  In this study we assessed the repeatability of the values of radiomics
features for small prostate tumors using test-retest Multiparametric Magnetic
Resonance Imaging (mpMRI) images. The premise of radiomics is that quantitative
image features can serve as biomarkers characterizing disease. For such
biomarkers to be useful, repeatability is a basic requirement, meaning its
value must remain stable between two scans, if the conditions remain stable. We
investigated repeatability of radiomics features under various preprocessing
and extraction configurations including various image normalization schemes,
different image pre-filtering, 2D vs 3D texture computation, and different bin
widths for image discretization. Image registration as means to re-identify
regions of interest across time points was evaluated against human-expert
segmented regions in both time points. Even though we found many radiomics
features and preprocessing combinations with a high repeatability (Intraclass
Correlation Coefficient (ICC) > 0.85), our results indicate that overall the
repeatability is highly sensitive to the processing parameters (under certain
configurations, it can be below 0.0). Image normalization, using a variety of
approaches considered, did not result in consistent improvements in
repeatability. There was also no consistent improvement of repeatability
through the use of pre-filtering options, or by using image registration
between timepoints to improve consistency of the region of interest
localization. Based on these results we urge caution when interpreting
radiomics features and advise paying close attention to the processing
configuration details of reported results. Furthermore, we advocate reporting
all processing details in radiomics studies and strongly recommend making the
implementation available.
