MixBranchNet: a task-adaptive network for glioma segmentation and genotype prediction by exploiting spatial-spectral correlations in CEST MRI

Objective: To develop a task-adaptive deep learning-based model, termed MixBranchNet, that leverages spatial-spectral correlations in chemical exchange saturation transfer (CEST) MRI for improved glioma segmentation and genotype prediction. Methods: ), accuracy, sensitivity, specificity, F1-score, and AUC for genotype prediction. Five-fold cross-validation was performed within the development cohort using strict patient-level partitioning. A hold-out test set was defined prior to cross-validation and remained fully isolated from training, validation, and model selection procedures. Results: < 0.05). Conclusion: MixBranchNet establishes a methodological foundation for spatial-spectral deep learning in CEST MRI and demonstrates encouraging performance for glioma segmentation and genotype prediction within the current single-center cohort.