Interobserver-variability of lung nodule volumetry considering different segmentation algorithms and observer training levels

Eur J Radiol. 2007 Nov;64(2):285-95. doi: 10.1016/j.ejrad.2007.02.031. Epub 2007 Apr 12.


Objective: The aim of this study was to investigate the interobserver variability of CT based diameter and volumetric measurements of artificial pulmonary nodules. A special interest was the consideration of different measurement methods, observer experience and training levels.

Materials and methods: For this purpose 46 artificial small solid nodules were examined in a dedicated ex-vivo chest phantom with multislice-spiral CT (20 mAs, 120 kV, collimation 16 mm x 0.75 mm, table feed 15 mm, reconstructed slice thickness 1mm, reconstruction increment 0.7 mm, intermediate reconstruction kernel). Two observer groups of different radiologic experience (0 and more than 5 years of training, 3 observers each) analysed all lesions with digital callipers and 2 volumetry software packages (click-point depending and robust volumetry) in a semi-automatic and manually corrected mode. For data analysis the variation coefficient (VC) was calculated in per cent for each group and a Wilcoxon test was used for analytic statistics.

Results: Click-point robust volumetry showed with a VC of <0.01% in both groups the smallest interobserver variability. Between experienced and un-experienced observers interobserver variability was significantly different for diameter measurements (p=0.023) but not for semi-automatic and manual corrected volumetry. A significant training effect was revealed for diameter measurements (p=0.003) and semi-automatic measurements of click-point depending volumetry (p=0.007) in the un-experienced observer group.

Conclusions: Compared to diameter measurements volumetry achieves a significantly smaller interobserver variance and advanced volumetry algorithms are independent of observer experience.

Publication types

  • Comparative Study

MeSH terms

  • Algorithms*
  • Animals
  • Calibration
  • Disease Models, Animal
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Lung Diseases / diagnostic imaging*
  • Observer Variation
  • Radiology / education*
  • Radiology Information Systems
  • Software
  • Swine
  • Tomography, Spiral Computed / methods
  • Tomography, Spiral Computed / statistics & numerical data*