Large chest x-ray datasets accumulate technical and biological variations. Technical variations are often caused by differences in scanner types (e.g. x-ray energy) whereas biological variations refer to those resulting from morphological differences among populations. Such variations decrease the generalizability of convolutional neural networks (CNNs) and could lead to disparities within specific patient subgroups. We sought to disentangle the sources of variations for a CNN in the context of technical and biological variability. The primary goal of this study was to understand whether tailored and specific AI models might need to be created (i.e., gender-specific or machine-specific AI models).
Lung cancer has high mortality and occurrence worldwide. Radiomics is a method for extracting quantitative features from medical images that can be used for predictive analysis. Radiomics has been applied quite successfully for lung nodule malignancy prediction. Along with traditional radiomics, Convolutional Neural Networks (CNN) are now used quite effectively for lung cancer analysis. Texture provides information about variation in pixel intensity in regions. Lung nodules/tumors possess a noticeable texture pattern. That’s why texture radiomics features can be used to construct predictive models to analyze malignant and benign lung nodules. As textures show visible patterns, training the CNNs using texture images is a novel idea that enables the creation of an ensemble of classifiers. In this study, 192 texture images (wavelet, Laws, gray level zone matrix, neighborhood grey tone difference, and run-length) were generated, and the same CNN architecture was trained separately on all texture images. We termed this approach, “Deep Radiomics.” The maximum classification accuracy of 73% and 0.82 AUC was achieved from both the P2L2C5 wavelet and L5E5L5 laws texture images. When multiple CNN model’s predictions were merged to generate an ensemble model, results of 81.43% (0.91 AUC) were achieved from our study, which was an improvement.
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.
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