The potential contribution of a computer-aided detection system for lung nodule detection in multidetector row computed tomography.
OBJECTIVE
We sought to evaluate the potential benefits of a computer-aided detection (CAD) system for detecting lung nodules in multidetector row CT (MDCT) scans.


METHODS
A CAD system was developed for detecting lung nodules on MDCT scans and was applied to the data obtained from 15 patients. Two chest radiologists in consensus established the reference standard. The nodules were categorized according to their size and their relationship to the surrounding structures (nodule type). The differences in the sensitivities between an experienced chest radiologist and a CAD system without user interaction were evaluated using a chi2 analysis. The differences in the sensitivities also were compared in terms of the nodule size and the nodule type.


RESULTS
A total of 309 nodules were identified as the reference standard. The sensitivity of a CAD system (81%) was not significantly different from that of a radiologist (85%; P > 0.05). The sensitivities of the CAD system for detecting nodules < or = 5 mm in diameter as well as detecting isolated nodules were higher than those of a radiologist (83% vs. 75%, P > 0.05; 93% vs. 76%, P < 0.001). The sensitivities of a radiologist for detecting nodules >5 mm and the nodules attached to other structures were higher than those of a CAD system (98% vs. 79%, P < 0.001; 91% vs. 71%, P < 0.001). There were 28.8 false-positive results of CAD per CT study.


CONCLUSIONS
The CAD system developed in this study performed the nodule detection task in different ways to that of a radiologist in terms of the nodule size and the nodule type, which suggests that the CAD system can play a complementary role to a radiologist in detecting nodules from large CT data sets.
Basic Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon, Korea.
Jeong Won
JW
Lee
Jin Mo
JM
Goo
Hyun Ju
HJ
Lee
Jong Hyo
JH
Kim
Seunghwan
S
Kim
Youn Tae
YT
Kim
D000328:Adult; D000368:Aged; D000465:Algorithms; D016009:Chi-Square Distribution; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D013902:Radiography, Thoracic; D012015:Reference Standards; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*