Understanding the benefits of deep convolutional neural networks (DCNN) may facilitate the development of robust computer-assisted image analysis methods. In this work we present features extracted from several DCNN structures with varying levels of depth for the classification of true and false lung nodules marked by a computer-aided detection system for thoracic computer tomography (CT) at a prescreening stage. 1048 true positive (TP) regions-of-interest (ROI) and 69,264 false positive (FP) ROIs from 350 patient cases were used for training VGG16, GoogLeNet, InceptionV4 and Inception-ResNet DCNN structures. For independent testing, 1022 TPs and 61,379 FPs from 310 cases were used. All nodule candidates were detected by applying multiscale Hessian enhancement filters and morphological operations to the segmented lungs. The top 200 ranked nodule candidates from each case were used for training and testing of the DCNN and for feature extraction. The area under the receiver operating characteristic curve (AUC) for the four DCNNs on the independent test set was 0.90, 0.89, 0.91, and 0.90. To analyze the characteristics of the deep features, we extracted features from the last fully connected layer by deploying the trained DCNNs to the independent test set. A total of 4096, 1024, 1536 and 1536 DCNN features from the four DCNNs were extracted and analyzed using uniform manifold approximation and projection (UMAP). The UMAP captured topological representation of features by clustering candidates in different anatomical regions such as lobular, terminal and respiratory bronchioles separately. FPs originated from similar global structures clustered in the UMAP space. The results indicate feature extracted from DCNNs have complementary characteristics that may be exploited to improve the accuracy of the classification tasks.