Promising results from recent studies involving helical computed tomography (CT) for the early detection of lung cancer as well as rapid developments in multidetector CT technology have led to an increased interest in computer-aided diagnosis (CAD) techniques applied to CT imaging for lung cancer. To stimulate research in this area, the National Cancer Institute formed a consortium of institutions to develop the standards and consensus necessary for constructing a database resource of thoracic helical CT images. This consortium - the Lung Image Database Consortium (LIDC) - seeks to establish standard formats and processes by which to manage lung images and the related technical and clinical data that will be used by researchers to develop, train and evaluate CAD algorithms for lung cancer detection and diagnosis. The resulting database will be made available to interested investigators in order to promote development and validation of such algorithms. This exhibit will describe the LIDC's goals and motivate the fundamental issues that challenge this endeavor. Initial methods under consideration for the creation of the reference database will be detailed.

1. Learn about the LIDC's goals and methods for creating a publicly available database for the development, training, and evaluation of CAD methods for lung cancer detection and diagnosis using helical CT. 2. Learn about challenges in interpreting CT image data sets for the detection and diagnosis of lung cancer. 3. Learn about the intricacies of establishing spatial truth for lesion location and boundary. (S. G. A. is a shareholder of R2 Technologies.)