The Impact of Preprocessing on the PET-CT Radiomics Features in Non-small Cell Lung Cancer

Purpose: This study aimed to investigate the impact of image preprocessing steps, including Gray Level Discretization (GLD) and different Interpolation Algorithms (IA) on 18F-Fluorodeoxyglucose (18F-FDG) radiomics features in Non-Small Cell Lung Cancer (NSCLC). Materials and Methods: One hundred and seventy-two radiomics features from the first-, second-, and higher-order statistic features were calculated from a set of Positron Emission Tomography/Computed Tomography (PET/CT) images of 20 non-small cell lung cancer delineated tumors with volumes ranging from 10 to 418 cm3 regarding five intensity discretization schemes with the number of gray levels of 16, 32, 64, 128, and 256, and four Interpolation algorithms, including nearest neighbor, tricubic convolution and tricubic spline interpolation, and trilinear were used. Segmentation was based on 3D region growing-based. The Intraclass Correlation Coefficient (ICC), Overall Concordance Correlation Coefficient (OCCC), and Coefficient Of Variations (COV) were calculated to demonstrate the features' variability and select robust features. ICC and OCCC < 0.5 presented weak reliability, ICC and OCCC between 0.5 and 0.75 illustrated appropriate reliability, values within 0.75 and 0.9 showed satisfying reliability, and values higher than 0.90 indicate exceptional reliability. Besides, features with less than 10% COV have been selected as robust features. Results: All morphology family (except four features), statistic, and Intensity volume histogram families were not affected by GLD and IA. And the rest of them, 10 and 61 features showed COV ≤ 5% against GLD and IA, respectively. Ten and 80 features showed excellent reliability (ICC values greater than 0.90) against GLD and IA. Eight and 60 features showed OCCC≥0.90 against GLD and IA, respectively. Based on our results Inverse difference normalized and Inverse difference moment normalized from Grey Level Co-occurrence Matrix (GLCM) were the most robust features against GLD and Skewness from intensity histogram family and Inverse difference normalized and Inverse difference moment normalized from GLCM were the most robust features against IA. Conclusion: Preprocessing can substantially impact the 18F-FDG PET image radiomic features in NSCLC. The impact of gray level discretization on radiomics features is significant and more than Interpolation algorithms.