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.
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.
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.