Imprecision in automated volume measurements of pulmonary nodules and its effect on the level of uncertainty in volume doubling time estimation.
BACKGROUND
Detection of small indeterminate pulmonary nodules (4 to 10 mm in diameter) in clinical practice is increasing, largely because of increased utilization and improved imaging technology. Although there currently exists software for CT scan machines that automate nodule volume estimation, the imprecision associated with volume estimates is particularly poor for nodules < or = 6 mm in diameter, with greater imprecision associated with increasing CT scan slice thickness. This study examined the effects of the volume estimation error associated with four CT scan slice thicknesses (0.625, 1.25, 2.50, and 5.00 mm) on estimates of volume doubling time (VDT) for solid nodules of various sizes.


METHODS
Data reflecting the accuracy of 1,624 automated volume estimations were obtained from experiments incorporating volume estimation software, performed on a commercially available lung phantom. These data informed mathematical simulations that were used to estimate imprecision around VDT estimates for hypothetical pairs of volume estimates for a given solid pulmonary nodule observed at different time points.


RESULTS
The confidence intervals around the VDT estimates were extremely wide for 2.50- and 5.00-mm slice thicknesses, often encompassing values traditionally associated with both benignity and malignity for simulated 1- and 2-mm growths in diameter.


CONCLUSIONS
Because of the inaccuracy in automated volume estimation, the confidence a clinician should have in estimating VDT should be highly dependent on the degree of observed growth and on the CT scan slice thickness. The performance of CT scanners with slice thicknesses of > or = 2.5 mm for assessing growth in pulmonary nodules is essentially inadequate for 1-mm changes in nodule diameter.
Nietert
Paul J
PJ
Department of Biostatistics, Bioinformatics, and Epidemiology, Division of Pulmonary and Critical Care, Medical University of South Carolina, Charleston, SC. Electronic address: nieterpj@musc.edu.
Ravenel
James G
JG
Department of Radiology, Division of Pulmonary and Critical Care, Medical University of South Carolina, Charleston, SC.
Leue
William M
WM
General Electric Global Research, Niskayuna, NY.
Miller
James V
JV
General Electric Global Research, Niskayuna, NY.
Taylor
Katherine K
KK
Department of Medicine, Division of Pulmonary and Critical Care, Medical University of South Carolina, Charleston, SC.
Garrett-Mayer
Elizabeth S
ES
Department of Biostatistics, Bioinformatics, and Epidemiology, Division of Pulmonary and Critical Care, Medical University of South Carolina, Charleston, SC.
Silvestri
Gerard A
GA
Department of Medicine, Division of Pulmonary and Critical Care, Medical University of South Carolina, Charleston, SC.
D016001:Confidence Intervals; D006801:Humans; D016014:Linear Models; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D010363:Pattern Recognition, Automated; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging* / Q000473:pathology*; D014057:Tomography, X-Ray Computed / Q000379:methods*; D047368:Tumor Burden