ArticleAJNR. American journal of neuroradiology2020
Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging.
Article in AJNR. American journal of neuroradiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
36 citing papers in PubMed, 3 syntheses or guidelines pooled it, 68 citations in OpenAlex.
- Application of radiomics for diagnosis, subtyping, and prognostication of medulloblastomas: a systematic review.Neurosurgical review · 2024Pooled it
- Current state of radiomics in pediatric neuro-oncology practice: a systematic review.Pediatric radiology · 2023Pooled it
- Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review.JCO clinical cancer informatics · 2021Pooled it
- 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
- Differentiating medulloblastoma and pilocytic astrocytoma in children based on multimodal MRI radiomics model.Neuroradiology · 2026Article
- Radiomics in pediatric brain tumors: from images to insights.Discover oncology · 2025Review
- Integrating Radiomics and Lesion Mapping for Cerebellar Mutism Syndrome Prediction.Children (Basel, Switzerland) · 2025Article
- Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning.BMC cancer · 2025Article
- Precise diagnosis of pediatric posterior cranial fossa neoplasms based on 2.5D MRI deep learning.Frontiers in oncology · 2025Article
- Review
- Advanced imaging techniques and non-invasive biomarkers in pediatric brain tumors: state of the art.Neuroradiology · 2024Review
- Radiomics and artificial intelligence applications in pediatric brain tumors.World journal of pediatrics : WJP · 2024Review
- Applications of Artificial Intelligence for Pediatric Cancer Imaging.AJR. American journal of roentgenology · 2024Review
- Human-Level Differentiation of Medulloblastoma from Pilocytic Astrocytoma: A Real-World Multicenter Pilot Study.Cancers · 2024Article
- Radiomics for Differentiation of Pediatric Posterior Fossa Tumors: A Meta-Analysis and Systematic Review of the Literature.Cancers · 2023Review
- Applications of artificial intelligence in magnetic resonance imaging of primary pediatric cancers: a scoping review and CLAIM score assessment.Japanese journal of radiology · 2023Article
- Article
- Deep learning based clinico-radiological model for paediatric brain tumor detection and subtype prediction.Exploration of targeted anti-tumor therapy · 2023Article
- Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review.Cancers · 2022Review
- How Machine Learning is Powering Neuroimaging to Improve Brain Health.Neuroinformatics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 9 institutions in 4 countries.
Funding
Abstract
background and purposeDifferentiating the types of pediatric posterior fossa tumors on routine imaging may help in preoperative evaluation and guide surgical resection planning. However, qualitative radiologic MR imaging review has limited performance. This study aimed to compare different machine learning approaches to classify pediatric posterior fossa tumors on routine MR imaging. MATERIALS AND
methodsThis retrospective study included preoperative MR imaging of 288 patients with pediatric posterior fossa tumors, including medulloblastoma (
resultsFor 3-way classification, the radiomics model by automatic machine learning with the Tree-Based Pipeline Optimization Tool achieved a test micro-averaged area under the curve of 0.91 with an accuracy of 0.83, while the most optimized model based on the feature-selection method χ
conclusionsAutomatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.
Indexed as
Identifiers
What OpenQuestion holds
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.