PET textural features stability and pattern discrimination power for radiomics analysis: An “ad-hoc” phantoms study

•The impact of quantization strategies for PET radiomics was assessed.•The images processing chain impact on PET radiomics metrics was measured.•Fixed bin number quantization was found to outperform fixed bin size. The analysis of PET images by textural features, also known as radiomics, shows promising results in tumor characterization. However, radiomic metrics (RMs) analysis is currently not standardized and the impact of the whole processing chain still needs deep investigation. We characterized the impact on RM values of: i) two discretization methods, ii) acquisition statistics, and iii) reconstruction algorithm. The influence of tumor volume and standardized-uptake-value (SUV) on RM was also investigated. The Chang-Gung-Image-Texture-Analysis (CGITA) software was used to calculate 39 RMs using phantom data. Thirty noise realizations were acquired to measure statistical effect size indicators for each RM. The parameter η2 (fraction of variance explained by the nuisance factor) was used to assess the effect of categorical variables, considering η2