Improving prognostic stratification in adult-type diffuse glioma using unsupervised spatiotemporal perfusion-based tumor habitat analysis

Motivation: Gliomas exhibit heterogeneity within and between tumors, affecting treatment responses and patient outcomes. Goal(s): This study aimed to investigate the association between tumor habitat heterogeneity, derived from DSC-MRI imaging, and progression-free survival (PFS) as well as overall survival (OS) in patients with diffuse gliomas. Approach: We employed a vector-quantized variational autoencoder (VQ-VAE) to analyze DSC-MRI signal data, enabling us to categorize tumors into distinct habitats and compute a Tumor Habitat Score (THS). The prognostic significance of the THS was evaluated using Cox regression. Results: The THS demonstrated independent predictive value for PFS and OS. Kaplan-Meier analysis further substantiated its prognostic relevance. Impact: The Tumor Habitat Score represents an imaging biomarker that allows clinicians to stratify diffuse glioma patients more accurately based on survival risk. This facilitates personalized treatment decisions, potentially improving outcomes by identifying patients who might benefit most from targeted therapies.