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  • RSNA-ASNR-MICCAI-BraTS-2021

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Summary

Excerpt

  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 (1,480 here), 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 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 

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Download requires the NBIA Data Retriever

Localtab Group


License
Localtab
activetrue
titleData Access

Data Access


Data TypeDownload all or Query/FilterLicense
Challenge data (both tasks, 142 GB, *.nii.gz or *.dcm)


Tcia button generator
urlhttps://faspex.cancerimagingarchive.net/aspera/faspex/external_deliveries/412?passcode=0007f4d1117719dc5a364012bb435e07bbc7ffa7

complete Challenge data on faspex


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

Tcia cc by 4




ID Crosswalk between BraTS ID and TCIA ID (xlsx, 79 kB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/133073473/BraTS2021_MappingToTCIA.xlsx?api=v2

BraTS2021_MappingToTCIA



Tcia cc by 4

Click the Versions tab for more info about data releases.

Nci_crdc additional resources

Imaging Data Commons (IDC) (Imaging Data)

TCIA Collections Used in these analyses:

Source Data TypeDownloadLicense
Original DICOM used in BraTS 2021 Segmentation Training set from 

CPTAC-GBM , 

,

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

  • Genomic Data Commons (GDC) (Genomic, Digitized Histopathology & Clinical Data)
  • Proteomic Data Commons (PDC) (Proteomic & Clinical Data)


    • Tcia button generator
    urlhttps://wiki.cancerimagingarchive.net/download/attachments/133073473/BraTS2021_TCIAderived_Seg-Task-Training.tcia?api=v2

    seg train

    Download requires the NBIA Data Retriever

    Tcia restricted license

    Original DICOM used in BraTS 2021 MGMT Classifier Training set from 

    CPTAC-GBM , TCGA-GBM , IvyGAP , UPENN-GBM


    Tcia button generator
    urlhttps://wiki.cancerimagingarchive.net/download/attachments/133073473/BraTS2021_TCIAderived_Class-Task-Training.tcia?api=v2

    class train

    Download requires the NBIA Data Retriever

    Tcia restricted license

    Additional Resources for this Dataset

    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.

    TCIA Collections Used (in part) in these analyses:

    		Source Data TypeDownload

    Original DICOM used in BraTS 2021 Segmentation
    Training
    Validation set
    from 
    from CPTAC-GBM , TCGA-GBM , TCGA-LGG , 
    ACRIN-FMISO-Brain (ACRIN 6684)
    IvyGAP , 
    IvyGAP ,
    UPENN-GBM


    Tcia button generator
    urlhttps://wiki.cancerimagingarchive.net/download/attachments/133073473/BraTS2021_TCIAderived_Seg-Task-
    Training
    Validation.tcia?api=v2

    seg

    train

    valid


    Download requires the NBIA Data Retriever

    Tcia restricted license

    Original DICOM used in BraTS 2021 MGMT Classifier
    Training
    Validation set from 

    CPTAC-GBM , TCGA-GBM ,  IvyGAP , UPENN-GBM


    Tcia button generator
    urlhttps://wiki.cancerimagingarchive.net/download/attachments/133073473/BraTS2021_TCIAderived_Class-Task-
    Training
    Validation.tcia?api=v2

    class

    train

    valid


    Download requires the NBIA Data Retriever

    Tcia restricted license

    Original DICOM used in BraTS 2021 Segmentation Validation set from CPTAC-GBM , TCGA-GBM , TCGA-LGG , IvyGAP , UPENN-GBM

    Original imaging from UCSF-PDGM v1


    Tcia button generator
    urlhttps://
    wiki
    faspex.cancerimagingarchive.net/
    download
    aspera/
    attachments
    faspex/
    133073473/BraTS2021_TCIAderived_Seg-Task-Validation.tcia?api=v2

    seg valid

    Tcia restricted license

    		Original DICOM used in BraTS 2021 MGMT Classifier Validation set from 

    CPTAC-GBM , TCGA-GBM ,  IvyGAP , UPENN-GBM

    Tcia button generator
    urlhttps://wiki.cancerimagingarchive.net/download/attachments/133073473/BraTS2021_TCIAderived_Class-Task-Validation.tcia?api=v2

    class valid

    Download requires the NBIA Data Retriever

    Tcia restricted license

    Original imaging from UCSF-PDGM v1

    Tcia button generator
    urlhttps://faspex.cancerimagingarchive.net/aspera/faspex/external_deliveries/383?passcode=bfd9d89cae2d79e6d824ba1a25e04fc6e37907ba

