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

      European Journal of Radiology
      Algorithms, Animals, Calibration, Disease Models, Animal, Humans, Image Processing, Computer-Assisted, methods, Lung Diseases, radiography, Observer Variation, Radiology, education, Radiology Information Systems, Software, Swine, Tomography, Spiral Computed, statistics & numerical data

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          Abstract

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

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