ArticleNature communications2025
Multimodal fusion of radio-pathology and proteogenomics identify integrated glioma subtypes with prognostic and therapeutic opportunities.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.Frontiers in immunology · 2025Pooled it
- AI-bioinformatics synergy for precision medicine in brain diseases: integrating radiomics and multi-omics data.Briefings in bioinformatics · 2026Review
- Article
- STRAP promotes hepatocellular carcinoma progression through activation of an STRAP/Wnt-β-catenin/STRAP positive feedback loop.iScience · 2026Article
- A unified vision-language model for precision oncology and biomarker prediction in neuroblastoma.Nature communications · 2026Article
- Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence.Science advances · 2026Article
- Ligand-Receptor Interaction Combined with Histopathology Improves Glioma Prognostic Model.Biomedicines · 2026Article
- Multi-omic characterization of nasopharyngeal carcinoma delineates the subtype-specific landscape of response to induction chemotherapy.Nature cancer · 2026Article
- Radiation-activated astrocytes promote tumor progression through CXCL12 secretion and induce resistance to anti-PD-1 immunotherapy.Translational cancer research · 2026Article
- Proteogenomics: decoding cancer in multiple dimensions.MedScience · 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
- Article
- More than meets the eye: decoding glioma genomes in MRI pixel patterns.European radiology · 2026Article
- A Sensor-Oriented Multimodal Medical Data Acquisition and Modeling Framework for Tumor Grading and Treatment Response Analysis.Sensors (Basel, Switzerland) · 2026Article
- Interpretable deep learning-based hierarchical multi-modal fusion model for predicting HER2 expression in gastric cancer.Frontiers in oncology · 2026Article
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.Frontiers in immunology · 2026Review
- Integrative Multi-Omics Analysis Reveals the Characteristic Metabolic Signature of Glioma and Enables Plasma-Based Liquid Biopsy.Research (Washington, D.C.) · 2026Article
- A roadmap for medical large language models: a review of foundations, applications, and challenges.Military Medical Research · 2026Review
- P4HB regulates tumor-associated macrophage polarization and chemotaxis by enhancing IL-6 cytokine secretion in glioblastoma.Annals of medicine and surgery (2012) · 2025Article
Corrections and comments
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37 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integrating multimodal data can uncover causal features hidden in single-modality analyses, offering a comprehensive understanding of disease complexity. This study introduces a multimodal fusion subtyping (MOFS) framework that integrates radiological, pathological, genomic, transcriptomic, and proteomic data from 122 patients with IDH-wildtype adult glioma, identifying three subtypes: MOFS1 (proneural) with favorable prognosis, elevated neurodevelopmental activity, and abundant neurocyte infiltration; MOFS2 (proliferative) with the worst prognosis, superior proliferative activity, and genome instability; MOFS3 (TME-rich) with intermediate prognosis, abundant immune and stromal components, and sensitive to anti-PD-1 immunotherapy. STRAP emerges as a prognostic biomarker and potential therapeutic target for MOFS2, associated with its proliferative phenotype. Stromal infiltration in MOFS3 serves as a crucial prognostic indicator, allowing for further prognostic stratification. Additionally, we develop a deep neural network (DNN) classifier based on radiological features to further enhance the clinical translatability, providing a non-invasive tool for predicting MOFS subtypes. Overall, these findings highlight the potential of multimodal fusion in improving the classification, prognostic accuracy, and precision therapy of IDH-wildtype glioma, offering an avenue for personalized management.
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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 OpenQuestion graph.