Observational studyScientific reports2021
Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors.
Observational study in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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
16 citing papers in PubMed, 30 citations in OpenAlex.
- Application of artificial intelligence in paediatric oncology imaging.Pediatric radiology · 2026Review
- Radiomics in pediatric brain tumors: from images to insights.Discover oncology · 2025Review
- Review
- Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.Cancer medicine · 2025Article
- Progress Achieved, Landmarks, and Future Concerns in Biomedical and Health Informatics.Healthcare (Basel, Switzerland) · 2024Review
- Radiomics and artificial intelligence applications in pediatric brain tumors.World journal of pediatrics : WJP · 2024Review
- Article
- Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors.Diagnostics (Basel, Switzerland) · 2023Review
- Multimodal deep learning to predict prognosis in adult and pediatric brain tumors.Communications medicine · 2023Article
- Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine.Current oncology (Toronto, Ont.) · 2023Review
- Developing a metabolic clearance rate framework as a translational analysis approach for hyperpolarizedScientific reports · 2023Article
- Precision diagnostics in children.Cambridge prisms. Precision medicine · 2023Review
- Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI.Diagnostics (Basel, Switzerland) · 2022Review
- Machine learning in neuro-oncology: toward novel development fields.Journal of neuro-oncology · 2022Review
- Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective.Frontiers in oncology · 2022Review
- Radiomic-based prognostic score for survival risk-stratification in pediatric medulloblastoma tumors: A multi-institutional study.Neuro-oncology advancesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 9 institutions in 1 country.
Funding
Abstract
Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols.
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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.