ArticleNature communications2025
Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma.
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 16 papers.
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Who cites it
16 citing papers in PubMed.
- Optimizing MRI sequence selection for glioma classification: a radiomics-based analysis of WHO CNS grade, final pathological diagnosis, and IDH status.La Radiologia medica · 2026Article
- Interpretable habitat radiomics based nomogram for predicting T790M mutation in non-small cell lung cancer with brain metastases.Quantitative imaging in medicine and surgery · 2026Article
- Predicting the region of tumor micro-infiltration in glioblastoma peritumoral edema using a multiparametric mri radiomics model.Radiation oncology (London, England) · 2026Article
- Neurosurgical Application of Artificial Intelligence in Pediatric Neuro-Oncology.Journal of Korean Neurosurgical Society · 2026Article
- 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
- Multimodal data-based graph convolutional networks for predicting outcomes in ovarian cancer receiving neoadjuvant chemotherapy.NPJ precision oncology · 2026Article
- Foundation Model Based on Routine Magnetic Resonance Imaging for Brain Tumor Molecular Profiling and Progression Prediction.JCO precision oncology · 2026Article
- Machine learning-based radiomics modeling of combined subcortical nuclei from quantitative susceptibility mapping for Parkinson's disease diagnosis.Frontiers in aging neuroscience · 2026Article
- Multimodal AI and tumour microenvironment integration predicts metastasis in cutaneous melanoma.Nature communications · 2025Article
- Progress of MRI-based radiomics and deep learning for predicting the prognosis of locally advanced rectal cancer (Review).Oncology letters · 2025Review
- Radiomics-based machine learning for glioma grade classification: a multicenter study with SHapley Additive exPlanations interpretability analysis.Translational cancer research · 2025Article
- Support vector machine-based preoperative identification of IDH-Mutant low-grade gliomas in adult gliomas using clinical features.BMC neurology · 2025Article
- Evaluating analytic strategies to obtain high-resolution, vertex-level measures of cortical neuroanatomy in children in low- and middle-income countries.Communications biology · 2025Article
- A new paradigm for immunotherapy in glioblastoma.Nature medicine · 2025Article
- Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma.Nature communications · 2025Article
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Authors and funding
32 authors.
Funding
Abstract
Pediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable responses to treatment, leading to tumor progression and adverse outcomes in cases where complete resection is unachievable. Early prediction of treatment responsiveness and suitability for immunotherapy has the potential to improve clinical management and outcomes. Here, we present a radiogenomic analysis of pLGGs, integrating MRI and RNA sequencing data. We identify three immunologically distinct clusters, with one group characterized by increased immune activity and poorer prognosis, indicating potential benefit from immunotherapies. We develop a radiomic signature that predicts these immune profiles with over 80% accuracy. Furthermore, our clinicoradiomic model predicts progression-free survival and correlates with treatment response. We also identify genetic variants and transcriptomic pathways associated with progression risk, highlighting links to tumor growth and immune response. This radiogenomic study in pLGGs provides a framework for the identification of high-risk patients who may benefit from targeted therapies.
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