ArticleNeuro-oncology advances
Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional 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 38 papers, 2 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
38 citing papers in PubMed, 2 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- Artificial intelligence models in the surgical planning of low-grade gliomas: a systematic review.Frontiers in oncology · 2025Pooled it
- Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.Frontiers in neuroscience · 2024Pooled it
- 3D Deep Learning for Brain Tumor Segmentation and Survival Prediction: A Comprehensive Multi-Modal Analysis Using the BraTS2020 Dataset.Journal of imaging · 2026Article
- Neurosurgical Application of Artificial Intelligence in Pediatric Neuro-Oncology.Journal of Korean Neurosurgical Society · 2026Article
- AI-powered segmentation and prognosis with missing MRI in pediatric brain tumors.NPJ precision oncology · 2026Article
- Evaluation of CNN-based deep learning models for auto-contouring in glioblastoma radiotherapy: a review.Radiation oncology (London, England) · 2025Review
- The Expanding Frontier: The Role of Artificial Intelligence in Pediatric Neuroradiology.Children (Basel, Switzerland) · 2025Review
- Imaging Clusters of Pediatric Low-Grade Glioma Are Associated with Distinct Molecular Characteristics.AJNR. American journal of neuroradiology · 2025Article
- Operative performance of RAPNO DIPG imaging criteria-a study from the International DIPG/DMG Registry.Neuro-oncology practice · 2025Article
- Applications of artificial intelligence and advanced imaging in pediatric diffuse midline glioma.Neuro-oncology · 2025Review
- Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI.Scientific reports · 2025Article
- Enhancing RAPNO: the need for standardized imaging heuristics and volumetric assessment.Neuroradiology · 2025Article
- Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs.AJNR. American journal of neuroradiology · 2025Article
- Exploring neonatal brain tumors: a narrative review about epidemiology, classification, and management.Annals of medicine and surgery (2012) · 2025Review
- Heterogeneity in response to neoadjuvant radiotherapy between soft tissue sarcoma histotypes: associations between radiology and pathology findings.European radiology · 2025Article
- A review of deep learning for brain tumor analysis in MRI.NPJ precision oncology · 2025Review
- Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma.Nature communications · 2025Article
- Comparison of Vendor-Pretrained and Custom-Trained Deep Learning Segmentation Models for Head-and-Neck, Breast, and Prostate Cancers.Diagnostics (Basel, Switzerland) · 2024Article
- Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation.Neuro-oncology · 2024Review
- Advancing Pediatric Neuro-Oncology: Multi-institutional nnU-Net Segmentation of Medulloblastoma.Radiology. Artificial intelligence · 2024Article
Corrections and comments
- Update of
Authors and funding
23 authors at 6 institutions in 1 country.
Funding
Abstract
Background: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients ( Results: Dice similarity score (median ± SD) was 0.91 ± 0.10/0.88 ± 0.16 for the whole tumor, 0.73 ± 0.27/0.84 ± 0.29 for ET, 0.79 ± 19/0.74 ± 0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98 ± 0.02 for brain tissue in both internal/external test sets. Conclusions: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements.
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.