The goal of this work is to determine whether malignant solitary pulmonary nodules (SPNs) can be discriminated form benign lesions based on quantitative features derived form CT images. The goal is to reach an accurate diagnosis quickly and without the need for additional imaging or more invasive tests. CT images were obtained from 54 patients identified as having an SPN. Of these, 24 SPN patients scanned by a spiral volumetric technique before and after the injection of an intravenous contrast agent. Diagnostic truth was determined using either pathology results from biopsy or surgical resection or from radiographic follow-up. All images were acquired using a volumetric CT scan protocol of <EQ3 mm beam collimation, pitch 1, and were reconstructed <EW3 mm apart (typically 1.5mm). For patients receiving the contrast enhanced spiral CT protocol, the nodule was scanned prior to the contrast injection and at 45, 90, 180, and 300 seconds after injection. Nodule boundaries were isolated using a semi-automated contouring procedure on each image in which the nodule appeared. The contour boundaries, as well as their internal pixels, were combined to form 3D regions of interest (ROIs). These ROIs were then used to extract two dimensional (from a representative slice) and 3D (from the complete volume of interest) measures of interest. Two dimensional measure categories include: attenuation, size, texture and boundary shape. 3D categories include attenuation properties, size and surface boundary shape. Each nodule's contrast enhancement was measured using individual pre-contrast and post-contrast images acquired at the same location. Stepwise analyses were performed for each category of features and then again using the combined results form each category of features. Once features were selected, they were used as input variables to a linear discriminant and a logistic regression classifier. The performance of each was evaluated using ROC analysis. Because false negatives are much more serious than false positives, we also evaluated performance using the false positive fraction (FPF) at which the true positive fraction (TPF) goes to 1.0. When the Logistic Regression classifier was used with 2D non texture features, it achieved >90% accuracy, area under ROC of >0.96 and FPF of .28 for TPF = 1.0. When the Logistic Regression classifier was used with 3D and contrast enhancement features, it achieved 92% accuracy, an area under ROC of .969 and a FPF of .063 for TPF = 1.0. When combinations of group features were used, the performance was not as good as individual groups listed above. The best was when enhancement and sphericity were used in a linear discriminant model, which achieved 87.5% accuracy and area under ROC of .820. The combination of contrast enhancement measures with other, morphological descriptors, hold promise for accurate classification of solitary pulmonary nodules imaged on CT. These results are preliminary as they are based on small numbers of cases and may be sensitive to the results of individual cases.