A heterogeneous phantom study for investigating the stability of PET images radiomic features with varying reconstruction settings

Abstract

The purpose of this work was to assess the capability of radiomic features in distinguishing PET image regions with different uptake patterns. Furthermore, we assessed the stability of PET radiomic features with varying image reconstruction settings. An in-house phantom was designed and constructed, consisting of homogenous and heterogenous artificial phantom inserts. Four artificially constructed inserts were placed into a water filled phantom and filled with varying levels of radioactivity to simulate homogeneous and heterogeneous uptake patterns. The phantom was imaged for 80 min. PET images were reconstructed whilst varying reconstruction parameters. The parameters adjusted included, number of ordered subsets, number of iterations, use of time-of-flight and filter cut off. Regions of interest (ROI) were established by segmentation of the phantom inserts from the reconstructed images. In total seventy eight 3D radiomic features for each ROI with unique reconstructed parameters were extracted. The Friedman test was used to determine the statistical power of each radiomic feature in differentiating phantom inserts with different hetero/homogeneous configurations. The Coefficient of Variation (COV) of each feature, with respect to the reconstruction setting was used to determine feature stability. Forty three out of seventy eight radiomic features were found to be stable (COV ≤ 5%) against all reconstruction settings. To provide any utility, stable features are required to differentiate between regions with different hetro/homogeneity. Of the forty three stable features, fifteen (35%) features showed a statistically significant difference between the artificially constructed inserts. Such features included GLCM (Difference average, Difference entropy, Dissimilarity and Inverse difference), GLRL (Long run emphasis, Grey level non uniformity and Run percentage) and NGTDM (Complexity and Strength). The finding of this work suggests that radiomic features are capable of distinguishing between radioactive distribution patterns that demonstrate different levels of heterogeneity. Therefore, radiomic features could serve as an adjuvant diagnostic tool along with traditional imaging. However, the choice of the radiomic features needs to account for variability introduced when different reconstruction settings are used. Standardization of PET image reconstruction settings across sites performing radiomic analysis in multi-centre trials should be considered.

A comparison of 18F-FDG PET-based radiomics and deep learning in predicting regional lymph node metastasis in patients with resectable lung adenocarcinoma: a cross-scanner and temporal validation study

“…Although image reconstruction shows varying effects on the stability of radiomic features, the study by Shiri et al . has shown that first-order entropy is robust against different reconstruction settings, which is consistent with the results of our study [53,56].…”