ArticleScientific reports2021
Classification of paediatric brain tumours by diffusion weighted imaging and machine learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 53 citations in OpenAlex.
- The role of machine learning, deep learning, and MRI findings in the classification of pediatric posterior fossa tumors.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2026Article
- Article
- Deep learning segmentation of paediatric brain tumours using diffusion-weighted MRI: towards an early,Brain communications · 2026Article
- Accurate Paediatric Brain Tumour Classification Through Improved Quantitative Analysis ofNMR in biomedicine · 2025Article
- Review
- CNS Embryonal Tumor with PLAGL Amplification, a New Tumor in Children and Adolescents: Insights from a Comprehensive MRI Analysis.AJNR. American journal of neuroradiology · 2025Article
- Texture analysis combined with machine learning in radiographs of the knee joint: potential to identify tibial plateau occult fractures.Quantitative imaging in medicine and surgery · 2025Article
- Precise diagnosis of pediatric posterior cranial fossa neoplasms based on 2.5D MRI deep learning.Frontiers in oncology · 2025Article
- Diagnostic Accuracy of MRI and CT Scan Features in Differentiation of Pediatric Ependymoma from Medulloblastoma.Iranian journal of child neurology · 2025Article
- Advanced imaging techniques and non-invasive biomarkers in pediatric brain tumors: state of the art.Neuroradiology · 2024Review
- Enhancing neuro-oncology care through equity-driven applications of artificial intelligence.Neuro-oncology · 2024Review
- Diagnostic Utility of Diffusion-Weighted Imaging in Distinguishing Common Pediatric Posterior Fossa Tumors: A Single Center Retrospective Study.Turkish archives of pediatrics · 2024Article
- 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
- Metabolite profiles of medulloblastoma for rapid and non-invasive detection of molecular disease groups.EBioMedicine · 2024Article
- Article
- [Pediatric posterior fossa tumors].Radiologie (Heidelberg, Germany) · 2023Review
- The Diagnostic Efficiency of Quantitative Diffusion Weighted Imaging in Differentiating Medulloblastoma from Posterior Fossa Tumors: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2022Review
- Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review.Cancers · 2022Review
Corrections and comments
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Authors and funding
21 authors at 11 institutions in 1 country.
Funding
Abstract
To determine if apparent diffusion coefficients (ADC) can discriminate between posterior fossa brain tumours on a multicentre basis. A total of 124 paediatric patients with posterior fossa tumours (including 55 Medulloblastomas, 36 Pilocytic Astrocytomas and 26 Ependymomas) were scanned using diffusion weighted imaging across 12 different hospitals using a total of 18 different scanners. Apparent diffusion coefficient maps were produced and histogram data was extracted from tumour regions of interest. Total histograms and histogram metrics (mean, variance, skew, kurtosis and 10th, 20th and 50th quantiles) were used as data input for classifiers with accuracy determined by tenfold cross validation. Mean ADC values from the tumour regions of interest differed between tumour types, (ANOVA P < 0.001). A cut off value for mean ADC between Ependymomas and Medulloblastomas was found to be of 0.984 × 10
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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.