Abstract
Recent advances in radiomics have shown promising results in prognostic and diagnostic studies with high dimensional imaging feature analysis. However, radiomic features are known to be affected by technical parameters and feature extraction methodology. We evaluate the robustness of CT radiomic features against the technical parameters involved in CT acquisition and feature extraction procedures using a standardized phantom and verify the feature robustness by using patient cases. ACR phantom was scanned with two tube currents, two reconstruction kernels, and two fields of view size. A total of 47 radiomic features of textures and first-order statistics were extracted on the homogeneous region from all scans. Intrinsic variability was measured to identify unstable features vulnerable to inherent CT noise and texture. Susceptibility index was defined to represent the susceptibility to the variation of a given technical parameter. Eighteen radiomic features were shown to be intrinsically unstable on reference condition. The features were more susceptible to the reconstruction kernel variation than to other sources of variation. The feature robustness evaluated on the phantom CT correlated with those evaluated on clinical CT scans. We revealed a number of scan parameters could significantly affect the radiomic features. These characteristics should be considered in a radiomic study when different scan parameters are used in a clinical dataset.
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Acknowledgements
This work was supported, in part, by a grant of the Radiation Technology R&D program through the National Research Foundation of Korea funded by the Ministry of Science and ICT (No. 2017M2A2A6A01070972), in part by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number : HI15C1532).
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Jin, H., Kim, J.H. Evaluation of Feature Robustness Against Technical Parameters in CT Radiomics: Verification of Phantom Study with Patient Dataset. J Sign Process Syst 92, 277–287 (2020). https://doi.org/10.1007/s11265-019-01496-z
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DOI: https://doi.org/10.1007/s11265-019-01496-z