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Summary

  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.


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 this Agreement form 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:

  • Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.

  • Continue with any names from additional submitting sites if collection consists of more that one.

  • Copied from Kaggle site: "Data used in this publication were obtained as part of the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge project through Synapse ID (syn25829067)." 

Data Access

Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 4.0 International License under which it has been published. Attribution should include references to the following citations:

Note to curators! This macro is for collections that are restricted due to facial reconstruction possibility.

Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA Restricted License Agreement to help@cancerimagingarchive.net before accessing the data.



Data TypeDownload all or Query/FilterLicense

Images and Segmentations (NIfTI, 1.4 TB)


Complete dataset  

(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) 

CC BY 4.0




BraTS 2021 Segmentation  Training set in batches of XXX PatientID Images, Segmentations (NIfTI, ) from CPTAC



  1. batch1 (ID PP to QQ) (XX GB)  

  2. batch2 (ID XX to YY) (XX GB)  

  3.  (and so on)



    (Download and apply the     IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) 

CC BY 4.0

BraTS 2021 Segmentation Validation set in batches of XXX PatientID Images, (maybe not Segmentations?)  (NIfTI, 1.4 TB)

  1.  

  2.  

  3. (Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) 

CC BY 4.0

BraTS 2021 Segmentation Validation set in batches of XXX PatientID  Segmentations  (NIfTI, 1.4 TB)

(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) 

TCIA Restricted (special permission beyond defacing DUA required to be fleshed out later)

BraTS 2021 Segmentation Task Brand new data files not elsewhere on TCIA

(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) 

CC by 4.0



BraTS Task2 Radiogenomics Classifier task Training images (DICOM, GB)

  1. link to faspex for nifti
  2. link to GDC/PDC if the detail are there ( e.g. TCGA)
  3. dicom-->nifti→dicom also on Faspex

( is this open license stuff?)

BraTS Task2 Radiogenomics Classifier task molecular marker table (JSON/XLS/CSV spreadsheet, MB)

  1. download JSON/XLS/CSV spreadsheet  or 
  2. if external link that would go below not in this table

( is this open license stuff?)

BraTS 2021 Classifier Task Brand new data files not elsewhere on TCIA

(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) 

CC by 4.0



















Clinical data (CSV)


 

CC BY 4.0

TCIA Restricted

Feature matrices (format, ##GB)

link or attachment

CC BY 4.0

Click the Versions tab for more info about data releases.

Additional Resources for this Dataset

Additional Resources for this Dataset

The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.


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:

Source Data TypeDownloadLicense
DICOM Used in BraTS 2021 Segmentation Training set from 

CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP ,

Note: Limited Access.
Download requires the NBIA Data Retriever

TCIA Restricted

To get data used in BraTS-2021 please request the following Collections in your Agreement:

CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP

DICOM Used in BraTS 2021 Classifier Training set from 

CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP ,

Note: Limited Access.
Download requires the NBIA Data Retriever

TCIA Restricted

To get data used in BraTS-2021 please request the following Collections in your Agreement:

CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP

DICOM Used in BraTS 2021 Segmentation Validation set from 

CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP ,

Note: Limited Access.
Download requires the NBIA Data Retriever

TCIA Restricted

To get data used in BraTS-2021 please request the following Collections in your Agreement:

CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP

DICOM Used in BraTS 2021 Classifier Validation set from 

CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP ,

Note: Limited Access.
Download requires the NBIA Data Retriever

TCIA Restricted

To get data used in BraTS-2021 please request the following Collections in your Agreement:

CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP

UCSF-PDGM  DICOM

Data retriever or Faspex?

CC BY 4.0

UPENN-GBM DICOM

Data retriever or Faspex?

CC BY 4.0

Images from TCGA-LGG that have been transformed for use in this challenge  - 108 Subjects (DICOM, 8.5 GB)

  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 . 

