Exploring the Impact of T2-Weighted MRI Fat Saturation on Radiomics Stability for Brain Radionecrosis Prediction After Skull-Base Proton Therapy: A Pilot Study

Background/Objectives: In skull-base proton therapy, T2-weighted brain magnetic resonance (MR) imaging may be acquired with or without fat saturation (FS). The present study investigated the impact of this protocol variation on radiomics feature stability and brain radionecrosis (BRN) prediction. Methods: Paired T2-weighted FS and non-FS follow-up MR scans of proton-treated skull-base chordoma patients (n = 52) were used to assess feature stability. For BRN prediction (CTCAEv5 grade ≥ 1), baseline planning scans of chordoma and chondrosarcoma patients (n = 80) with mixed FS protocols were used. Following automated brain tissue segmentation, 1911 radiomics features were extracted from cerebrospinal fluid, gray, and/or white matter using PyRadiomics (v3.1.0). Feature stability was quantified using Lin’s concordance correlation coefficient (CCC). Several MR image- and feature-level processing configurations were explored, and the stability of combined gray and white matter features served as the reference for selecting the optimal configuration. Features were stratified based on CCC thresholds and were subjected to univariable feature selection using a Mann–Whitney U-test. Logistic regression was used for predictive modeling and its performance was measured using micro-averaged AUC within a repeated stratified cross-validation framework. Results: Radiomics features were highly sensitive to FS variations (median CCC range: 0.21–0.58). Combat harmonization without image-level processing was selected as the optimal configuration, under which 30% of features achieved CCC ≥ 0.70, and 10.7% demonstrated a CCC ≥ 0.85. Restricting modeling to a highly stable subset eliminated nearly 90% of the baseline features while significantly improving predictive performance (ΔAUC = 0.05, p < 0.001). Conclusions: A subset of radiomics features robust to FS variations was identified. Use of these stable features may mitigate the impact of protocol-induced variability in mixed-FS T2-weighted MR data while simultaneously improving BRN prediction performance. These findings are preliminary and should be interpreted cautiously, given the pilot nature of the study.