ReviewNeuro-oncology2024
Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation.
Review in Neuro-oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
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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.
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Who cites it
19 citing papers in PubMed.
- Application of artificial intelligence in paediatric oncology imaging.Pediatric radiology · 2026Review
- Toward uncertainty-aware manual delineation of brain tumours using eye-tracking and image-derived features.Physics and imaging in radiation oncology · 2026Article
- Advances in the management of pediatric low-grade glioma.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026Review
- Multi-task deep learning assists detection and diagnosis of gliomas and brain metastases.NPJ digital medicine · 2026Article
- Neurosurgical Application of Artificial Intelligence in Pediatric Neuro-Oncology.Journal of Korean Neurosurgical Society · 2026Article
- Contrast-Enhanced MR Fingerprinting With Delta-Relaxometry: Investigating a New Avenue for Tumor Characterization.Journal of magnetic resonance imaging : JMRI · 2026Observational
- AI-powered segmentation and prognosis with missing MRI in pediatric brain tumors.NPJ precision oncology · 2026Article
- Matched MRI, Segmentations, and Histopathologic Images of Brain Metastases from Primary Lung Cancer.Scientific data · 2026Article
- Response Assessment in Neuro-Oncology (RANO) 2009-2025: Broad scope and implementation-A progress report.Neuro-oncology · 2025Review
- The Expanding Frontier: The Role of Artificial Intelligence in Pediatric Neuroradiology.Children (Basel, Switzerland) · 2025Review
- Operative performance of RAPNO DIPG imaging criteria-a study from the International DIPG/DMG Registry.Neuro-oncology practice · 2025Article
- 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
- Comment on Dalboni da Rocha et al. Artificial Intelligence for Neuroimaging in Pediatric Cancer.Cancers · 2025Article
- CAR T cell therapy for pediatric central nervous system tumors: a review of the literature and current North American trials.Cancer metastasis reviews · 2024Review
- Advancing Pediatric Neuro-Oncology: Multi-institutional nnU-Net Segmentation of Medulloblastoma.Radiology. Artificial intelligence · 2024Article
- Current state and promise of user-centered design to harness explainable AI in clinical decision-support systems for patients with CNS tumors.Frontiers in radiology · 2024Review
- Automated pediatric brain tumor imaging assessment tool from CBTN: Enhancing suprasellar region inclusion and managing limited data with deep learning.Neuro-oncology advancesArticle
- Enhancing efficiency in pediatric brain tumor segmentation using a pathologically diverse single-center clinical dataset.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
21 authors.
Funding
Abstract
MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Several response assessment guidelines have been set forth by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working groups in different tumor histologies; however, the visual delineation of tumor components using MRIs is not always straightforward, and complexities not currently addressed by these criteria can introduce inter- and intra-observer variability in manual assessments. Differentiation of non-enhancing tumors from peritumoral edema, mild enhancement from absence of enhancement, and various cystic components can be challenging; particularly given a lack of sufficient and uniform imaging protocols in clinical practice. Automated tumor segmentation with artificial intelligence (AI) may be able to provide more objective delineations, but rely on accurate and consistent training data created manually (ground truth). Herein, this paper reviews existing challenges and potential solutions to identifying and defining subregions of pediatric brain tumors (PBTs) that are not explicitly addressed by current guidelines. The goal is to assert the importance of defining and adopting criteria for addressing these challenges, as it will be critical to achieving standardized tumor measurements and reproducible response assessment in PBTs, ultimately leading to more precise outcome metrics and accurate comparisons among clinical studies.
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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.