Quantifying Radiomic Texture Characterization Performance on Image Resampling and Discretization

Abstract

Purpose: To develop a novel radiomic quantification framework to quantify the impact of image resampling and discretization on radiomic texture characterization performance.Methods: The study employed 251 CT scans of a Credence Cartridge phantom (consisting of 10 texture materials) with different image acquisition parameters. Each material was segmented using a pre-defined cylindrical mask. Different image pre-processing workflows including 5 resampling methods (no resampling, trilinear, and nearest resampling to both 1mm³ and 5mm³) and 8 discretization methods (fixed bin size of 25,50,75,100 and fixed bin counts of 8,16,32,64) were randomly applied. 75 radiomic texture features (including 24GLCM-based, 16GLRLM-based, 16GLSZM-based, 14GLDM-based, and 5NGTDM-based) were extracted from each material to characterize its textural attributes. Three machine learning models including logistic regression (LR), random forest (RF), and supporting vector machine (SVM) were developed to identify 10 materials based on the extracted features, and grid search was adopted to optimize the model hyperparameters. The model performance was evaluated on 10-class macro-AUC with 5-fold cross-validation.Results: Three models successfully classified 10 materials with macro-AUC=0.9941±0.0081, 0.9979±0.0040, and 0.9957±0.0067 for LR, RF, and SVM, respectively. Across 8 different discretization methods, an increasing trend in performance can be observed when the original CT was discretized to a larger gray level range: performance improved by 0.0038 with bin sizes decreasing from 100-25, and by 0.0074 with bin counts increasing from 8 to 64. Among 5 resampling methods, resampling CT to an isotropic voxel spacing showed an improved prediction performance (0.9942±0.0075/0.9944±0.0073 for trilinear/nearest resampling to 1mm³ and 5mm³, respectively) over no interpolation (0.9862±0.0228), with minimal performance discrepancies observed among two different interpolation algorithms. In addition, no statistically significant differences were observed across five folds.Conclusion: The proposed framework successfully quantified the dependence of radiomics texture characterization on image resampling and discretization.