Purpose: To compare the interobserver variability of the unidimensional diameter and volume measurements of pulmonary nodules in an intrascan and interscan analysis using semi-automated segmentation software on ultra-low-dose computed tomography (ULD-CT) and standard dose CT (SD-CT) data.
Materials and methods: In 33 patients with pulmonary nodules, two chest multi-slice CT (MSCT) datasets (1 mm slice thickness; 20 % reconstruction overlap) had been consecutively acquired with an ultra-low dose (120 kV, 5 mAs) and standard dose technique (120 kV, 75 mAs). MSCT data was retrospectively analyzed using the segmentation software OncoTREAT (MeVis, Bremen, Germany, version 1.3). The volume of 229 solid pulmonary nodules included in the analysis as well as the largest diameter according to RECIST (Response Evaluation Criteria for Solid Tumors) were measured by two radiologists. Interobserver variability was calculated and SD-CT and ULD-CT data compared in an intrascan and interscan analysis.
Results: The median nodule diameter (n = 229 nodules) was registered with 8.2 mm (range: 2.8 to 43.6 mm, mean: 10.8 mm). The nodule volume ranged between 0.01 and 49.1 ml (median 0.1 ml, mean 1.5 ml). With respect to interobserver variability, the intrascan analysis did not reveal statistically significant differences (p > 0.05) between ULD-CT and SD-CT with broader limits of agreement for relative differences of RECIST measurements (-31.0 % + 27.0 % mean -2.0 % for SD-CT; -27.0 % + 38.6 %, mean 5.8 % for ULD-CT) than for volume measurements (-9.4 %, 8.0 %, mean 0.7 % for SD-CT; -13 %, 13 %, mean 0.0 % for ULD-CT). The interscan analysis showed broadened 95 % confidence intervals for volume measurements (-26.5 % 29.1 % mean 1.3 %, and -25.2 %, 29.6 %, mean 2.2 %) but yielded comparable limits of agreement for RECIST measurements.
Conclusion: The variability of nodule volumetry assessed by semi-automated segmentation software as well as nodule size determination by RECIST appears to be independent of the acquisition dose in the CT source dataset. This is particularly important regarding size determination of pulmonary nodules in screening trials using low-dose CT data for follow-up imaging.