Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy.
OBJECTIVE
The aim of this study was to evaluate the usefulness of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector-row computed tomography (MDCT) in terms of improvement in radiologists' diagnostic accuracy in detecting lung nodules, using jackknife free-response receiver-operating characteristic (JAFROC) analysis.


METHODS
Twenty-one patients (6 without and 15 with lung nodules) were selected randomly from 120 consecutive thoracic computed tomographic examinations. The gold standard for the presence or absence of nodules in the observer study was determined by consensus of two radiologists. Six expert radiologists participated in a free-response receiver operating characteristic study for the detection of lung nodules on MDCT, in which cases were interpreted first without and then with the output of CAD software. Radiologists were asked to indicate the locations of lung nodule candidates on the monitor with their confidence ratings for the presence of lung nodules.


RESULTS
The performance of the CAD software indicated that the sensitivity in detecting lung nodules was 71.4%, with 0.95 false-positive results per case. When radiologists used the CAD software, the average sensitivity improved from 39.5% to 81.0%, with an increase in the average number of false-positive results from 0.14 to 0.89 per case. The average figure-of-merit values for the six radiologists were 0.390 without and 0.845 with the output of the CAD software, and there was a statistically significant difference (P < .0001) using the JAFROC analysis.


CONCLUSIONS
The CAD software for the detection of lung nodules on MDCT has the potential to assist radiologists by increasing their accuracy.
Hirose
Tomohiro
T
Department of Radiology, Shiga University of Medical Science, Seta-Tsukinowa-cho, Otsu-shi, Shiga 520-2192, Japan. hirotomo@belle.shiga-med.ac.jp
Nitta
Norihisa
N
Shiraishi
Junji
J
Nagatani
Yukihiro
Y
Takahashi
Masashi
M
Murata
Kiyoshi
K
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods; D011361:Professional Competence; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000295:instrumentation / Q000379:methods*; D011871:Radiology; 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 / Q000295:instrumentation / Q000379:methods*; D014481:United States; D000078329:Workforce