TCIA Restricted

Images from TCGA-GBM that have been transformed for use in this challenge- 135 subjects (DICOM, 6 GB)

 

Note: Limited Access.
Click the Download  button to save a ".tcia" manifest file, needs the NBIA Data Retriever

TCIA Restricted

Images from Ivy GAP that have been transformed for use in this challenge- XXX subjects (DICOM, XX GB)

  Note: Limited Access.
Click the Download  button to save a ".tcia" manifest file, needs the  NBIA Data Retriever

TCIA Restricted


Third Party Analyses of this Dataset

TCIA 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>


Detailed Description

Image Statistics

Radiology Image StatisticsPathology Image Statistics

Modalities



Number of Patients



Number of Studies



Number of Series



Number of Images



Images Size (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.

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:

Note: Transformation matrices DICOM to NIfTI are not available.ll Seg tasks are x/y/z 240/240/155 and all Radiogenomics Classifier files are restored to original DICOM resolution & orientation.


Citations & Data Usage Policy

Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:

Data Citation

DOI goes here. Create using Datacite with information from Collection Approval form

44 authors, most with ORCiD. 

Publication Citation

U.Baid, et al., "The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification", arXiv:2107.02314, 2021. 

Acknowledgement

"The results <published or shown> here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/."


TCIA Citation

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 Data

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.

Copied from the Kaggle site: 

You are free to use and/or refer to the BraTS datasets in your own research, provided that you always cite the flagship manuscript (published or pre-published) resulting from the challenge as well as the following three manuscripts:

  1.  U.Baid, et al., The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification, arXiv:2107.02314, 2021.
  2. Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.-A., Arbel, T., Avants, B. B., Ayache, N., Buendia, P., Collins, D. L., Cordier, N., … Van Leemput, K. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). In IEEE Transactions on Medical Imaging (Vol. 34, Issue 10, pp. 1993–2024). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tmi.2014.2377694
  3. Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017). Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. In Scientific Data (Vol. 4, Issue 1). https://doi.org/10.1038/sdata.2017.117

    In addition, please be specific and also cite the following datasets that were part of this Challenge:
    1. Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., & Davatzikos, C. (2017). Segmentation Labels for the Pre-operative Scans of the TCGA-GBM collection [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q
    2. Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., & Davatzikos, C. (2017). Segmentation Labels for the Pre-operative Scans of the TCGA-LGG collection [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
    3. Scarpace, L., Mikkelsen, T., Cha, S., Rao, S., Tekchandani, S., Gutman, D., Saltz, J. H., Erickson, B. J., Pedano, N., Flanders, A. E., Barnholtz-Sloan, J., Ostrom, Q., Barboriak, D., & Pierce, L. J. (2016). The Cancer Genome Atlas Glioblastoma Multiforme Collection (TCGA-GBM) (Version 4) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.RNYFUYE9
    4. Pedano, N., Flanders, A. E., Scarpace, L., Mikkelsen, T., Eschbacher, J. M., Hermes, B., Sisneros, V., Barnholtz-Sloan, J., & Ostrom, Q. (2016). The Cancer Genome Atlas Low Grade Glioma Collection (TCGA-LGG) (Version 3) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.L4LTD3TK
    5. Calabrese, E., Villanueva-Meyer, J., Rudie, J., Rauschecker, A., Baid, U., Bakas, S., Cha, S., Mongan, J., & Hess, C. (2022). The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) [Dataset].  The Cancer Imaging Archive.  https://10.7937/tcia.bdgf-8v37 
    6. Bakas, S., Sako, C., Akbari, H., Bilello, M., Sotiras, A., Shukla, G., Rudie, J. D., Flores Santamaria, N., Fathi Kazerooni, A., Pati, S., Rathore, S., Mamourian, E., Ha, S. M., Parker, W., Doshi, J., Baid, U., Bergman, M., Binder, Z. A., Verma, R., … Davatzikos, C. (2021). Multi-parametric magnetic resonance imaging (mpMRI) scans for de novo Glioblastoma (GBM) patients from the University of Pennsylvania Health System (UPENN-GBM) (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.709X-DN49

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