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: MixBranchNet incorporates a dual-branch Mixing Block that extracts spatial and spectral features in parallel through convolutional and self-attention pathways. The model was trained on multi-offset CEST images with 41 frequency offsets from a 3.0 T MRI scanner. Segmentation and genotype prediction tasks were trained separately, with the segmentation output used as the input for the genotype prediction. Manual annotations from two board-certified neuroradiologists were used as the reference standard for segmentation, while genotype labels were obtained from surgical histopathology. Model performance was assessed using the Dice coefficient for segmentation, mean probabilities (P Results: MixBranchNet achieved a Dice coefficient of 0.84 (95% CIs: 0.80-0.88) for segmentation, outperforming the full Z-spectrum-based fully convolutional network (FCN) (0.61, 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.