Imprecision in automated volume measurements of pulmonary nodules and its effect on the level of uncertainty in volume doubling time estimation

Chest. 2009 Jun;135(6):1580-1587. doi: 10.1378/chest.08-2040. Epub 2009 Jan 13.


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.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Confidence Intervals
  • Humans
  • Linear Models
  • Lung Neoplasms / diagnostic imaging
  • Lung Neoplasms / pathology*
  • Pattern Recognition, Automated
  • Phantoms, Imaging
  • Radiographic Image Interpretation, Computer-Assisted*
  • Sensitivity and Specificity
  • Solitary Pulmonary Nodule / diagnostic imaging*
  • Solitary Pulmonary Nodule / pathology*
  • Tomography, X-Ray Computed / methods*
  • Tumor Burden