Evidence map›Paper›PMID 40445501›Full record

ArticleDiscover oncology2025

Enhanced magnetic resonance imaging-based radiomics predicts CD40LG expression and survival in high-grade gliomas: a retrospective study.

Jie He, Nan Liu, Lin Li, Hongjie Hu

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Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jie HeDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, People's Republic of China.
Nan LiuDepartment of Translational Medicine and Clinical Research, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, People's Republic of China.
Lin LiDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, People's Republic of China.
Hongjie HuDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, People's Republic of China. hongjiehu@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aimed to assess the prognostic significance of CD40LG and a related radiomics model in high-grade gliomas.

methodsThis 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.

resultsKaplan-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.

conclusionsCD40LG 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.

Indexed as

CD40LGHigh-grade gliomaMRIPrediction modelRadiomic

Identifiers

PMID40445501
PMCPMC12125465

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.