ArticleJournal of translational medicine2026
Deciphering the glioblastoma microenvironmental landscape with multi-modal radiogenomics to guide prognosis and personalized therapy.
Article in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
8 authors.
Funding
Abstract
backgroundGlioblastoma (GBM) is a highly heterogeneous and treatment-refractory tumor, where the tumor microenvironment (TME) plays a central role in shaping progression and therapeutic response. However, the molecular and cellular basis of TME-linked heterogeneity, and how it can be captured through noninvasive imaging, remains poorly understood.
objectiveThis study aims to establish a noninvasive framework for characterizing GBM heterogeneity by bridging radiomic features (RFs) with TME architecture, and to identify novel druggable vulnerabilities for personalized treatment.
methodsWe analyzed magnetic resonance imaging (MRI)-derived RFs to identify prognostic RFs characterizing tumor heterogeneity. These RFs were correlated with TME composition through integrated analysis of single-cell transcriptomic data and functional enrichment. We utilized a ranking-based computational approach to evaluate gene set activity at single-cell resolution, assessing the enrichment of critical gene subsets within individual cells' expressed genes. Drug sensitivity was assessed by matching RF-associated gene signatures with pharmacogenomic perturbation profiles.
resultsLeveraging noninvasive MRI, we identified 31 prognostic RFs that effectively stratified patients into distinct risk groups (C-index = 0.84; HR = 2.16, p < 0.001). These RFs showed significant associations with key dimensions of TME heterogeneity, showing significant associations with specific cellular states-including neural progenitor cell-like (NPC-like)/oligodendrocyte progenitor cell-like (OPC-like) tumor subclasses, macrophages, and myeloid-derived suppressor cells (MDSCs)-as revealed by single-cell RNA-sequencing (scRNA-seq) analysis. Computational drug screening based on these associations identified targeted agents capable of reversing high-risk expression patterns linked to specific RFs, thereby suggesting potential therapeutic strategies aligned with individual TME profiles.
conclusionOur findings indicate that combining imaging-derived RFs with transcriptomic profiling of the TME offers a promising approach to decode GBM heterogeneity and uncover therapeutic opportunities. This multimodal strategy enables noninvasive stratification and may aid in the design of personalized treatment approaches in GBM.
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