ArticleNeuro-oncology advances
Exploring adult glioma through MRI: A review of publicly available datasets to guide efficient image analysis.
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 5 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.
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Who cites it
5 citing papers in PubMed.
- Knowledge-guided brain tumor segmentation via synchronized visual-semantic-topological prior fusion.BMC medical imaging · 2026Article
- A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Development.Journal of imaging · 2025Review
- Free water elimination tractometry reveals local and remote white matter alterations in diffuse gliomas.Journal of neuro-oncology · 2025Article
- A critical assessment of artificial intelligence in magnetic resonance imaging of cancer.Npj imaging · 2025Review
- Validation of open-source deep learning segmentation tools for automated glioma volumetry: a narrative review of Dice scores, workflow efficiency, and clinical RANO 2.0 implementation.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Publicly available data are essential for the progress of medical image analysis, in particular for crafting machine learning models. Glioma is the most common group of primary brain tumors, and magnetic resonance imaging (MRI) is a widely used modality in their diagnosis and treatment. However, the availability and quality of public datasets for glioma MRI are not well known. Methods: In this review, we searched for public datasets of glioma MRI using Google Dataset Search, The Cancer Imaging Archive, and Synapse. Results: A total of 28 datasets published between 2005 and May 2024 were found, containing 62 019 images from 5515 patients. We analyzed the characteristics of these datasets, such as the origin, size, format, annotation, and accessibility. Additionally, we examined the distribution of tumor types, grades, and stages among the datasets. The implications of the evolution of the World Health Organization (WHO) classification on tumors of the brain are discussed, in particular the 2021 update that significantly changed the definition of glioblastoma. Conclusions: Potential research questions that could be explored using these datasets were highlighted, such as tumor evolution through malignant transformation, MRI normalization, and tumor segmentation. Interestingly, only 2 datasets among the 28 studied reflect the current WHO classification. This review provides a comprehensive overview of the publicly available datasets for glioma MRI currently at our disposal, providing aid to medical image analysis researchers in their decision-making on efficient dataset choice.
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Registered trials
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