Journal of Digital Imaging

, Volume 28, Issue 1, pp 18–23

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

  • James J. Morrison
  • Jason Hostetter
  • Kenneth Wang
  • Eliot L. Siegel
Article

DOI: 10.1007/s10278-014-9720-1

Cite this article as:
Morrison, J.J., Hostetter, J., Wang, K. et al. J Digit Imaging (2015) 28: 18. doi:10.1007/s10278-014-9720-1

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.

Keywords

Decision support Data mining Decision support techniques Web technology 

Copyright information

© Society for Imaging Informatics in Medicine 2014

Authors and Affiliations

  • James J. Morrison
    • 1
  • Jason Hostetter
    • 1
  • Kenneth Wang
    • 2
  • Eliot L. Siegel
    • 1
    • 2
  1. 1.Department of RadiologyUniversity of MarylandBaltimoreUSA
  2. 2.Baltimore VAMCBaltimoreUSA