Influence of image preprocessing on reproducibility and longitudinal repeatability analysis of radiomics features in...

Radiomics has emerged as a promising approach for extracting quantitative features from medical images to support tumor characterization and treatment response assessment. With the increasing use of magnetic resonance-guided linear accelerator (MR-Linac), ensuring the stability and reproducibility of radiomics features derived from magnetic resonance imaging (MRI) has become critical for reliable clinical applications. However, image preprocessing parameters may substantially influence feature stability. Therefore, this study aimed to evaluate the effects of image preprocessing parameters on radiomics feature stability in MRI acquired on a 1.5T MR-Linac system, with specific assessment of test-retest repeatability, longitudinal repeatability, and inter-platform reproducibility.

MRI datasets were acquired using the American College of Radiology (ACR) phantom on 1.5T MR-Linac systems for test-retest, longitudinal, and inter-platform analysis. The T1-weighted (T1w), T2-weighted (T2w), and fluid-attenuated inversion recovery (FLAIR) sequences were collected. Five regions of interest were delineated on T1w images and propagated to corresponding T2w and FLAIR sequences. Image preprocessing strategies included voxel resampling (multiple isotropic resolutions), intensity normalization (none or Z-score), and intensity discretization using bin width (BW) or bin number (BN). Feature stability was assessed using the intraclass correlation coefficient (ICC) and coefficient of variation (CV). Features with ICC values >0.9 and CV values <10% were considered robust.

Optimal preprocessing strategies varied across imaging sequences and evaluation tasks. In the test-retest analysis, the proportion of robust features reached 85.87% for T1w, 89.13% for T2w, and 90.22% for FLAIR sequences under optimal settings. In contrast, longitudinal repeatability and inter-platform reproducibility showed substantially lower stability, with robust feature proportions ranging from 42.39% to 76.09% across sequence and preprocessing configurations. Larger voxel sizes (>2 mm isotropic) consistently reduced stability across all tasks. The BN discretization method generally yielded higher proportions of robust features than the BW method; however, this advantage was sequence- and task-dependent. Z-score normalization had minimal effect when applied with BN discretization, but reduced feature stability when combined with the BW discretization.

Image preprocessing parameters significantly influence the stability of radiomics features acquired on MR-Linac. Stability varies considerably between test-retest repeatability and longitudinal repeatability or inter-platform reproducibility. Task- and sequence-specific optimization of preprocessing strategies is therefore essential before clinical implementation. Further validation in clinical datasets is acquired to support robust integration of MR-Linac radiomics into adaptive radiotherapy workflows.