title: Accuracy of software-assisted contour propagation from planning CT to cone beam CT in head and neck radiotherapy.
abstract: BACKGROUND
  Autocontouring improves workflow in computed tomography (CT)-based dose planning, but could also potentially play a role for optimal use of daily cone beam CT (CBCT) in adaptive radiotherapy. This study aims to determine the accuracy of a deformable image registration (DIR) algorithm for organs at risk (OAR) in the neck region, when applied to CBCT.
  
  
  METHODS
  For 30 head and neck cancer (HNC) patients 14 OARs including parotid glands, swallowing structures and spinal cord were delineated. Contours were propagated by DIR from CT to the CBCTs of the first and last treatment fraction. An indirect approach, propagating contours to the first CBCT and from there to the last CBCT was also tested. Propagated contours were compared to manually corrected contours by Dice similarity coefficient (DSC) and Hausdorff distance (HD). Dose was recalculated on CBCTs and dosimetric consequences of uncertainties in DIR were reviewed.
  
  
  RESULTS
  Mean DSC values of ≥0.8 were considered adequate and were achieved in tongue base (0.91), esophagus (0.85), glottic (0.81) and supraglottic larynx (0.83), inferior pharyngeal constrictor muscle (0.84), spinal cord (0.89) and all salivary glands in the first CBCT. For the last CBCT by direct propagation, adequate DSC values were achieved for tongue base (0.85), esophagus (0.84), spinal cord (0.87) and all salivary glands. Using indirect propagation only tongue base (0.80) and parotid glands (0.87) were ≥0.8. Mean relative dose difference between automated and corrected contours was within ±2.5% of planed dose except for esophagus inlet (-4.5%) and esophagus (5.0%) for the last CBCT using indirect propagation.
  
  
  CONCLUSIONS
  Compared to manually corrected contours, the DIR algorithm was accurate for use in CBCT images of HNC patients and the minor inaccuracies had little consequence for mean dose in most clinically relevant OAR. The method can thus enable a more automated segmentation of CBCT for use in adaptive radiotherapy.
authors.affiliation: a Department of Oncology , Aarhus University Hospital , Aarhus C , Denmark.
authors.forename: Christian A
authors.initials: CA
authors.lastname: Hvid
authors.affiliation: b Department of Medical Physics , Aarhus University Hospital , Aarhus C , Denmark.
authors.forename: Ulrik V
authors.initials: UV
authors.lastname: Elstrøm
authors.affiliation: a Department of Oncology , Aarhus University Hospital , Aarhus C , Denmark.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Jensen
authors.affiliation: b Department of Medical Physics , Aarhus University Hospital , Aarhus C , Denmark.
authors.forename: Markus
authors.initials: M
authors.lastname: Alber
authors.affiliation: a Department of Oncology , Aarhus University Hospital , Aarhus C , Denmark.
authors.forename: Cai
authors.initials: C
authors.lastname: Grau
mesh_terms: D000465:Algorithms; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D005260:Female; D006258:Head and Neck Neoplasms / Q000473:pathology / Q000532:radiotherapy*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged; D058958:Organs at Risk / Q000528:radiation effects*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D012469:Salivary Glands / Q000528:radiation effects; D012984:Software; D014059:Tongue / Q000528:radiation effects