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This dataset includes brain MRI scans of adult brain glioma patients, comprising of 4 structural modalities (i.e., T1, T1c, T2, T2-FLAIR) and associated manually generated ground truth labels for each tumor sub-region (enhancement, necrosis, edema), as well as their MGMT promoter methylation status. These scans are a collection of data from existing TCIA collections, but also cases provided by individual institutions and willing to share with a cc-by license. The BraTS dataset describes a retrospective collection of brain tumor structural mpMRI scans of 2,040 patients, acquired from multiple different institutions under standard clinical conditions, but with different equipment and imaging protocols, resulting in a vastly heterogeneous image quality reflecting diverse clinical practice across different institutions. The 4 structural mpMRI scans included in the BraTS challenge describe a) native (T1) and b) post-contrast T1-weighted (T1Gd (Gadolinium)), c) T2-weighted (T2), and d) T2 Fluid Attenuated Inversion Recovery (T2-FLAIR) volumes, acquired with different protocols and various scanners from multiple institutions. Furthermore, data on the O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is provided as a binary label. Notably, MGMT is a DNA repair enzyme that the methylation of its promoter in newly diagnosed glioblastoma has been identified as a favorable prognostic factor and a predictor of chemotherapy response. It is curated for computational image analysis of segmentation and prediction of the MGMT promoter methylation status. |
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A note about available TCIA data which were converted for use in this Challenge
Dr. Bakas's group has provided skull-stripped challenge TRAINING data in NIfTI that do not pose DUA-level risk of potential facial reidentification, and segmentations to go with them. Dr. Bakas's group has provided the skull-stripped challenge TEST data in NIfTI, and segmentations to go with them, available upon request through the helpdesk. If you want the DICOM, then as part of page 1 of
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this Agreement form
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TCIA needs to have the citation for the (TCGA-GBM, CPTAC-GBM, Ivy GAP, TCGA-LGG, other facial-detail-retained Collections) filled in. These earlier data that pose enough reidentification risk that TCIA must keep them behind an explicit usage agreement follow this restriction.
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
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title | Data Access |
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| Data Access Tcia license 4 international |
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Note to curators! This macro is for collections that are restricted due to facial reconstruction possibility.
Data Type | Download all or Query/Filter | License |
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Images , and Segmentations (NIfTI, 1.4 TB)
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complete data on faspex |
(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) | | BraTS 2021 Training set in batches of XXX PatientID Images, Segmentations (NIfTI, 1.4 TB) | batch1 (ID PP to QQ) (XX GB) Tcia button generator |
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batch1 |
batch2 (ID XX to YY) (XX GB) Tcia button generator |
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batch2 |
(and so on)
(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package)
| | BraTS 2021 Validation set in batches of XXX PatientID Images, Segmentations (NIfTI, 1.4 TB) |
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batch1 |
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batch2 |
(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package)
| | BraTS Task2 Radiogenomics Classifier task images (DICOM?, GB) | - link to faspex for nifti
- link to GDC/PDC if the detail are there
| ( is this open license stuff?) | BraTS Task2 Radiogenomics Classifier task molecular marker table (JSON/XLS/CSV spreadsheet, MB) | - download JSON/XLS/CSV spreadsheet or
- if external link that would go below not in this table
| ( is this open license stuff?) | Images from TCGA-LGG that have been transformed for use in this challenge - 108 Subjects (DICOM, 8.5 GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/24282668/doiJNLP-JAMS4RFq.tcia?api=v2 |
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| TCGA-LGG batch1 |
Note: Limited Access. Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever . | | Images from TCGA-GBM that have been transformed for use in this challenge- 135 subjects (DICOM, 6 GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/24282666/doiJNLP-QoOaKUdn.tcia?api=v2 |
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| TCGA-GBM |
Note: Limited Access. Click the Download button to save a ".tcia" manifest file, needs the NBIA Data Retriever | | Images from Ivy GAP that have been transformed for use in this challenge- XXX subjects (DICOM, XX GB) |
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Ivy GAP |
Note: Limited Access. Click the Download button to save a ".tcia" manifest file, needs the NBIA Data Retriever | | CPTAC-GBM DICOM | | | UCSF-PDGM DICOM (note Evan didn't give this to us – only nifti so far) | | | UPENN-GBM DICOM ( others) | | | Transformation matrices DICOM to NII (zip, XXMB) | | | Clinical data (CSV)
| | | Feature matrices (format, ##GB) | | | the "challenge test set dataset" is sequestered on synapse as a tarball of nii & seg & age & diagnosis. | Not available. Please see <website> for more detail. | |
Click the Versions tab for more info about data releases. Additional Resources for this DatasetNote to curators! Use this any time you are linking to NCI's IDC/GDC/PDC resources. The links below are examples and will need to be tailored to point to the specific dataset (see parameters in URLS). Nci_crdc additional resources |
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Note to curators! Below are examples for what to do with other external resources/links that don't fit into the above categories. The following external resources have been made available by the data submitters. These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection. - Software / Code on Github
- Genomics data in DbGAP
- Genomics data in Gene Expression Omnibus
- RSNA or kaggle link to molecular marker table if they only provide us a link
TCIA Collections Used (in part) in these analyses, for exact series see table above:Third Party Analyses of this DatasetTCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection: - <these get filled in as groups cite the dataset in their papers related to analysis of the 2021 task 1 & task2 using this Collection's DOI>
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title | Detailed Description |
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| Detailed DescriptionImage Statistics | Radiology Image Statistics | Pathology Image Statistics |
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<< Add any additional information that didn't fit or belong in the Summary section. >>
- the "challenge test set dataset" is sequestered on synapse as a tarball of nii & seg & age & diagnosis. Not available. Please see <website> for more detail.
The data used in BraTS Challenges often have some overlap with other TCIA Collections, cases, and series. Some filters for handling these, so that you can work with statistically not-duplicated images, include these below: - Manifest of case identifiers between BraTS and TCIA
- Spreadsheet list of cases and series used in prior year BraTS Challenges which may refer also to these:
- Spreadsheet list of new (DICOM and NIFTI) series files with no TCIA DICOM equivalent:
- Manifest of (DICOM and NIFTI) files sourced from other TCIA Collections, so you can avoid superset accidental duplication in case you want everything TCIA has, after you've gotten all of BRaTS data:
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title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy Tcia limited license policy |
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| DOI goes here. Create using Datacite with information from Collection Approval form 44 authors, most with ORCiD. |
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title | Publication Citation |
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| U.Baid, et al., "The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification", arXiv:2107.02314, 2021. |
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| Required acknowledgements only (ex:The CPTAC program requests that publications using data from this program...). If they just want to thank someone, that goes in the Acknowledgement section underneath the Summary. |
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| Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC.https://doi.org/10.1007/s10278-013-9622-7 |
Other Publications Using This DataTCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk. |
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| Version X (Current): Updated yyyy/mm/ddcopy Access tab table here << One or two sentences about what you changed since last version. No note required for version 1. >> |
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