Recent progress in computer-aided diagnosis of lung nodules on thin-section CT.
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 directions 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 thin-section 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.
Li
Qiang
Q
Department of Radiology, The University of Chicago, 5841 S. Maryland Avenue, MC2026, Chicago, IL 6063, USA. qiangli@uchicago.edu
D003952:Diagnostic Imaging; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008175:Lung Neoplasms / Q000175:diagnosis*; D014057:Tomography, X-Ray Computed; D014481:United States