title: Solid, part-solid, or non-solid?: classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system.
abstract: OBJECTIVE
  The purpose of this study was to develop and validate a computer-aided diagnosis (CAD) tool for automatic classification of pulmonary nodules seen on low-dose computed tomography into solid, part-solid, and non-solid.
  
  
  METHODS
  Study lesions were randomly selected from 2 sites participating in the Dutch-Belgian NELSON lung cancer screening trial. On the basis of the annotations made by the screening radiologists, 50 part-solid and 50 non-solid pulmonary nodules with a diameter between 5 and 30 mm were randomly selected from the 2 sites. For each unique nodule, 1 low-dose chest computed tomographic scan was randomly selected, in which the nodule was visible. In addition, 50 solid nodules in the same size range were randomly selected. A completely automatic 3-dimensional segmentation-based classification system was developed, which analyzes the pulmonary nodule, extracting intensity-, texture-, and segmentation-based features to perform a statistical classification. In addition to the nodule classification by the screening radiologists, an independent rating of all nodules by 3 experienced thoracic radiologists was performed. Performance of CAD was evaluated by comparing the agreement between CAD and human experts and among human experts using the Cohen κ statistics.
  
  
  RESULTS
  Pairwise agreement for the differentiation between solid, part-solid, and non-solid nodules between CAD and each of the human experts had a κ range between 0.54 and 0.72. The interobserver agreement among the human experts was in the same range (κ range, 0.56-0.81).
  
  
  CONCLUSIONS
  A novel automated classification tool for pulmonary nodules achieved good agreement with the human experts, yielding κ values in the same range as the interobserver agreement. Computer-aided diagnosis may aid radiologists in selecting the appropriate workup for pulmonary nodules.
authors.affiliation: From the *Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands; †Fraunhofer MEVIS, Bremen, Germany; ‡Department of Radiology, Kennemer Gasthuis, Haarlem; §Department of Radiology, University Medical Center Utrecht, Utrecht; and ∥Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands.
authors.forename: Colin
authors.initials: C
authors.lastname: Jacobs
authors.forename: Eva M
authors.initials: EM
authors.lastname: van Rikxoort
authors.forename: Ernst Th
authors.initials: ET
authors.lastname: Scholten
authors.forename: Pim A
authors.initials: PA
authors.lastname: de Jong
authors.forename: Mathias
authors.initials: M
authors.lastname: Prokop
authors.forename: Cornelia
authors.initials: C
authors.lastname: Schaefer-Prokop
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
mesh_terms: D000368:Aged; D000465:Algorithms; D001530:Belgium; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D009426:Netherlands; D010363:Pattern Recognition, Automated / Q000379:methods*; D011829:Radiation Dosage; D011835:Radiation Protection / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D012986:Software Validation; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*