title: Automated interpretation of PET/CT images in patients with lung cancer.
abstract: OBJECTIVE
  To develop a completely automated method based on image processing techniques and artificial neural networks for the interpretation of combined [(18)F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) images for the diagnosis and staging of lung cancer.
  
  
  METHODS
  A total of 87 patients who underwent PET/CT examinations due to suspected lung cancer comprised the training group. The test group consisted of PET/CT images from 49 patients suspected with lung cancer. The consensus interpretations by two experienced physicians were used as the 'gold standard' image interpretation. The training group was used in the development of the automated method. The image processing techniques included algorithms for segmentation of the lungs based on the CT images and detection of lesions in the PET images. Lung boundaries from the CT images were used for localization of lesions in the PET images in the feature extraction process. Eight features from each examination were used as inputs to artificial neural networks trained to classify the images. Thereafter, the performance of the network was evaluated in the test set.
  
  
  RESULTS
  The performance of the automated method measured as the area under the receiver operating characteristic curve, was 0.97 in the test group, with an accuracy of 92%. The sensitivity was 86% at a specificity of 100%.
  
  
  CONCLUSIONS
  A completely automated method using artificial neural networks can be used to detect lung cancer with such a high accuracy that the application as a clinical decision support tool appears to have significant potential.
authors.affiliation: Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Denmark. Henrik.gutte@rh.hosp.dk
authors.forename: Henrik
authors.initials: H
authors.lastname: Gutte
authors.forename: David
authors.initials: D
authors.lastname: Jakobsson
authors.forename: Fredrik
authors.initials: F
authors.lastname: Olofsson
authors.forename: Mattias
authors.initials: M
authors.lastname: Ohlsson
authors.forename: Sven
authors.initials: S
authors.lastname: Valind
authors.forename: Annika
authors.initials: A
authors.lastname: Loft
authors.forename: Lars
authors.initials: L
authors.lastname: Edenbrandt
authors.forename: Andreas
authors.initials: A
authors.lastname: Kjaer
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001331:Automation; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D012372:ROC Curve; D014057:Tomography, X-Ray Computed / Q000379:methods*