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Objective <p>This study aims to develop an MRI-based radiomics model for predicting the prognosis of LGGs and investigating associated immune infiltration patterns.</p> Materials and methods <p>Radiomics features were extracted from preoperative T1WI contrast-enhanced MRI images of 133 LGG patients from The Cancer Genome Atlas (TCGA) cohort. A total of 851 radiomic features were analyzed for each patient. The performance of the radiomics model was evaluated by calculating the area under the receiver operating characteristic curve (AUC). Cox proportional hazards regression identified predictors of progression-free survival (PFS). Additionally, paired RNA-seq data from the same cohort were used to explore immune infiltration patterns linked to the radiomic features.</p> Results <p>The radiomics model, constructed using 11 features, demonstrated strong predictive performance for 5-year prognosis with an AUC of 0.85 (95% CI: 0.78–0.91). Cox analysis confirmed that the radiomics score was an independent predictor of PFS. Cluster analysis revealed that patients with an immune-inflammatory phenotype exhibited higher radiomic scores and lower tumor purity, suggesting that the radiomics model correlates with altered humoral immunity.</p> Conclusion <p>MRI-derived radiomics scores effectively predict the prognosis of LGGs. Immune-inflammatory phenotypes are associated with higher radiomics scores, indicating more aggressive tumor characteristics and poorer prognosis compared to immune-desert phenotypes.</p>

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Immune infiltration differences underlying prognostic radiomics subtypes from matched MRI and RNA sequencing in patients with lower-grade gliomas

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月11日

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  • Shuangyun Hou,
  • Wenfei Li,
  • Min Fu,
  • Yanguo Li,
  • Yuemei Zhao,
  • Huanlei Zhang

摘要

Objective

This study aims to develop an MRI-based radiomics model for predicting the prognosis of LGGs and investigating associated immune infiltration patterns.

Materials and methods

Radiomics features were extracted from preoperative T1WI contrast-enhanced MRI images of 133 LGG patients from The Cancer Genome Atlas (TCGA) cohort. A total of 851 radiomic features were analyzed for each patient. The performance of the radiomics model was evaluated by calculating the area under the receiver operating characteristic curve (AUC). Cox proportional hazards regression identified predictors of progression-free survival (PFS). Additionally, paired RNA-seq data from the same cohort were used to explore immune infiltration patterns linked to the radiomic features.

Results

The radiomics model, constructed using 11 features, demonstrated strong predictive performance for 5-year prognosis with an AUC of 0.85 (95% CI: 0.78–0.91). Cox analysis confirmed that the radiomics score was an independent predictor of PFS. Cluster analysis revealed that patients with an immune-inflammatory phenotype exhibited higher radiomic scores and lower tumor purity, suggesting that the radiomics model correlates with altered humoral immunity.

Conclusion

MRI-derived radiomics scores effectively predict the prognosis of LGGs. Immune-inflammatory phenotypes are associated with higher radiomics scores, indicating more aggressive tumor characteristics and poorer prognosis compared to immune-desert phenotypes.