Heterogeneity phenotypes in recurrent glioblastoma: a multimodal MRI-based spatial mapping framework for precision treatment.

backgroundTo develop a multimodal magnetic resonance imaging (MRI)-based spatial mapping framework for quantitatively characterizing intratumoral heterogeneity in recurrent glioblastoma (rGBM), identifying distinct imaging subregions, and classifying heterogeneity phenotypes predictive of treatment response and survival outcomes.

methodsA total of 140 rGBM patients were recruited and underwent standardized diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Pixel-wise colocalization of apparent diffusion coefficient (ADC) and DCE-MRI features identified four Multimodal Imaging Subregions (MIS). Entropy and Moran's I quantified heterogeneity, and hierarchical clustering defined imaging phenotypes. Treatment response to 1-(2-chloroethyl)-3-cyclohexyl-1-nitrosourea (CCNU), bevacizumab (Bev) + stereotactic radiotherapy (SRT), and Bev + CCNU was assessed by volumetric and component-level changes. Survival analyses were performed using Kaplan-Meier and multivariate Cox models.

resultsMIS4, defined by low ADC and slow-rising enhancement, was consistently treatment-resistant. Three imaging phenotypes with distinct heterogeneity patterns demonstrated significant prognostic stratification across regimens. Phenotype A showed the best outcomes under Bev-based regimens, while Phenotype B responded better to CCNU. Imaging phenotypes independently predicted progression-free survival (PFS) and overall survival (OS).

conclusionThis framework enables spatially resolved, phenotype-based analysis of rGBM heterogeneity using routine MRI. Imaging phenotypes serve as non-invasive biomarkers to guide personalized treatment planning and outcome prediction in recurrent glioblastoma. CLINICAL TRIAL REGISTRATION NUMBER: Not applicable.