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<p>Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor with a poor prognosis. Magnetic resonance imaging (MRI) is widely used for the clinical diagnosis and prognostic evaluation of GBM. This study aimed to investigate the relationship between MRI semantic features and overall survival, and to explore the underlying biological mechanisms by transcriptomic analysis. In this study, we reviewed the MRI images of 171 patients with GBM from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases and evaluated twelve MRI semantic features. Cox regression model and Kaplan-Meier survival curve were used to assess the prognostic value of the imaging features. Additionally, we investigated the relationship between imaging features and gene expression using differential gene expression and enrichment analysis in the cohort of 68 tumor samples with RNA-seq data. 171patients with GBM were included in the imaging-prognostic cohort (median age was 60.0 years and 59.6% were male). In the multivariate analyses, age (HR: 1.04, 95% CI: 1.03–1.06, <i>P</i> &lt; 0.001), ependymal extension (HR:1.88, 95% CI:1.32–2.69, <i>P</i> &lt; 0.001), contrast-enhancing tumor (CET) crossing midline (HR:2.38, 95% CI:1.16–4.91, <i>P</i> = 0.018) were significantly associated with shorter overall survival (OS). Gene set enrichment analysis (GSEA) showed that these features were significantly associated with pathways involved in inflammatory responses and tumor invasiveness, such as TNF-α signaling via NF-κB and epithelial-to-mesenchymal transition. Our study demonstrated that MRI semantic features, including ependymal extension and CET crossing the midline, can serve as prognostic indicators for patients with GBM. Additionally, several selected MRI features were found to be associated with specific biological pathways, potentially informing treatment decisions based on these distinctive semantic characteristics of GBM.</p>

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MRI semantic features as prognostic indicators and biological mechanism insights in glioblastoma multiforme

  • 期刊论文

  • 发表日期：

    2026年4月9日

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  • Yuxi Gui,
  • Jie Lou,
  • Yusheng Guo,
  • Yi Ren,
  • Bingxin Gong,
  • Yanjie Yang,
  • Yi Li,
  • Dongyong Zhu,
  • Lian Yang

摘要

Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor with a poor prognosis. Magnetic resonance imaging (MRI) is widely used for the clinical diagnosis and prognostic evaluation of GBM. This study aimed to investigate the relationship between MRI semantic features and overall survival, and to explore the underlying biological mechanisms by transcriptomic analysis. In this study, we reviewed the MRI images of 171 patients with GBM from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases and evaluated twelve MRI semantic features. Cox regression model and Kaplan-Meier survival curve were used to assess the prognostic value of the imaging features. Additionally, we investigated the relationship between imaging features and gene expression using differential gene expression and enrichment analysis in the cohort of 68 tumor samples with RNA-seq data. 171patients with GBM were included in the imaging-prognostic cohort (median age was 60.0 years and 59.6% were male). In the multivariate analyses, age (HR: 1.04, 95% CI: 1.03–1.06, P < 0.001), ependymal extension (HR:1.88, 95% CI:1.32–2.69, P < 0.001), contrast-enhancing tumor (CET) crossing midline (HR:2.38, 95% CI:1.16–4.91, P = 0.018) were significantly associated with shorter overall survival (OS). Gene set enrichment analysis (GSEA) showed that these features were significantly associated with pathways involved in inflammatory responses and tumor invasiveness, such as TNF-α signaling via NF-κB and epithelial-to-mesenchymal transition. Our study demonstrated that MRI semantic features, including ependymal extension and CET crossing the midline, can serve as prognostic indicators for patients with GBM. Additionally, several selected MRI features were found to be associated with specific biological pathways, potentially informing treatment decisions based on these distinctive semantic characteristics of GBM.