ArticlePhysics and imaging in radiation oncology2026
Repeatability and reproducibility of MRI-derived radiomics on a 0.35 T MR-Linac using a tissue-mimicking phantom.
Article in Physics and imaging in radiation oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 <5% and/or an intraclass correlation coefficient (2,1) > 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.
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