The aims of this dataset are to determine the stability of radiomics features against computed tomography (CT) parameter variations and to study their discriminative power concerning tissue classification using a 3D-printed CT phantom based on real patient data. A radiopaque 3D phantom was developed using real patient data and a potassium iodide solution paper-printing technique. Normal liver tissue and 3 lesion types (benign cyst, hemangioma, and metastasis) were manually annotated in the phantom. 240 CT series with 8 parameter variations of reconstruction algorithms, reconstruction kernels, slice thickness, and slice spacing are available.

In a preliminary study, we showed that the 8 CT parameter variation pairwise group comparisons had statistically significant differences on average in 78/86 radiomics features. On the other hand, 84% of the univariate radiomics feature tests had a successful and statistically significant differentiation of the 4 classes of liver tissue. We concluded that the differences in radiomics feature values obtained from different types of liver tissue are generally greater than the intraclass differences resulting from CT parameter variations.

The phantom was printed using the Phantom X solution detailed in the study, "Radiopaque Three-dimensional Printing: A Method to Create Realistic CT Phantoms". It relies on inkjet cartridges filled with potassium iodide solutions (600 mg/mL) with prints realized on plain paper (80 g/m2). Stacked paper sheets result in three-dimensional phantoms.  Because the printing process is very specific to the Phantom X company and no other printer can be used, we did not made printer creation file publicly available.

This dataset can serve as a reference to assess both the discriminative power and stability of radiomics features with CT variations. In addition, it can be used to develop efficient data harmonization techniques to improve robustness of (deep) radiomics features and models.  Additional information and source code related to the project can be found at https://qa4iqi.github.io/.