Integrating deep learning and radiomics for preoperative glioma grading using multi-center MRI data.

Accurate preoperative glioma grading remains a critical challenge in neuro-oncology. This study presents a novel integrated approach combining deep learning architectures with radiomics features derived from multi-parametric MRI to improve preoperative glioma grading accuracy. In this retrospective multi-center study, we analyzed 847 patients with histopathologically confirmed gliomas from 5 tertiary neurosurgical centers. Multi-parametric MRI sequences (T1, T1-contrast, T2, FLAIR) were processed using a dual-stream framework where: (1) a 3D convolutional neural network extracted deep imaging features, and (2) 1,423 quantitative radiomic features were extracted and selected using a recursive feature elimination algorithm. We developed an ensemble model that integrates both feature streams with clinical variables. Model performance was evaluated through 5-fold cross-validation and external validation on an independent cohort (n = 213). The integrated model achieved superior performance (AUC = 0.946, 95% CI: 0.927-0.965) compared to radiomics-only (AUC = 0.891) or deep learning-only (AUC = 0.903) approaches for distinguishing high-grade (WHO grades III-IV) from low-grade (WHO grades I-II) gliomas. Notably, the model demonstrated robust performance across different MRI acquisition parameters (AUC = 0.921 on external validation). Subgroup analysis revealed particular efficacy in identifying isocitrate dehydrogenase (IDH) wild-type gliomas (sensitivity 0.954, specificity 0.912). The model accurately identified 89.2% of gliomas with molecular features associated with aggressive behavior but ambiguous conventional imaging characteristics. This integrated radiomics-deep learning approach significantly improves preoperative glioma grading accuracy across diverse patient populations and imaging protocols. The proposed framework offers a non-invasive tool for preoperative risk stratification, potentially informing surgical planning and treatment strategies. The model's interpretability provides insights into imaging biomarkers associated with glioma aggressiveness.