Abstract
Abstract
Objectives
To evaluate robustness of dual-energy CT (DECT) radiomics features of virtual unenhanced (VUE) image and virtual monoenergetic image (VMI) among different imaging platforms.
Methods
A phantom with sixteen clinical-relevant densities was scanned on ten DECT platforms with comparable scan parameters. Ninety-four radiomic features were extracted via Pyradiomics from VUE images and VMIs at energy level of 70 keV (VMI70keV). Test–retest repeatability was assessed by Bland–Altman analysis. Inter-platform reproducibility of VUE images and VMI70keV was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD) among platforms, and by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) between platform pairs. The correlation between variability of CT number radiomics reproducibility was estimated.
Results
92.02% and 92.87% of features were repeatable between scan–rescans for VUE images and VMI70keV, respectively. Among platforms, 11.30% and 28.39% features of VUE images, and 15.16% and 28.99% features of VMI70keV were with CV < 10% and QCD < 10%. The average percentages of radiomics features with ICC > 0.90 and CCC > 0.90 between platform pairs were 10.00% and 9.86% in VUE images and 11.23% and 11.23% in VMI70keV. The CT number inter-platform reproducibility using CV and QCD showed negative correlations with percentage of the first-order radiomics features with CV < 10% and QCD < 10%, in both VUE images and VMI70keV (r2 0.3870–0.6178, all p < 0.001).
Conclusions
The majority of DECT radiomics features were non-reproducible. The differences in CT number were considered as an indicator of inter-platform DECT radiomics variation.
Critical relevance statement: The majority of radiomics features extracted from the VUE images and the VMI70keV were non-reproducible among platforms, while synchronizing energy levels of VMI to reduce the CT number value variability may be a potential way to mitigate radiomics instability.
Graphical Abstract
Funder
National Natural Science Foundation of China
Shanghai Science and Technology Commission Science and Technology Innovation Action Clinical Innovation Field
Yangfan Project of Science and Technology Commission of Shanghai Municipality
Medicine and Engineering Combination Project of Shanghai Jiao Tong University
Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging
Cited by
8 articles.
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