Gliomas are among the most aggressive cancers, with complex diagnostic processes. Existing glioma segmentation methods often struggle with high variability in imaging data and inadequate optimization. Furthermore, radiomic analysis is typically applied only after segmentation is finished, limiting its ability to inform the segmentation process itself. To address these challenges, we propose a novel radiomics-enhanced fused residual multiparametric 3D network (ReFRM3D) for brain tumor characterization. The framework is based on a 3D U-Net architecture and features multi-scale feature fusion, hybrid upsampling, and an extended residual skip mechanism. Additionally, we introduce a radiomic conditioning mechanism that extracts texture and intensity descriptors from an intermediate coarse segmentation and re-injects them into the decoder to refine the final output. Experimental results on BraTS2019, BraTS2020, and BraTS2021 show strong performance, with mean DSC values of 93.45%, 93.61%, and 92.06%, respectively, across whole tumor, enhancing tumor, and tumor core regions. Compared with recent models, ReFRM3D improves average DSC by 5.79%, 7.25%, and 0.96% on these datasets. Our model also generalized well on the BraTS-Africa dataset with an average DSC of 87.8%, despite differences in scanner field strength and patient demographics.

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