Data-driven decision support for radiologists: re-using the National Lung Screening Trial dataset for pulmonary nodule management.

Authors

Morrison JJ, Hostetter J, Wang K, Siegel EL

Affiliations

• Department of Radiology, University of Maryland, 22 S. Greene St., Baltimore, MD, 21201, USA, jjmorrison@gmail.com.

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.