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

• Published in: Insights into imaging Vol. 14; no. 1; p. 79
• Main Authors: Zhong, Jingyu, Pan, Zilai, Chen, Yong, Wang, Lingyun, Xia, Yihan, Wang, Lan, Li, Jianying, Lu, Wei, Shi, Xiaomeng, Feng, Jianxing, Yan, Fuhua, Zhang, Huan, Yao, Weiwu
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 11.05.2023; Springer Nature B.V; SpringerOpen
• Subjects: Coefficient of variation; Computed tomography; Correlation coefficients; Diagnostic Radiology; Energy levels; Image enhancement; Image reconstruction; Imaging; Internal Medicine; Interventional Radiology; Machine learning; Medical imaging; Medicine; Medicine & Public Health; Multidetector computed tomography; Neuroradiology; Original; Original Article; Platforms; Radiology; Radiomics; Reproducibility; Reproducibility of results; Robustness; Synchronism; Ultrasound; Reproducibility of results; Image enhancement; Image reconstruction; Machine learning; Multidetector computed tomography
• ISSN: 1869-4101; 1869-4101
• DOI: 10.1186/s13244-023-01426-5

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 ( r 2 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 Key points The repeatability of DECT radiomics features was high between scan–rescans. The inter-reproducibility of radiomics features in VUE images and VMI 70keV was low. The differences in DECT techniques obviously altered the radiomics features. Synchronizing energy levels of VMI can potentially improve radiomics robustness.