Mixture distribution analysis of a computer assisted diagnostic method for the evaluation of pulmonary nodules on computed tomography scan.
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.
Kung
Justin W
JW
Department of Radiology, University of Pennsylvania, 3600 Science Center, Suite 370, 3600 Market St, Philadelphia, PA 19104, USA.
Matsumoto
Sumiaki
S
Hasegawa
Ichiro
I
Nguyen
Binh
B
Toto
Lawrence C
LC
Kundel
Harold
H
Hatabu
Hiroto
H
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