He Jie, Liu Nan, Li Lin, Hu Hongjie
Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, People's Republic of China.
Department of Translational Medicine and Clinical Research, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, People's Republic of China.
Discov Oncol. 2025 May 30;16(1):962. doi: 10.1007/s12672-025-02721-x.
This study aimed to assess the prognostic significance of CD40LG and a related radiomics model in high-grade gliomas.
This retrospective cohort study utilized data from TCGA (n = 298) and TCIA (n = 89) following STROBE guidelines. From The Cancer Genome Atlas (TCGA), HGGs with genomic and clinical data were analyzed to establish CD40LG's prognostic value through Kaplan-Meier survival analysis and multivariate Cox regression. A radiomic model, based on TCGA data and matched MRI T1 images from The Cancer Imaging Archive (TCIA), was built to predict CD40LG levels. Radiomic features were extracted via PyRadiomics, filtered by 1000-repeat LASSO regression, and validated through 5-fold cross-validation. An independent cohort (n = 182) tested the model's prognostic utility. Subsequently, a prognostic model and nomogram were developed.
Kaplan-Meier curves indicated a significant association between CD40LG expression and overall survival. CD40LG emerged as a crucial risk factor in both univariate and multivariate analyses. Immune cell infiltration analyses highlighted CD40LG's connection to the tumor immune microenvironment. A radiomic model, constructed using LASSO regression and five features, successfully predicted CD40LG expression pre-surgery. Combining the model's Rad-scores with clinical data, we created an effective prognostic model.
CD40LG expression correlates with high-grade glioma prognosis. Our MRI-based radiomic signature predicted CD40LG expression and prognosis, offering potential guidance for treatment decisions and future research.
本研究旨在评估CD40LG及相关影像组学模型在高级别胶质瘤中的预后意义。
本回顾性队列研究遵循STROBE指南，利用了来自TCGA（n = 298）和TCIA（n = 89）的数据。从癌症基因组图谱（TCGA）中分析具有基因组和临床数据的高级别胶质瘤，通过Kaplan-Meier生存分析和多变量Cox回归确定CD40LG的预后价值。基于TCGA数据和来自癌症影像存档（TCIA）的匹配MRI T1图像构建了一个影像组学模型，以预测CD40LG水平。通过PyRadiomics提取影像组学特征，经1000次重复的LASSO回归进行筛选，并通过五折交叉验证进行验证。一个独立队列（n = 182）测试了该模型的预后效用。随后，开发了一个预后模型和列线图。
Kaplan-Meier曲线表明CD40LG表达与总生存期之间存在显著关联。在单变量和多变量分析中，CD40LG均为关键危险因素。免疫细胞浸润分析突出了CD40LG与肿瘤免疫微环境的联系。使用LASSO回归和五个特征构建的影像组学模型成功预测了术前CD40LG的表达。将该模型的Rad分数与临床数据相结合，我们创建了一个有效的预后模型。
CD40LG表达与高级别胶质瘤的预后相关。我们基于MRI的影像组学特征预测了CD40LG的表达和预后，为治疗决策和未来研究提供了潜在指导。
