Abstract
Background and purpose
Magnetic resonance-guided radiotherapy provides real-time soft-tissue-based targeting during dose delivery. Beyond image guidance, magnetic resonance linear accelerator (MR-Linac) systems offer new opportunities for developing magnetic resonance imaging (MRI)-derived radiomics biomarkers. However, the reproducibility of these biomarkers in a low-field MR-Linac environment remains unclear.Materials and methods
This study evaluated the repeatability and reproducibility of MRI-derived radiomic features acquired on a 0.35 T MR-Linac using a tissue-mimicking phantom containing multiple tissue-equivalent materials. Fifty balanced steady-state free precession acquisitions were performed over 10 sessions, enabling both intra- and inter-session analyses. Forty-eight preprocessing pipelines combining normalization, discretization, and bias-field correction were assessed using the coefficient of variation and intraclass correlation coefficient for 68 radiomic features across 12 defined regions of interest.Results
The combination of N4 bias correction and z-score normalization yielded the highest overall stability, with several first-order (e.g. Entropy, Mean) and texture-based (e.g. glcm_DifferenceEntropy and glrlm_ShortRunEmphasis) features showing a coefficient of variation 0.85 across phantom materials. Stability varied with phantom composition, with higher reproducibility in homogeneous PVP-40 and water inserts compared to fat or fibroglandular compartments. Comparison with previous 0.35 T MR-Linac studies identified a consistent subset of robust features, supporting their potential as standardized MRI-derived radiomic biomarkers.Conclusions
These findings demonstrated the critical role of preprocessing and tissue composition in feature reproducibility and highlighted the importance of tissue-mimicking phantoms for validating MR-Linac-based quantitative imaging pipelines.