ArticleScientific data2022
Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Deciphering the glioblastoma microenvironmental landscape with multi-modal radiogenomics to guide prognosis and personalized therapy.Journal of translational medicine · 2026Article
- MM FD ConvFormer multimodal frequency aware deformable CNN transformer network for robust brain tumor classification.Scientific reports · 2026Article
- Domain-Adaptive MRI Learning Model for Precision Diagnosis of CNS Tumors.Biomedicines · 2026Article
- Data-driven probabilistic mapping of the spatial and molecular landscape of glioma.Brain communications · 2026Article
- Improving radiomics-based isocitrate dehydrogenase 1 prediction in glioma patients using semi-supervised machine learning models.BMC medical imaging · 2025Article
- Assessing inter-rater reliability of MRI features in glioma: a multi-radiologist agreement study.BMC medical imaging · 2025Article
- A foundation model for enhancing magnetic resonance images and downstream segmentation, registration and diagnostic tasks.Nature biomedical engineering · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Article
- AI-Based Glioma Grading for a Trustworthy Diagnosis: An Analytical Pipeline for Improved Reliability.Cancers · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
Abstract
Malignancy of the brain and CNS is unfortunately a common diagnosis. A large subset of these lesions tends to be high grade tumors which portend poor prognoses and low survival rates, and are estimated to be the tenth leading cause of death worldwide. The complex nature of the brain tissue environment in which these lesions arise offers a rich opportunity for translational research. Magnetic Resonance Imaging (MRI) can provide a comprehensive view of the abnormal regions in the brain, therefore, its applications in the translational brain cancer research is considered essential for the diagnosis and monitoring of disease. Recent years has seen rapid growth in the field of radiogenomics, especially in cancer, and scientists have been able to successfully integrate the quantitative data extracted from medical images (also known as radiomics) with genomics to answer new and clinically relevant questions. In this paper, we took raw MRI scans from the REMBRANDT data collection from public domain, and performed volumetric segmentation to identify subregions of the brain. Radiomic features were then extracted to represent the MRIs in a quantitative yet summarized format. This resulting dataset now enables further biomedical and integrative data analysis, and is being made public via the NeuroImaging Tools & Resources Collaboratory (NITRC) repository ( https://www.nitrc.org/projects/rembrandt_brain/ ).
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Registered trials
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