Observational studyNeurosurgical review2025
Survival and immune microenvironment prediction of glioma based on MRI imaging genomics method: a retrospective observational study.
Observational study in Neurosurgical review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Imaging genomics of cancer: a bibliometric analysis and review.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Review
- MDL-CA: a multimodal deep learning approach with a cross attention mechanism for accurate brain cancer diagnosis.Frontiers in public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
Abstract
Glioma is characterized by high heterogeneity and poor prognosis. Attempts have been made to understand its diversity in both genetic expressions and radiomic characteristics, while few integrated the two omics in predicting survival of glioma. This study was intended to investigate the connection between glioma imaging and genome, and examine its predictive value in glioma mortality risk and tumor immune microenvironment (TIME). Clinical, transcriptomics and radiomics data were obtained from public datasets and patients in our center. Correlation analysis between gene expression and radiomic feature (RF) was performed, followed by survival analysis to select RF-related genes (RFRGs) and gene expression-related RFs (GRRFs). After that, RFRGs and GRRFs were used to construct mortality risk prediction model of all glioma and isocitrate dehydrogenase (IDH) wild type (WT) glioma. The association between RFRGs and TIME was explored. Six cohorts composed of 1,754 glioma patients were included. Thirty-five genes and eighty-two RFs demonstrated high correlation with each other. Gene score based on RFRGs was independent predictor of both glioma (P < 0.05) and IDH-WT glioma (P < 0.05). Same score based on GRRFs was also able to stratify risk of both glioma (P < 0.0001) and IDH-WT glioma (P < 0.0001), with nomograms constructed separately. The TIME of gliomas predicted with RFRGs' score found mismatched risk of death with immune response. RFRGs and GRRFs were able to predict glioma mortality risk and TIME. Further studies could validate our results and explore this genome-imaging interactions.
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Registered trials
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 OpenQuestion graph.