Recent progress in computer-aided diagnosis of lung nodules on thin-section CT

Abstract

Computer-aided diagnosis (CAD) provides a computer output as a "second opinion" in order to assist radiologists in the diagnosis of various diseases on medical images. Currently, a significant research effort is being devoted to the detection and characterization of lung nodules in thin-section computed tomography (CT) images, which represents one of the newest direction of CAD development in thoracic imaging. We describe in this article the current status of the development and evaluation of CAD schemes for the detection and characterization of lung nodules in thin-section CT. We also review a number of observer performance studies in which it was attempted to assess the potential clinical usefulness of CAD schemes for nodule detection and characterization in thin-section CT. Whereas current CAD schemes for nodule characterization have achieved high performance levels and would be able to improve radiologists' performance in the characterization of nodules in thinsection CT, current schemes for nodule detection appear to report many false positives, and, therefore, significant efforts are needed in order further to improve the performance levels of current CAD schemes for nodule detection in thin-section CT.

The UK Lung Cancer Screening Trial: a pilot randomised controlled trial of low-dose computed tomography screening for the early detection of lung cancer

“…Reassuringly, radiographers in general did not exceed three average false-positives per case, and are thus comparable with computer-aided detection (CAD) systems, where average false-positives per case range between 0.3 and 15 per case. 78 Therefore, we believe that these results show that the radiographers in this study (having initially undergone training and testing) may not yet be suitable to act as first and only readers in lung cancer screening. However, their performance compared reasonably well with reported results for radiologists and CAD systems in the literature, and therefore these results certainly do not disqualify radiographers from CT scan reading in lung cancer screening.…”

Comparing the performance of trained radiographers against experienced radiologists in the UK lung cancer screening (UKLS) trial

“…However, it is reassuring that radiographers in general did not exceed 3 average FP detections per CT, which may be considered favourably when compared with some computer-aided detection/diagnosis systems, where average FP rates between 0.3 and 15 per case have been reported. 30 When viewed in this context, the higher average FP detection rate of radiographers compared with radiologists in this study does not disqualify them from being used as aids to reading.…”

Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation

“…CADe systems detect lesions through medical images, while CADx systems aim to measure the characteristics of the lesion. CADe systems have the following objectives [20]:…”