Lung CT Screening Reporting and Data System Speed and Accuracy Are Increased With the Use of a Semiautomated Computer Application.
OBJECTIVE
The Lung CT Screening Reporting and Data System (Lung-RADS™) is an algorithm that can be used to classify lung nodules in patients with significant smoking histories. It is published in table format but can be implemented as a computer program. The aim of this study was to assess the efficiency and accuracy of the use of a computer program versus the table in categorizing lung nodules.


METHODS
The Lung-RADS algorithm was implemented as a computer program. Through the use of a survey tool, respondents were asked to categorize 13 simulated lung nodules using the computer program and the Lung-RADS table as published. Data were gathered regarding time to completion, accuracy of each nodule's categorization, users' subjective categorization confidence, and users' perceived efficiency using each method.


RESULTS
The use of a computer program to categorize lung nodules resulted in significantly increased interpretation speed (80.8 ± 37.7 vs 156 ± 105 seconds, P < .0001), lung nodule classification accuracy (99.6% vs 76.5%, P < .0001), and perceived confidence and efficiency compared with the use of the table. There were no significant differences in accuracy when comparing thoracic radiologists with the remainder of the group.


CONCLUSIONS
Radiologists were both more efficient and more accurate in lung nodule categorization when using computerized decision support tools. The authors propose that other institutions use computerized implementations of Lung-RADS in the interests of both efficiency and patient outcomes through proper management. Furthermore, they suggest the ACR design future iterations of the Lung-RADS algorithm with computerized decision support in mind.
University of Colorado Denver, Aurora, Colorado. Electronic address: toshimasa.clark@ucdenver.edu.
Toshimasa J
TJ
Clark
University of Colorado Denver, Aurora, Colorado.
Thomas F
TF
Flood
University of Washington and VA Puget Sound Health Care System, Seattle, Washington.
Suresh T
ST
Maximin
University of Colorado Denver, Aurora, Colorado.
Peter B
PB
Sachs
D000465:Algorithms; D003198:Computer Simulation; D003658:Decision Making, Computer-Assisted; D055088:Early Detection of Cancer / Q000379:methods*; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D058996:Quality Improvement; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011873:Radiology Information Systems / Q000592:standards; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging / Q000473:pathology; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014481:United States
Lung-RADS; decision support; lung cancer screening