ArticleNeuro-oncology advances
MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Multimodal imaging and AI in brain tumor surgery: current tools and emerging integration.npj biomedical innovations · 2026Review
- Article
- Diffuse Midline Gliomas: Clinical, Diagnostic, and Therapeutic Perspectives.Biomedicines · 2026Review
- Prognostic role of delta radiomics in pediatric pontine diffuse midline gliomas.Journal of neuro-oncology · 2026Article
- Deep Learning Auto-segmentation of Diffuse Midline Glioma on Multimodal Magnetic Resonance Images.Journal of imaging informatics in medicine · 2026Article
- Radiotherapy plus neoadjuvant and concomitant IL-13Rα2-directed immunotoxin therapy for diffuse intrinsic pontine glioma.Communications biology · 2026Article
- Radiomics and clinical data predict pseudoprogression after radiotherapy in high-grade glioma.Frontiers in oncology · 2026Article
- Radiomics in pediatric brain tumors: from images to insights.Discover oncology · 2025Review
- Applications of artificial intelligence and advanced imaging in pediatric diffuse midline glioma.Neuro-oncology · 2025Review
- Intracerebroventricular B7-H3-targeting CAR T cells for diffuse intrinsic pontine glioma: a phase 1 trial.Nature medicine · 2025Article
- Preoperative CT-based radiomics nomogram for progression-free survival prediction in pediatric posterior mediastinal malignancies.Frontiers in oncology · 2025Article
- Pediatric diffuse intrinsic pontine glioma radiotherapy response prediction: MRI morphology and T2 intensity-based quantitative analyses.European radiology · 2024Article
- Advanced imaging techniques and non-invasive biomarkers in pediatric brain tumors: state of the art.Neuroradiology · 2024Review
- An international study presenting a federated learning AI platform for pediatric brain tumors.Nature communications · 2024Article
- Reirradiation for diffuse intrinsic pontine glioma: prognostic radiomic factors at progression.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2024Article
- Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: a two-center study.medRxiv : the preprint server for health sciences · 2024Article
- Decoding Diffuse Midline Gliomas: A Comprehensive Review of Pathogenesis, Diagnosis and Treatment.Cancers · 2023Review
- Adult-type and Pediatric-type Diffuse Gliomas : What the Neuroradiologist Should Know.Clinical neuroradiology · 2023Review
- Cellular Therapy for Children with Central Nervous System Tumors: Mining and Mapping the Correlative Data.Current oncology reports · 2023Review
- Promoting the application of pediatric radiomics via an integrated medical engineering approach.Cancer innovation · 2023Review
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Authors and funding
26 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiffuse intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model.
methodsWe isolated tumor volumes of T1-post-contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naïve DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of least absolute shrinkage and selection operator Cox regression selected optimal features to predict overall survival in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables.
resultsAll selected features were intensity and texture-based on the wavelet-filtered images (3 T1 gray-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first-order mean). This multivariable Cox model demonstrated a concordance of 0.68 (95% CI: 0.61-0.74) in the training dataset, significantly outperforming the clinical-only model (
conclusionsIn this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance.
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