Deep radiomics: deep learning on radiomics texture images

Abstract

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.