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SummaryFinding better therapies for the treatment of brain tumors is hampered by the lack of consistently obtained molecular data in a large sample set and the ability to integrate biomedical data from disparate sources enabling translation of therapies from bench to bedside. Hence, a critical factor in the advancement of biomedical research and clinical translation is the ease with which data can be integrated, redistributed, and analyzed both within and across functional domains. Novel biomedical informatics infrastructure and tools are essential for developing individualized patient treatment based on the specific genomic signatures in each patient's tumor. The Repository of Molecular Brain Neoplasia Data (REMBRANDT) is aimed at facilitating discovery by connecting the dots between clinical information and genomic characterization data.
Rembrandt contains data generated through the Glioma Molecular Diagnostic Initiative from 874 glioma specimens comprising approximately 566 gene expression arrays, 834 copy number arrays, and 13,472 clinical phenotype data points. These data are currently housed in Georgetown University's G-DOC System and are described in a related manuscript. This image collection was created as a companion data set to augment the larger REMBRANDT project. It contains the pre-surgical magnetic resonance (MR) multi-sequence images from 130 REMBRANDT patients.
Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever. Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.
|Data Type||Download all or Query/Filter|
|Images (DICOM, 9.9GB)|
|Molecular/Clinical Data in G-DOC|
|Clinical Data (XLS)|
|VASARI_MRI_features (gmdi-wiki) (XLS)|
Click the Versions tab for more info about data releases.
Number of Patients
Number of Studies
Number of Series
Number of Images
|Image Size (GB)||9.9|
Clinical and Genomics Data
A clinical data dump was exported from the publicly accessible section of the REMBRANDT Data Portal on 1/16/2014 for convenience to TCIA users. The old data portal has since been retired and all non-image data has been migrated to Georgetown University's G-DOC System.
G-DOC contains extensive clinical, gene, and expression data of the same cases to research the link between radiological phenotype and tissue genotype. Registration is required. After logging in search for the REMBRANDT study to locate the data. The mapping table they provide within G-DOC is required to match TCIA's subject identifiers to the G-DOC identifiers.
In addition, there are imaging feature characterizations provided by neuroradiologists from Thomas Jefferson University (TJU) Hospital. This feature set has become known as "VASARI" and became the starting point for the The Cancer Genome Archive (TCGA) Glioma Phenotype Research Group efforts, which is utilizing data from the TCGA-GBM and TCGA-LGG collections.
- VASARI_MR_featurekey4.pdf- This document is a "key" for understanding and interpreting the annotation spreadsheet.
- VASARI_MRI_features (gmdi-wiki).xls- This document is the actual annotations spreadsheet generated at TJU.
Citations & Data Usage Policy
This collection is freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to firstname.lastname@example.org.
Please be sure to include the following citations in your work if you use this data set:
Scarpace, Lisa, Flanders, Adam E., Jain, Rajan, Mikkelsen, Tom, & Andrews, David W. (2015). Data From REMBRANDT. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2015.588OZUZB
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)
Other Publications Using This Data
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