title: PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes.
abstract: OBJECTIVE
  To compare source-to-background ratio (SBR)-based PET-CT auto-delineation with pathology in non-small-cell lung cancer (NSCLC) and to investigate whether auto-delineation reduces the interobserver variability compared with manual PET-CT-based gross tumor volume (GTV) delineation.
  
  
  METHODS
  Source-to-background ratio-based auto-delineation was compared with macroscopic tumor dimensions to assess its validity in 23 tumors. Thereafter, GTVs were delineated manually on 33 PET-CT scans by five observers for the primary tumor (GTV-1) and the involved lymph nodes (GTV-2). The delineation was repeated after 6 months with the auto-contour provided. This contour was edited by the observers. For comparison, the concordance index (CI) was calculated, defined as the ratio of intersection and the union of two volumes (A intersection B)/(A union or logical sum B).
  
  
  RESULTS
  The maximal tumor diameter of the SBR-based auto-contour correlated strongly with the macroscopic diameter of primary tumors (correlation coefficient = 0.90) and was shown to be accurate for involved lymph nodes (sensitivity 67%, specificity 95%). The median auto-contour-based target volumes were smaller than those defined by manual delineation for GTV-1 (31.8 and 34.6 cm(3), respectively; p = 0.001) and GTV-2 (16.3 and 21.8 cm(3), respectively; p = 0.02). The auto-contour-based method showed higher CIs than the manual method for GTV-1 (0.74 and 0.70 cm(3), respectively; p < 0.001) and GTV-2 (0.60 and 0.51 cm(3), respectively; p = 0.11).
  
  
  CONCLUSIONS
  Source-to-background ratio-based auto-delineation showed a good correlation with pathology, decreased the delineated volumes of the GTVs, and reduced the interobserver variability. Auto-contouring may further improve the quality of target delineation in NSCLC patients.
authors.affiliation: Department of Radiation Oncology (MAASTRO), GROW, University Hospital Maastricht, Maastricht, The Netherlands. angela.vanbaardwijk@maastro.nl
authors.forename: Angela
authors.initials: A
authors.lastname: van Baardwijk
authors.forename: Geert
authors.initials: G
authors.lastname: Bosmans
authors.forename: Liesbeth
authors.initials: L
authors.lastname: Boersma
authors.forename: Jeroen
authors.initials: J
authors.lastname: Buijsen
authors.forename: Stofferinus
authors.initials: S
authors.lastname: Wanders
authors.forename: Monique
authors.initials: M
authors.lastname: Hochstenbag
authors.forename: Robert-Jan
authors.initials: RJ
authors.lastname: van Suylen
authors.forename: André
authors.initials: A
authors.lastname: Dekker
authors.forename: Cary
authors.initials: C
authors.lastname: Dehing-Oberije
authors.forename: Ruud
authors.initials: R
authors.lastname: Houben
authors.forename: Søren M
authors.initials: SM
authors.lastname: Bentzen
authors.forename: Marinus
authors.initials: M
authors.lastname: van Kroonenburgh
authors.forename: Philippe
authors.initials: P
authors.lastname: Lambin
authors.forename: Dirk
authors.initials: D
authors.lastname: De Ruysscher
mesh_terms: D001185:Artificial Intelligence; D002289:Carcinoma, Non-Small-Cell Lung / Q000175:diagnosis*; D005260:Female; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008175:Lung Neoplasms / Q000175:diagnosis*; D008297:Male; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods; D049268:Positron-Emission Tomography / Q000379:methods*; D019275:Radiopharmaceuticals; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18