MRI radiomic signature predicts peritumoral brain edema resolution following meningioma surgery

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BACKGROUND: Intracranial meningiomas(IM) are often associated with peritumoral brain edema(PTBE), visible as hyperintensities on T2/FLAIR MRI. Postoperative persisting PTBE-like changes likely represent gliosis that, in turn, contributes to surgical morbidity. Since the human eye is unable to distinguish between PTBE and gliosis on MR images, we hypothesized that radiomic analysis of preoperative peritumoral T2/FLAIR hyperintensities could distinguish preoperatively established gliosis from reversible edema. METHODS: MRI of patients with gross totally resected IM were retrospectively analyzed. Preoperative and 1-year postoperative PTBE were segmented on MRI. One-year MRI were classified into two categories based on whether PTBE resolution exceeded 80% of the initial volume. RF were extracted from meningioma and PTBE regions on T1-contrast-enhanced, T2, and FLAIR MRI sequences. The dataset was split into training, validation, and test cohorts(70-10-20%). Feature reduction used correlation-based exclusion and recursive feature elimination with cross-validation. Nine ML algorithms were trained and evaluated, and best model's interpretability assessed using Shapley Additive Explanations(SHAP). RESULTS: 644 RF were extracted per individual from the pre and postoperative MRI of 123 operated patients. The Random Forest model utilizing 10 RF achieved the best performance (accuracy:0.91;precision:0.92;F1-score:0.92;ROC-AUC:0.94), demonstrating radiomics' utility in predicting PTBE resolution at 1-year post-surgery. SHAP analysis provided interpretability, highlighting key RF, differences between patient groups, and potential sources of algorithmic error. CONCLUSIONS: These results underscore the potential of radiomics and ML to accurately predict postoperative PTBE resolution, differentiating transient PTBE from persistent PTBE-like changes (gliosis). This study provides initial insights into the potential of advanced imaging and computational techniques for non-invasive preoperative assessment, which may contribute to more personalized surgical strategies.

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