Dynamic radiomics: A new methodology to extract quantitative time-related features from tomographic images

Abstract

At present, the feature extraction method of radiology is mainly based on static tomography images at a certain time. However, the occurrence and development of disease is a dynamic process, and the information contained in static images is not enough to fully evaluate the patient’s condition. Therefore, in this study, we propose a new dynamic radiomics feature extraction workflow that uses time-related tomographic images of the same patient to extract static features at different times, which are then quantified as new dynamic features for diagnosis or prognostic evaluation. We first define the concept and mathematical paradigm of dynamic radiomics and propose three types of construction methods for dynamic features to describe static features over time from different perspectives. Three different clinical questions were used to compare the performance of dynamic features with conventional static features in predicting different clinical questions. The results of all experimental cohorts show that the newly proposed dynamic features can achieve higher sensitivity, specificity and accuracy than static features and have higher robustness. We also found that different dynamic features may be desired to address different clinical issues.