Multimodal artificial intelligence integrating peritumoral MRI and clinical data stratifies meningioma recurrence risk after radiotherapy

Reliable preoperative risk stratification remains challenging for meningioma patients receiving adjuvant radiotherapy, particularly in real-world settings where complete multimodal datasets are difficult to assemble. This retrospective multicenter study developed and externally validated a multimodal artificial intelligence model integrating peritumoral multiparametric MRI and clinicopathological data to predict post-radiotherapy recurrence. A total of 250 meningioma patients treated with adjuvant radiotherapy across three neurosurgical centers were included. Preoperative T1-weighted, T2-weighted, and contrast-enhanced T1-weighted MRI were analyzed using a 2.5D ResNet-50 framework across three spatial contexts: tumor only, tumor plus 1-cm peritumoral margin, and tumor plus 2-cm peritumoral margin. The 1-cm peritumoral region provided the most transferable imaging representation and was selected for downstream modeling. Deep learning, radiomics, and clinicopathological features were evaluated alone and in late-fusion Cox models. Radiomics showed high apparent training performance but limited external generalizability, whereas deep learning features demonstrated more stable cross-center performance. The final clinical–deep learning fusion model achieved the best overall discrimination, with a mean C-index of 0.856 across cohorts, showed favorable calibration and clinical net benefit, and stratified patients into clinically distinct recurrence-risk groups. These findings support peritumoral MRI-based multimodal AI as a practical tool for recurrence risk stratification after adjuvant radiotherapy in meningioma.