Improved Interobserver Agreement on Lung-RADS Classification of Solid Nodules Using Semiautomated CT Volumetry.

Background Classification of lung cancer screening CT scans depends on measurement of lung nodule size. Information about interobserver agreement is limited. Purpose To assess interobserver agreement in the measurements and American College of Radiology Lung CT Screening Reporting and Data System (Lung-RADS) classifications of solid lung nodules detected at lung cancer screening using manual measurements of average diameter and computer-aided semiautomated measurements of average diameter and volume (CT volumetry). Materials and Methods Two radiologists and one radiology resident retrospectively measured lung nodules from screening CT scans obtained between September 2016 and June 2018 with a Lung-RADS (version 1.0) classification of 2, 3, 4A, or 4B in the clinical setting. Average manual diameter and semiautomated computer-aided diameter and volume measurements were converted to the corresponding Lung-RADS categories. Interobserver agreement in raw measurements was assessed using intraclass correlation and Bland-Altman indexes, and interobserver agreement in Lung-RADS classification was assessed using bi-rater κ. Results One hundred twenty patients (mean age, 63 years ± 6 [standard deviation]; 67 women) were evaluated. All manual, semiautomated diameter, and semiautomated volume measurements were obtained by all three readers in 120 of 147 nodules (82%). Intraclass correlation coefficients were greater than or equal to 0.95 for all reader pairs using all measurement methods and were highest using volumetry. Bias and 95% limits of agreement for average diameter were smaller with semiautomated measurements than with manual measurements. κ values across all Lung-RADS classifications were greater than or equal to 0.81, with the lowest being for manual measurements and the highest being for volumetric measurements. Forty-three of 120 (36%) of the nodules were classified into a lower Lung-RADS category on the basis of volumetry compared with using manual diameter measurements by at least one reader, whereas the reverse occurred for four of 120 (3%) of the nodules. Conclusion Interobserver agreement was high with manual diameter measurements and increased with semiautomated CT volumetric measurements. Semiautomated CT volumetry enabled classification of more nodules into lower Lung CT Screening Reporting and Data System categories than manual or semiautomated diameter measurements. © RSNA, 2020 See also the editorial by Nishino in this issue.