Gliomas Analysis via Multimodal MRI-Deep Learning Fusion: Technical Innovations in Segmentation, Molecular Subtyping, and Clinical Translation Pathways

The integration of multimodal MRI and deep learning is reshaping glioma diagnosis and treatment, shifting from experience-dependent to data-driven paradigms. Conventional radiology, limited by subjective qualitative assessment, fails to fully quantify glioma heterogeneity, whereas deep learning addresses multidimensional data complexity through cross-modal feature fusion-particularly via Transformer-3D CNN hybrid models with cross-modal attention mechanisms. These models have enhanced glioma segmentation accuracy to a Dice coefficient of 0.92 and enabled noninvasive prediction of critical molecular markers (eg, IDH mutation), while uncovering biological links between imaging features and EGFR/PI3K-AKT signaling pathways. Clinically, this framework predicts glioma recurrence 3-6 months earlier and traces metastatic brain tumor primary lesions with 87.5% accuracy. However, challenges remain, including data heterogeneity, poor model interpretability, and ethical constraints, which demand standardized protocols for clinical translation. Future efforts will focus on integrating multi-omics data, developing real-time decision systems, and establishing evidence-based medical frameworks via interdisciplinary collaboration to achieve personalized whole-process glioma management. This review systematically synthesizes recent advances in multimodal MRI-deep learning fusion for glioma care, clarifies technical development trajectories, addresses core bottlenecks (eg, cross-center data discrepancies, clinical translation latency), and provides a theoretical basis for translating these technologies into clinical practice.