Screening lung CT has a high sensitivity for the detection of lung nodules, but many of the nodules identified cannot be directly classified as benign or malignant. Nodules identified on screening CT have various mathematical features that may help to determine their malignant potential. Our group has defined computational features of nodules that define their mathematical characteristics. The CAD group at Georgetown University, directed by Ben Lo, Ph.D. and Matthew Freedman, MD, MBA, has done a significant amount of work on the computational features that are defined based on image patterns and textures of the nodules. In addition, we have also trained a vision-based neural network system to automatically extract the features. We would like to apply these mathematical features to nodules identified on Georgetowns screening lung CTs from the NLST and LSS protocols. Because histologic diagnoses are not yet available on the majority of screen-detected nodules, we will not be able to make immediate correlations. However, as the cases evolve we will be able to correlate the image features with the histology of the nodules.