title: CT-based patient modeling for head and neck hyperthermia treatment planning: manual versus automatic normal-tissue-segmentation.
abstract: OBJECTIVE
  Clinical trials have shown that hyperthermia, as adjuvant to radiotherapy and/or chemotherapy, improves treatment of patients with locally advanced or recurrent head and neck (H&N) carcinoma. Hyperthermia treatment planning (HTP) guided H&N hyperthermia is being investigated, which requires patient specific 3D patient models derived from Computed Tomography (CT)-images. To decide whether a recently developed automatic-segmentation algorithm can be introduced in the clinic, we compared the impact of manual- and automatic normal-tissue-segmentation variations on HTP quality.
  
  
  METHODS
  CT images of seven patients were segmented automatically and manually by four observers, to study inter-observer and intra-observer geometrical variation. To determine the impact of this variation on HTP quality, HTP was performed using the automatic and manual segmentation of each observer, for each patient. This impact was compared to other sources of patient model uncertainties, i.e. varying gridsizes and dielectric tissue properties.
  
  
  RESULTS
  Despite geometrical variations, manual and automatic generated 3D patient models resulted in an equal, i.e. 1%, variation in HTP quality. This variation was minor with respect to the total of other sources of patient model uncertainties, i.e. 11.7%.
  
  
  CONCLUSIONS
  Automatically generated 3D patient models can be introduced in the clinic for H&N HTP.
authors.affiliation: Erasmus MC - Cancer Institute, Department of Radiation Oncology, Rotterdam, The Netherlands. Electronic address: r.f.verhaart@erasmusmc.nl.
authors.forename: René F
authors.initials: RF
authors.lastname: Verhaart
authors.affiliation: Erasmus MC, Department of Medical Informatics and Radiology, Rotterdam, The Netherlands.
authors.forename: Valerio
authors.initials: V
authors.lastname: Fortunati
authors.affiliation: Erasmus MC - Cancer Institute, Department of Radiation Oncology, Rotterdam, The Netherlands.
authors.forename: Gerda M
authors.initials: GM
authors.lastname: Verduijn
authors.affiliation: Erasmus MC, Department of Medical Informatics and Radiology, Rotterdam, The Netherlands.
authors.forename: Theo
authors.initials: T
authors.lastname: van Walsum
authors.affiliation: Erasmus MC, Department of Medical Informatics and Radiology, Rotterdam, The Netherlands.
authors.forename: Jifke F
authors.initials: JF
authors.lastname: Veenland
authors.affiliation: Erasmus MC - Cancer Institute, Department of Radiation Oncology, Rotterdam, The Netherlands.
authors.forename: Margarethus M
authors.initials: MM
authors.lastname: Paulides
mesh_terms: D000465:Algorithms; D002294:Carcinoma, Squamous Cell / Q000473:pathology / Q000628:therapy*; D006258:Head and Neck Neoplasms / Q000473:pathology / Q000628:therapy*; D006801:Humans; D006979:Hyperthermia, Induced / Q000379:methods*; D015588:Observer Variation; D010347:Patient Care Planning; D000077195:Squamous Cell Carcinoma of Head and Neck; D014057:Tomography, X-Ray Computed / Q000379:methods
keywords: Automatic segmentation; Dosimetry; Inter-observer variability; Intra-observer variability; Manual segmentation; Sensitivity analysis