List of correctable feature scenarios.

Background
Greater access to clinically meaningful data from [18F]-FDG-PET images could be made possible through radiomics. However, the vulnerability of radiomic measurements to changes in image acquisition and reconstruction settings has raised concerns on their reliability in clinical practice.
Methods
Using the NEMA-IQ phantom, we evaluated the robustness of [18F]-FDG-PET radiomic features to variations in acquisition duration, reconstruction algorithm, transaxial matrix size, z-axis filtering, Gaussian smoothing, and other reconstruction algorithm-specific settings (number of iterations, subsets, updates, and penalisation factors). Feature robustness was assessed using the coefficient of variation (CV < 10%) and intraclass correlation coefficient (ICC > 0.9). Non-robust features were examined for dependencies on these parameters that could be corrected using simple mathematical equations. Using mixed-effects models, we also explored whether differences in region volume or intensity could explain the variability of feature values.
Results
Our findings demonstrated that the majority of [18F]-FDG-PET radiomic features were not robust to variations in image acquisition/reconstruction parameters, with features displaying the least stability to matrix size. Robust features mainly comprised shape-based and entropy-related measurements. Most non-robust features did not possess a dependency on acquisition/reconstruction settings that could be corrected using simple equations. The volume and intensity of interrogated regions were also shown to be likely determinants of feature variability to these settings.
Conclusions
Care should be taken when handling radiomic data extracted from heterogeneously acquired/reconstructed [18F]-FDG-PET images. Alternative strategies could be required to mitigate the effects of variations in these parameters on radiomic features.