Toshimasa J Clark1, Thomas F Flood2, Suresh T Maximin3, Peter B Sachs2. 1. University of Colorado Denver, Aurora, Colorado. Electronic address: toshimasa.clark@ucdenver.edu. 2. University of Colorado Denver, Aurora, Colorado. 3. University of Washington and VA Puget Sound Health Care System, Seattle, Washington.
Abstract
PURPOSE: 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.
PURPOSE: 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.
Authors: Paul F Pinsky; David S Gierada; William Black; Reginald Munden; Hrudaya Nath; Denise Aberle; Ella Kazerooni Journal: Ann Intern Med Date: 2015-04-07 Impact factor: 25.391
Authors: Onno M Mets; Kaman Chung; Pieter Zanen; Ernst T Scholten; Wouter B Veldhuis; Bram van Ginneken; Mathias Prokop; Cornelia M Schaefer-Prokop; Pim A de Jong Journal: Eur Respir J Date: 2018-04-12 Impact factor: 16.671
Authors: Eric C Ehman; Geoffrey B Johnson; Javier E Villanueva-Meyer; Soonmee Cha; Andrew Palmera Leynes; Peder Eric Zufall Larson; Thomas A Hope Journal: J Magn Reson Imaging Date: 2017-03-30 Impact factor: 4.813
Authors: Douglas E Wood; Ella A Kazerooni; Scott L Baum; George A Eapen; David S Ettinger; Lifang Hou; David M Jackman; Donald Klippenstein; Rohit Kumar; Rudy P Lackner; Lorriana E Leard; Inga T Lennes; Ann N C Leung; Samir S Makani; Pierre P Massion; Peter Mazzone; Robert E Merritt; Bryan F Meyers; David E Midthun; Sudhakar Pipavath; Christie Pratt; Chakravarthy Reddy; Mary E Reid; Arnold J Rotter; Peter B Sachs; Matthew B Schabath; Mark L Schiebler; Betty C Tong; William D Travis; Benjamin Wei; Stephen C Yang; Kristina M Gregory; Miranda Hughes Journal: J Natl Compr Canc Netw Date: 2018-04 Impact factor: 11.908