Dynamic radiomics: a new methodology to extract quantitative time-related features from tomographic images
This addresses the problem of incomplete disease characterization in medical imaging for clinicians and researchers, though it appears incremental as it extends existing radiomics with time-dependent analysis.
The study tackled the limitation of static radiomics by proposing a dynamic radiomics workflow that extracts quantitative time-related features from tomographic images to better reflect disease progression, validating it on three clinical problems with performance comparisons to conventional static features.
The feature extraction methods of radiomics are mainly based on static tomographic images at a certain moment, while the occurrence and development of disease is a dynamic process that cannot be fully reflected by only static characteristics. This study proposes a new dynamic radiomics feature extraction workflow that uses time-dependent tomographic images of the same patient, focuses on the changes in image features over time, and then quantifies them as new dynamic features for diagnostic or prognostic evaluation. We first define the mathematical paradigm of dynamic radiomics and introduce three specific methods that can describe the transformation process of features over time. Three different clinical problems are used to validate the performance of the proposed dynamic feature with conventional 2D and 3D static features.