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.
Shi et al. (Tue,) studied this question.