title: Head and neck target delineation using a novel PET automatic segmentation algorithm.
abstract: To evaluate the feasibility and impact of using a novel advanced PET auto-segmentation method in Head and Neck (H&N) radiotherapy treatment (RT) planning.
  
  
  
  ATLAAS, Automatic decision Tree-based Learning Algorithm for Advanced Segmentation, previously developed and validated on pre-clinical data, was applied to18F-FDG-PET/CT scans of 20 H&N patients undergoing Intensity Modulated Radiation Therapy. Primary Gross Tumour Volumes (GTVs) manually delineated on CT/MRI scans (GTVpCT/MRI), together with ATLAAS-generated contours (GTVpATLAAS) were used to derive the RT planning GTV (GTVpfinal). ATLAAS outlines were compared to CT/MRI and final GTVs qualitatively and quantitatively using a conformity metric.
  
  
  
  The ATLAAS contours were found to be reliable and useful. The volume of GTVpATLAASwas smaller than GTVpCT/MRIin 70% of the cases, with an average conformity index of 0.70. The information provided by ATLAAS was used to grow the GTVpCT/MRIin 10 cases (up to 10.6mL) and to shrink the GTVpCT/MRIin 7 cases (up to 12.3mL). ATLAAS provided complementary information to CT/MRI and GTVpATLAAScontributed to up to 33% of the final GTV volume across the patient cohort.
  
  
  
  ATLAAS can deliver operator independent PET segmentation to augment clinical outlining using CT and MRI and could have utility in future clinical studies.
authors.affiliation: Wales Research & Diagnostic PET Imaging Centre, Cardiff, UK. Electronic address: beatrice.walker@espci.fr.
authors.forename: B
authors.initials: B
authors.lastname: Berthon
authors.affiliation: Velindre Cancer Centre, Cardiff, UK.
authors.forename: M
authors.initials: M
authors.lastname: Evans
authors.affiliation: Wales Research & Diagnostic PET Imaging Centre, Cardiff, UK.
authors.forename: C
authors.initials: C
authors.lastname: Marshall
authors.affiliation: Velindre Cancer Centre, Cardiff, UK.
authors.forename: N
authors.initials: N
authors.lastname: Palaniappan
authors.affiliation: Velindre Cancer Centre, Cardiff, UK.
authors.forename: N
authors.initials: N
authors.lastname: Cole
authors.affiliation: Velindre Cancer Centre, Cardiff, UK.
authors.forename: V
authors.initials: V
authors.lastname: Jayaprakasam
authors.affiliation: Velindre Cancer Centre, Cardiff, UK.
authors.forename: T
authors.initials: T
authors.lastname: Rackley
authors.affiliation: Velindre Cancer Centre, Cardiff, UK; School of Engineering, Cardiff University, Cardiff, UK.
authors.forename: E
authors.initials: E
authors.lastname: Spezi
mesh_terms: D000465:Algorithms; D005260:Female; D019788:Fluorodeoxyglucose F18; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology / Q000532:radiotherapy*; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D000072078:Positron Emission Tomography Computed Tomography / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted; D050397:Radiotherapy, Intensity-Modulated; D047368:Tumor Burden
chemical_list: D019788:Fluorodeoxyglucose F18
keywords: Automatic PET segmentation; Image Segmentation; Intensity Modulated Radiation Therapy; Positron Emission Tomography