ArticleMolecular imaging and biology2025
Radiogenomic Profiling for Survival Analysis in Gastric Cancer: Integrating CT Imaging, Gene Expression, and Clinical Data.
Article in Molecular imaging and biology, 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
- Artificial Intelligence in Gastric Cancer: Diagnostic, Prognostic, and Predictive Developments, Evidence Maturity, and Translational Challenges.Cancer reports (Hoboken, N.J.) · 2026Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aims to integrate CT (Computed Tomography) radiomic features, gene expression profiles, and clinical data to identify radiogenomic biomarkers and improve overall survival prediction in gastric cancer (GC) patients. PROCEDURES: Quantitative radiomic analysis was performed on 37 GC CT images, alongside gene expression and clinical data, to identify biomarkers associated with overall survival. Tumor segmentation and radiomic feature extraction were followed by Pearson correlation for feature selection. Gene Set Enrichment Analysis (GSEA) identified pathways linking gene expression changes with radiomic features. Regression models were applied to explore the relationships between these pathways, radiomic features, and clinical data in survival prediction.
resultsA total of 107 radiomic features were extracted, with 46 radiomic features, 1,032 genes, and one clinical feature (age) selected for further analysis. GSEA identified 29 significant KEGG pathways, mainly involving immune, signal transduction, and catabolism pathways. In survival analysis, the SVM model performed best, identifying age, genes CSF1R and CXCL12, and image features ShortRunHighGrayLevelEmphasis and Idn (Inverse Difference Normalized) as independent predictors.
conclusionThis study highlights the potential of integrating imaging, genomics, and clinical data for prognosis in GC patients, with identified genes suggesting new radiogenomic biomarker candidates for future evaluation.
Indexed as
Identifiers
40374970What OpenQuestion holds
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.