Evaluation of automated lung nodule detection on low-dose computed tomography scans from a lung cancer screening program(1).
OBJECTIVE
The purpose of this study was to evaluate the performance of a fully automated lung nodule detection method in a large database of low-dose computed tomography (CT) scans from a lung cancer screening program. Because nodules demonstrate a spectrum of radiologic appearances, the performance of the automated method was evaluated on the basis of nodule malignancy status, size, subtlety, and radiographic opacity.


METHODS
A database of 393 thick-section (10 mm) low-dose CT scans was collected. Automated lung nodule detection proceeds in two phases: gray-level thresholding for the initial identification of nodule candidates, followed by the application of a rule-based classifier and linear discriminant analysis to distinguish between candidates that correspond to actual lung nodules and candidates that correspond to non-nodules. Free-response receiver operating characteristic analysis was used to evaluate the performance of the method based on a jackknife training/testing approach.


RESULTS
An overall nodule detection sensitivity of 70% (330 of 470) was attained with an average of 1.6 false-positive detections per section. At the same false-positive rate, 83% (57 of 69) of the malignant lung nodules in the database were detected. When the method was trained specifically for malignant nodules, a sensitivity of 80% (55 of 69) was attained with 0.85 false-positives per section.


CONCLUSIONS
We have evaluated an automated lung nodule detection method with a large number of low-dose CT scans from a lung cancer screening program. An overall sensitivity of 80% for malignant nodules was achieved with 0.85 false-positive detections per section. Such a computerized lung nodule detection method is expected to become an important part of CT-based lung cancer screening programs.
Armato
Samuel G
SG
Department of Radiology, MC 2026, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA. s-armato@uchicago.edu
Roy
Arunabha S
AS
Macmahon
Heber
H
Li
Feng
F
Doi
Kunio
K
Sone
Shusuke
S
Altman
Michael B
MB
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019992:Databases as Topic; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008171:Lung Diseases / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008403:Mass Screening; D008875:Middle Aged; D016571:Neural Networks, Computer; D011829:Radiation Dosage; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D036542:Tomography, Spiral Computed / Q000379:methods*