Towards deep radiomics: nodule malignancy prediction using CNNs on feature images

Lung cancer is a leading cause of cancer-related death worldwide and in the USA. Low Dose Computed tomography (LDCT) is the primary method of detection and diagnosis of lung cancers. Radiomics provides further analysis using LDCT scans which provide an opportunity for early detection and diagnosis of lung cancers. The convolutional neural network (CNN), a powerful method for image classification and recognition, has opened an alternative path for tumor identification and detection from LDCT scans. Nodules have different shapes, boundaries or patterns. In this study, we created feature images from different texture features of nodules and then used a CNN to classify each of the feature images. We call this approach “Deep Radiomics”. Law’s 3-D texture images were used for our analysis. Ten Law’s texture images were generated and used to train an ensemble of CNNs. Texture provides information about how an image looks. The use of feature images as CNN input is a novel approach to enable the generation and extraction of new types of features and lends itself to ensemble generation. From the LDCT arm of the national lung cancer screening study (NLST) dataset, a subset of nodule positive and screen-detected lung cancer (SDLC) cases were used in our study. The best result obtained from this study was 79.32% accuracy and 0.88 AUC, which is an improvement in accuracy over using just image features or just original images as input to CNNs for classification.