ArticleInternational journal of cancer2025
A strategy for multimodal integration of transcriptomics, proteomics, and radiomics data for the prediction of recurrence in patients with IDH-mutant gliomas.
Article in International journal of cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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Who cites it
7 citing papers in PubMed.
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Temporal Integration of Serum Proteomics, Metabolomics and MRI Tumor Volumetrics via Deep Learning Identifies Systemic Mediators of Glioblastoma Response to Chemoradiotherapy.Research square · 2026Article
- Beyond morphology: imaging the glioblastoma microenvironment in the era of quantitative neuro-oncology.Frontiers in medicine · 2026Review
- Exposure-aware multi-omics and artificial intelligence for biomarker discovery and precision prevention in diffuse glioma.Frontiers in immunology · 2026Review
- Ultra-Fast IntraoperativeInternational journal of molecular sciences · 2025Article
- A strategy for multimodal integration of transcriptomics, proteomics, and radiomics data for the prediction of recurrence in patients with IDH-mutant gliomas.International journal of cancer · 2025Article
- Application of Fast Integration Strategy for Multi-Omics Data and Limited Random Forest Model in Survival Prediction of Glioblastoma.IET systems biologyArticle
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
Isocitrate dehydrogenase-mutant gliomas are lethal brain cancers that impair quality of life in young adults. Although less aggressive than glioblastomas, IDH-mutant gliomas invariably progress to incurable disease with unpredictable recurrence. A better classification of patient risk of recurrence is needed. Here, we describe a multimodal analytical pipeline integrating imaging, transcriptomic, and proteomic profiles using machine learning to improve patient stratification with novel signatures of patient risk of recurrence based on gene expression, protein level, and imaging. Additionally, we describe the biological characteristics of IDH-mutant glioma subtypes categorized by positron emission tomography (PET) and histology, and we reinforce the integration of positron emission tomography (PET) metrics in the classification of IDH-mutant gliomas. We identify a gene signature (KRT19, RUNX3, and SCRT2) and a protein signature (ATXN10, EIF4H, ITGAV, and NCAM1) associated with an increased risk of early recurrence. Furthermore, we integrated these markers with imaging-derived features, obtaining a better stratification of IDH-mutant glioma patients in comparison to histomolecular classification alone.
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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.