Assessing radiomics feature stability with simulated CT acquisitions

TL;DR

This study introduces a CT simulation environment to evaluate the stability and discriminative power of radiomics features, addressing the challenge of feature variability due to acquisition differences in medical imaging.

Contribution

The paper develops and validates a CT simulator that accurately assesses radiomics feature stability, aligning simulated results with real-world multi-center studies.

Findings

Simulated radiomics features match variability observed in physical phantom studies.

The simulator effectively evaluates feature stability across different acquisition settings.

Results support using simulation for feature selection in clinical research.

Abstract

Medical imaging quantitative features had once disputable usefulness in clinical studies. Nowadays, advancements in analysis techniques, for instance through machine learning, have enabled quantitative features to be progressively useful in diagnosis and research. Tissue characterisation is improved via the 'radiomics' features, whose extraction can be automated. Despite the advances, stability of quantitative features remains an important open problem. As features can be highly sensitive to variations of acquisition details, it is not trivial to quantify stability and efficiently select stable features. In this work, we develop and validate a Computed Tomography (CT) simulator environment based on the publicly available ASTRA toolbox (www.astra-toolbox.com). We show that the variability, stability and discriminative power of the radiomics features extracted from the virtual phantom images generated by the simulator are similar to those observed in a tandem phantom study. Additionally, we show that the variability is matched between a multi-center phantom study and simulated results. Consequently, we demonstrate that the simulator can be utilised to assess radiomics features' stability and discriminative power.