Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability

Jingyu Zhong; Zilai Pan; Yong Chen; Lingyun Wang; Yihan Xia; Lan Wang; Jianying Li; Wei Lu; Xiaomeng Shi; Jianxing Feng; Fuhua Yan; Huan Zhang; Weiwu Yao

doi:10.1186/s13244-023-01426-5

Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability

Insights Imaging. 2023 May 11;14(1):79. doi: 10.1186/s13244-023-01426-5.

Authors

Jingyu Zhong^#¹, Zilai Pan^#², Yong Chen², Lingyun Wang², Yihan Xia², Lan Wang², Jianying Li³, Wei Lu⁴, Xiaomeng Shi⁵, Jianxing Feng⁶, Fuhua Yan², Huan Zhang⁷, Weiwu Yao⁸

Affiliations

¹ Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
² Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
³ Computed Tomography Research Center, GE Healthcare, Beijing, 100176, China.
⁴ Computed Tomography Research Center, GE Healthcare, Shanghai, 201203, China.
⁵ Department of Materials, Imperial College London, London, SW7 2AZ, UK.
⁶ Haohua Technology Co., Ltd., Shanghai, 201100, China.
⁷ Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. huanzhangy@163.com.
⁸ Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China. yaoweiwuhuan@163.com.

^# Contributed equally.

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 (VMI_70keV). Test-retest repeatability was assessed by Bland-Altman analysis. Inter-platform reproducibility of VUE images and VMI_70keV 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 VMI_70keV, respectively. Among platforms, 11.30% and 28.39% features of VUE images, and 15.16% and 28.99% features of VMI_70keV 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 VMI_70keV. 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 VMI_70keV (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.

Keywords: Image enhancement; Image reconstruction; Machine learning; Multidetector computed tomography; Reproducibility of results.

Abstract

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