, allowing for rapid development of a robust client interface and server-side data layer. Real-time data mining of user-specified patient cohorts achieved a rapid return of cohort cancer statistics and lung nodule distribution information. This system demonstrates the potential of individualized real-time data mining using large high-quality clinical trial datasets to drive evidence-based clinical decision-making.

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References

  1. American Cancer Society. Cancer Facts & Figures 2013. Available from: http://www.cancer.org/research/cancerfactsfigures/cancerfactsfigures/cancer-facts-figures 2013. Accessed 14 February 2014.

  2. National Cancer Institute. Surveillance, Epidemiology, and End-Results Program. Available from: http://seer.cancer.gov/statfacts/html/lungb.html. Accessed 25 December 2013.

  3. Aberle DR, DeMello S, Berg CD, Black WC, Brewer B, Church TR, et al: Results of the two incidence screenings in the national lung screening trial. N Engl J Med 369:920–31, 2013

    Article  CAS  PubMed  Google Scholar 

  4. United States Preventative Services Task Force. Lung Cancer Screening 2013 Available from: http://www.uspreventiveservicestaskforce.org/uspstf/uspslung.htm. Accessed 19 December 2013.

  5. Tota JE, Ramanakumar AV, Franco EL. Lung cancer screening: review and performance comparison under different risk scenarios. Lung 192:55–63, 2014

    Article  PubMed  Google Scholar 

  6. Reiner BI, Siegel EL: The clinical imperative of medical imaging informatics. J Digit Imaging 22:345–7, 2009

    Article  PubMed Central  PubMed  Google Scholar 

  7. Chen W, Liu J, Chen Q, Li W, Xiong Z, Long X: Bayes analysis in clinical decision-making for solitary pulmonary nodules. Zhong Nan Da Xue Xue Bao Yi Xue Ban 34:401–5, 2009

    Article  PubMed  Google Scholar 

  8. Sesen MB, Nicholson AE, Banares-Alcantara R, Kadir T, Brady M: Bayesian networks for clinical decision support in lung cancer care. PloS one 8:e82349, 2013

    Article  PubMed Central  PubMed  Google Scholar 

  9. Raja AS, Ip IK, Prevedello LM, Sodickson AD, Farkas C, Zane RD, et al: Effect of computerized clinical decision support on the use and yield of CT pulmonary angiography in the emergency department. Radiology 262:468–74, 2012

    Article  PubMed Central  PubMed  Google Scholar 

  10. Canonical LLC. Ubuntu Linux. Available from: http://www.ubuntu.com. Accessed 25 December 2013.

  11. DigitalOcean Inc. DigitalOcean. Available from: http://www.digitalocean.com. Accessed 25 December 2013.

  12. NGINX Inc. NGINX. Available from: http://www.nginx.org. Accessed 25 December 2013.

  13. Oracle Inc. MySQL. Available from: http://www.mysql.com. Accessed 25 December 2013.

  14. Joyent Inc. NodeJS. Available from: http://www.nodejs.org. Accessed 25 December 2013.

  15. Geisendorfer F. node-mysql. Available from: https://npmjs.org/package/mysql. Accessed 26 December 2013.

  16. Otto M, Thornton J. Bootstrap. Available from: http://www.getbootstrap.com. Accessed 25 December 2013.

  17. Google Inc. AngularJS. Available from: http://www.angularjs.org. Accessed 25 December 2013.

  18. The JQuery Foundation. jQuery. Available from: http://www.jquery.com. Accessed 25 December 2013.

  19. Kahn CE, Jr: Artificial intelligence in radiology: decision support systems. Radiographics 14:849–61, 1994

    Article  PubMed  Google Scholar 

  20. Boroczky L, Simpson M, Abe H, Drysdale J: Observer study of a prototype clinical decision support system for breast cancer diagnosis using dynamic contrast-enhanced MRI. AJR American J Roentgenol, 200:277–83, 2013

    Article  Google Scholar 

  21. Wang KC, Jeanmenne A, Weber GM, Thawait SK, Carrino JA: An online evidence-based decision support system for distinguishing benign from malignant vertebral compression fractures by magnetic resonance imaging feature analysis. J Digit Imaging 24:507–15, 2011

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgments

The authors thank the National Cancer Institute for access to NCI’s data collected by the National Lung Screening Trial. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI.

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Authors and Affiliations

  1. Department of Radiology, University of Maryland, 22 S. Greene St., Baltimore, MD, 21201, USA

    James J. Morrison, Jason Hostetter & Eliot L. Siegel

  2. Baltimore VAMC, Baltimore, MD, 21201, USA

    Kenneth Wang & Eliot L. Siegel

Authors
  1. James J. Morrison
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  2. Jason Hostetter
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  3. Kenneth Wang
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  4. Eliot L. Siegel
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Correspondence to James J. Morrison.

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Morrison, J.J., Hostetter, J., Wang, K. et al. Data-Driven Decision Support for Radiologists: Re-using the National Lung Screening Trial Dataset for Pulmonary Nodule Management. J Digit Imaging 28, 18–23 (2015). https://doi.org/10.1007/s10278-014-9720-1

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  • DOI: https://doi.org/10.1007/s10278-014-9720-1

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