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

Abstract

Purpose: 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.

Methods: 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 < 20% and 20% < η² < 40% as representative of a "negligible" and a "small" dependence respectively. The Cohen's d was used as discriminatory power to quantify the separation of two distributions.

Results: We found the discretization method based on fixed-bin-number (FBN) to outperform the one based on fixed-bin-size in units of SUV (FBS), as the latter shows a higher SUV dependence, with 30 RMs showing η² > 20%. FBN was also less influenced by the acquisition and reconstruction setup:with FBN 37 RMs had η² < 40%, only 20 with FBS. Most RMs showed a good discriminatory power among heterogeneous PET signals (for FBN: 29 out of 39 RMs with d > 3).

Conclusions: For RMs analysis, FBN should be preferred. A group of 21 RMs was suggested for PET radiomics analysis.

Keywords: Image analysis; PET; Radiomics.