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

TL;DR

This paper introduces a dynamic radiomics methodology that extracts time-dependent features from sequential tomographic images to better capture disease progression, improving diagnostic and prognostic assessments.

Contribution

It proposes a novel workflow and mathematical framework for dynamic radiomics, incorporating three methods to analyze feature changes over time, validated across multiple clinical problems.

Findings

Dynamic features outperform static features in clinical tasks

Three methods effectively describe feature transformations over time

Validation across three clinical problems demonstrates utility

Abstract

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.