4.7 Article

Repeatability and reproducibility study of radiomic features on a phantom and human cohort

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SCIENTIFIC REPORTS
卷 11, 期 1, 页码 -

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NATURE PORTFOLIO
DOI: 10.1038/s41598-021-81526-8

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资金

  1. STW-Strategy research grant [P14-19_1.2]
  2. Ministry of Electronics and Information technology [13(2)-2015-CC-BT]
  3. Indo-Dutch NWO research grant BIONIC [629.002.205]
  4. [MietY-13(2)-2015-CC-BT]

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The repeatability and reproducibility of radiomic features were investigated across different scanners, slice thicknesses, tube currents, and use of IV contrast. Results showed that half of the features had good repeatability, with changes in slice thickness affecting reproducibility. A total of 108 features demonstrated both good repeatability and reproducibility, with most being wavelet and Laplacian of Gaussian features.
The repeatability and reproducibility of radiomic features extracted from CT scans need to be investigated to evaluate the temporal stability of imaging features with respect to a controlled scenario (test-retest), as well as their dependence on acquisition parameters such as slice thickness, or tube current. Only robust and stable features should be used in prognostication/prediction models to improve generalizability across multiple institutions. In this study, we investigated the repeatability and reproducibility of radiomic features with respect to three different scanners, variable slice thickness, tube current, and use of intravenous (IV) contrast medium, combining phantom studies and human subjects with non-small cell lung cancer. In all, half of the radiomic features showed good repeatability (ICC>0.9) independent of scanner model. Within acquisition protocols, changes in slice thickness was associated with poorer reproducibility compared to the use of IV contrast. Broad feature classes exhibit different behaviors, with only few features appearing to be the most stable. 108 features presented both good repeatability and reproducibility in all the experiments, most of them being wavelet and Laplacian of Gaussian features.

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