Biology-informed risk stratification of glioblastoma by integrating MRI-based intratumoral heterogeneity with...

Refined risk stratification before randomization is clinically important for reducing prognostic imbalance across study arms when evaluating novel therapies for glioblastoma. However, current artificial intelligence-assisted prognostic models are often limited by complex computational pipelines, limited bedside applicability, and insufficient biological interpretability. This study aimed to develop a prognostic model, supported by an online-accessible platform, for individualized risk stratification of glioblastoma using MRI-based intratumoral heterogeneity and routinely available clinical features, and to investigate the biological meaning of model-driven risk stratification.

This retrospective multicenter study included 836 patients with isocitrate dehydrogenase-wildtype glioblastoma from six centers between October 1996 and May 2025. The habitat risk score (HRS) for each patient was derived from a proposed intratumoral heterogeneity index and quantitative metrics extracted from three-dimensional preoperative MRI-based vascular habitat mappings. Independent predictors of overall survival (OS) were identified using Cox proportional hazards regression analysis, and three prognostic models (HRS model, clinical model, and radio-clinical model) were developed and validated in spatial and temporal external sets. Model interpretability was assessed using time-stratified Shapley additive explanations (SHAP) analysis. A web-based interactive platform was implemented for rapid individualized risk assessment. The biological meaning of model-driven risk stratification was explored using transcriptomic and histologic profiling.

HRS, Karnofsky performance status (KPS), O⁶-methylguanine-DNA methyltransferase promoter methylation status, and extent of resection were identified as independent predictors of OS, with KPS and HRS contributing most strongly to survival prediction in SHAP analysis. The radio-clinical model demonstrated good predictive performance and outperformed the clinical model and the HRS model, with C-indexes of 0.74 and 0.77 in the spatial and temporal validation sets, respectively. It also effectively stratified patients into low- and high-risk groups regardless of first-line therapeutic regimen (log-rank, P < 0.05). Mechanistically, high-risk tumors showed increased tumor stemness and expanded HIF1α-positive regions, whereas low-risk tumors exhibited an immune-stimulatory phenotype. The deployed web-based platform enabled rapid patient-specific risk estimation to support bedside application.

This study establishes an interpretable and clinically deployable framework for glioblastoma risk stratification by integrating imaging-derived intratumoral heterogeneity with routine clinical features, without requiring complex computational infrastructure. The model also provides biologically grounded insights into model-driven risk stratification.