title: Semiautomatic lymph node segmentation in multislice computed tomography: impact of slice thickness on segmentation quality, measurement precision, and interobserver variability.
abstract: OBJECTIVE
  To determine the impact of slice thickness on semiautomatic lymph node analysis.
  
  
  METHODS
  Thoracic multislice computed tomography (MSCT) of 46 patients with NSCLC were reconstructed at 1.0-, 3.0-, and 5.0-mm slice thickness. Two radiologists independently determined long and short axis diameter and volume of 299 thoracic lymph nodes by semiautomatic segmentation software. Necessity of manual correction (= relative difference between uncorrected and corrected segmented lymph node volume) and relative interobserver differences were determined. The precision of segmentation was expressed by relative measurement deviations (RMD) from the reference standard (mean of 1.0 mm datasets). Statistical analysis encompassed t test and Bland-Altman plots.
  
  
  RESULTS
  Necessity of manual correction was significantly higher for 5.0 mm than for 3.0 (P = 0.042) or 1.0 mm (P = 0.0012). The RMD for long and short axis diameter were found to be independent of slice thickness, whereas the RMD for lymph node volume significantly (P = 0.021) increased from 4.0% at 1.0 mm (95% CI: 1.0%-3.5%) to 35% at 5.0 mm (95% CI: 10.5%-60.5%). The relative interobserver differences was consistently low for metric and volumetric parameters (eg, volume 2.3%, 95% CI: -7.4%-10.8% at 5.0 mm) with no difference in any of the slice thicknesses (P > 0.064).
  
  
  CONCLUSIONS
  Significant deviations in lymph node volume together with excessive manual corrections suggest reconstruction of the data for semiautomatic lymph node assessment at a slice thickness of 1.0 mm but not exceeding 3.0 mm.
authors.affiliation: Departments of Clinical Radiology, daggerBiomathematics and Statistics, and double daggerNuclear Medicine, University of Muenster, Germany. buerkeb@uni-muenster.de
authors.forename: Boris
authors.initials: B
authors.lastname: Buerke
authors.forename: Michael
authors.initials: M
authors.lastname: Puesken
authors.forename: Florian
authors.initials: F
authors.lastname: Beyer
authors.forename: Joachim
authors.initials: J
authors.lastname: Gerss
authors.forename: Matthias
authors.initials: M
authors.lastname: Weckesser
authors.forename: Harald
authors.initials: H
authors.lastname: Seifarth
authors.forename: Walter
authors.initials: W
authors.lastname: Heindel
authors.forename: Johannes
authors.initials: J
authors.lastname: Wessling
mesh_terms: D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging* / Q000556:secondary*; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008198:Lymph Nodes / Q000000981:diagnostic imaging*; D008207:Lymphatic Metastasis; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*