Artificial intelligence and radiogenomics in the peritumoral microenvironment of gliomas: a systematic review of diagnostic performance and clinical implications

Abstract

Introduction Delineating infiltrative peritumoral edema (PTE) in gliomas remains challenging due to acquisition variability and the lack of histopathological validation. We systematically assessed artificial intelligence (AI) and radiomics models for peritumoral characterization.Methods Following PRISMA, we searched five databases on July 18, 2025, for peritumoral AI/radiomics studies predicting tumor infiltration, recurrence, or molecular status. Quality was evaluated using Prediction model Risk Of Bias Assessment Tool (PROBAST).Results Nineteen studies (6,851 patients) were included. Deep learning (DL) (nnU-Net, CNNs) identified PTE infiltration with high performance, achieving >80% sensitivity for recurrence (OR = 9.97), and outperforming radiomics (AUC = 0.61–0.98). Multimodal MRI (DTI/DSC+FLAIR) showed higher accuracy (0.90–0.98) than standard sequences. Synthetic MRI enabled standardized IDH/1p/19q prediction (AUC = 0.95/0.86). Voxel-wise histopathological validation was extremely rare (1 study).Discussion AI-based peritumoral phenotyping shows strong non-invasive potential. However, prospective clinical trials are essential to validate these predictive maps before their clinical use in surgical or radiotherapy planning.