  Perfusion MRI (pMRI) offers valuable insights into tumor vascularity and
promises to predict tumor genotypes, thus benefiting prognosis for glioma
patients, yet effective models tailored to 4D pMRI are still lacking. This
study presents the first attempt to model 4D pMRI using a GNN-based
spatiotemporal model PerfGAT, integrating spatial information and temporal
kinetics to predict Isocitrate DeHydrogenase (IDH) mutation status in glioma
patients. Specifically, we propose a graph structure learning approach based on
edge attention and negative graphs to optimize temporal correlations modeling.
Moreover, we design a dual-attention feature fusion module to integrate
spatiotemporal features while addressing tumor-related brain regions. Further,
we develop a class-balanced augmentation methods tailored to spatiotemporal
data, which could mitigate the common label imbalance issue in clinical
datasets. Our experimental results demonstrate that the proposed method
outperforms other state-of-the-art approaches, promising to model pMRI
effectively for patient characterization.
