Abstract
Low-dose computed tomography (LDCT) plays a critical role in the early detection of lung cancer. Despite the life-saving benefit of early detection by LDCT, there are many limitations of this imaging modality including high rates of detection of indeterminate pulmonary nodules. Radiomics is the process of extracting and analyzing image-based, quantitative features from a region-of-interest which then can be analyzed to develop decision support tools that can improve lung cancer screening. Although prior published research has shown that delta radiomics (i.e., changes in features over time) have utility in predicting treatment response, limited work has been conducted using delta radiomics in lung cancer screening. As such, we conducted analyses to assess the performance of incorporating delta with conventional (non delta) features using machine learning to predict lung nodule malignancy. We found the best improved area under the receiver operating characteristic curve (AUC) was 0.822 when delta features were combined with conventional features versus an AUC 0.773 for conventional features only. Overall, this paper demonstrates the important utility of combining delta radiomics features with conventional radiomics features to improve performance of models in the lung cancer screening setting.