Computer-Aided Detection of Lung Nodules on Chest CT: Issues to be Solved before Clinical Use

iven the increasing resolution of modern CT scanners, and the requirements for large-scale lung-screening examinations and diagnostic studies, there is an increased need for the accurate and reproducible analysis of the large numbers of images. Nodule detection is one of the main challenges of CT imaging, as they can be missed due to their small size, low relative contrast, or because they are located in an area with complex anatomy. Recent developments in computeraided diagnosis (CAD) schemes are expected to aid radiologists in various tasks of chest imaging. In this era of multidetector row CT, the thoracic applications of greatest interest include the detection and volume measurement of lung nodules (1 7). Technology for CAD as applied to lung nodule detection on chest CT has been approved by the Food and Drug Administration and is currently commercially available. The article by Lee et al. (5) in this issue of the Korean Journal of Radiology is one of the few studies to examine the influence of a commercially available CAD system on the detection of lung nodules. In this study, some additional nodules were detected with the help of a CAD system, but at the expense of increased false positivity. The nodule detection rate of the CAD system in this study was lower than that achieved by radiologists, and the authors insist that the CAD system should be improved further. Compared to the use of CAD on mammograms, CAD evaluations of chest CTs remain limited to the laboratory setting. In this field, apart from the issues of detection rate and false positive detections, many obstacles must be overcome before CAD can be used in a true clinical reading environment. In this editorial, I will list some of these issues, but I emphasize now that I believe these issues will be solved by improved CAD versions in the near future. Underlying disease in the thorax other than the presence of a lung nodule or mass: In the study by Lee et al. (5), cases with consolidation, lobar or greater atelectasis, airway disease, or interstitial lung disease were excluded from the analysis. Although these underlying diseases are unusual in screening settings, radiologists should identify lung nodules in the presence of these conditions. Commercially available CAD systems can also be used for detecting lung nodules in these circumstances, but they may erroneously segment lung areas, which can affect detection performance.