Developing a Platform to Analyze the Dependence of Radiomic Features on Different CT Imaging Parameters

Abstract

Purpose: To develop a framework to quantify the impact of different CT imaging parameters on the variations of radiomic features and to investigate the effectiveness of different image processing methods on the reduction of radiomic feature variations.Method: A publicly available CT image dataset (Credence Cartridge Radiomics Phantom CT scans) acquired on a phantom consisting of different texture materials was used in this study. 251 scans were divided into 5 groups. In each group, only one of the following imaging parameters changed: slice thickness, pixel size, convolutional kernel, exposure (mAs), and scanner model. 92 radiomic features from intensity, intensity histogram, GLCM, GLRLM, and GLSZM groups were extracted from the same region of interest (ROI) in each scan using an in-house application. The coefficient of variation (CV) was used to measure the variation of radiomic features due to each imaging parameter. Three preprocessing methods, resampling, gray level rebinning, and image filtering were tested for their effectiveness in reducing feature variations.Result: The proposed workflow identified individual features and groups of features with high variation and showed responses of each feature to different image preprocessing methods. The convolutional kernel of scanners caused the largest variations in calculated features in general, while exposure had low influence on features in all categories. GLSZM features showed higher sensitivity to pixel size and slice thickness due to the dependence of number of voxels in a gray level zone to the voxel size. Image preprocessing did not improve feature robustness in most cases. Conclusion: This study demonstrated the ability to reveal the relationships between radiomic feature variations, imaging parameters, and image correction methods. The proposed workflow can be used to study the feature robustness prior to the application of any radiomic features in multi-institutional studies.