title: Mixture distribution analysis of a computer assisted diagnostic method for the evaluation of pulmonary nodules on computed tomography scan.
abstract: OBJECTIVE
  To compare the effectiveness of a new computational scheme for pulmonary nodule detection in computed tomography images against human observers.
  
  
  METHODS
  The study involved evaluation of 81 potential nodules by four radiologists. Each radiologist separately evaluated the potential nodules and provided a confidence level for the presence of pulmonary nodules. Their performance was compared with that of the new computational scheme by mixture distribution analysis.
  
  
  RESULTS
  Mixture distribution analysis of the results of the four radiologists demonstrated a relative proportion agreement of 0.84. The kappa statistic was used to compare the agreement of the computational scheme with the results of the four radiologists. A kappa value of .65 (se = .11) was shown to be significantly different from chance (P = .99).
  
  
  CONCLUSIONS
  The new computational scheme correlates well with the radiologists' subjective rankings of pulmonary nodules on computed tomography scans and may prove a useful tool in the evaluation of algorithms for the screening and diagnosis of lung cancer.
authors.affiliation: Department of Radiology, University of Pennsylvania, 3600 Science Center, Suite 370, 3600 Market St, Philadelphia, PA 19104, USA.
authors.forename: Justin W
authors.initials: JW
authors.lastname: Kung
authors.forename: Sumiaki
authors.initials: S
authors.lastname: Matsumoto
authors.forename: Ichiro
authors.initials: I
authors.lastname: Hasegawa
authors.forename: Binh
authors.initials: B
authors.lastname: Nguyen
authors.forename: Lawrence C
authors.initials: LC
authors.lastname: Toto
authors.forename: Harold
authors.initials: H
authors.lastname: Kundel
authors.forename: Hiroto
authors.initials: H
authors.lastname: Hatabu
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D015588:Observer Variation; D011857:Radiographic Image Interpretation, Computer-Assisted; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000145:classification / Q000000981:diagnostic imaging* / Q000453:epidemiology; D016008:Statistical Distributions; D014057:Tomography, X-Ray Computed