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

    CC BY 4.0

    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:
    external_deliveries/383?passcode=bfd9d89cae2d79e6d824ba1a25e04fc6e37907ba


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

    		CC BY 4.0


    Nci_crdc additional resources

    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.

    Collections Used in this Third Party Analysis

    Below is a list of the Collections used in these analyses:



    Localtab
    titleDetailed Description

    Detailed Description

    Image Statistics

    		Radiology Image Statistics

    Modalities

    MR, Segmentations

    Number of Patients

    1,480

    Number of Studies

    1,480

    Number of Series

    7,131

    Number of Images

    7,131 (Seg) + 400,114 (Class)

    Images Size (GB)12 (Seg) + 128 (Class)


    NOTE:  The "challenge test set dataset" is sequestered on synapse.org (Project SynID: syn25829067). Please see their site 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:



    Notes about Image Registration:

    • Transformation matrices DICOM to NIfTI are not available.
    • Segmentation task image volume have been set to x=y=240 voxels by z=155 voxels.
    • All Radiogenomics Classifier task files are restored to original DICOM resolution & orientation (thus volume may vary).



    Localtab
    titleCitations & Data Usage Policy

    Citations & Data Usage Policy

    Tcia limited license policy


    Info
    titleData Citation

    Baid, U., Ghodasara, S., Mohan, S., Bilello, M., Calabrese, E., Colak, E., Farahani, K., Kalpathy-Cramer, J., Kitamura, F. C., Pati, S., Prevedello, L., Rudie, J., Sako, C., Shinohara, R., Bergquist, T., Chai, R., Eddy, J., Elliott, J., Reade, W., Schaffter, T., Yu, T., Zheng, J., Davatzikos, C., Mongan, J., Hess, C., Cha, S., Villanueva-Meyer, J., Freymann, J. B., Kirby, J. S., Wiestler, B., Crivellaro, P., Colen, R. R., Kotrotsou, A., Marcus, D., Milchenko, M., Nazeri, A., Fathallah-Shaykh, H., Wiest, R., Jakab, A., Weber, M-A., Mahajan, A., Menze, B., Flanders, A E., Bakas, S., (2023) RSNA-ASNR-MICCAI-BraTS-2021 Dataset. The Cancer Imaging Archive DOI: 10.7937/jc8x-9874 


    Info
    titleAcknowledgement

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


    Info
    titlePublication Citation

    1. Baid, U., Ghodasara, S., Mohan, S., Bilello, M., Calabrese, E., Colak, E., Farahani, K., Kalpathy-Cramer, J., Kitamura, F. C., Pati, S., Prevedello, L. M., Rudie, J. D., Sako, C., Shinohara, R. T., Bergquist, T., Chai, R., Eddy, J., Elliott, J., Reade, W., Schaffter, T., Yu, T., Zheng, J., Moawad, A. W., Coelho, L. O., McDonnell, O., Miller, E., Moron, F. E., Oswood, M. C., Shih, R. Y., Siakallis, L., Bronstein, Y., Mason, J. R., Miller, A. F., Choudhary, G., Agarwal, A., Besada, C. H., Derakhshan, J. J., Diogo, M. C., Do-Dai, D D., Farage, L., Go, J. L., Hadi, M., Hill, V. B., Iv, M., Joyner, D., Lincoln, C., Lotan, E., Miyakoshi, A., Sanchez-Montano, M., Nath, J., Nguyen, X. V., Nicolas-Jilwan, M., Ortiz Jimenez, J., Ozturk, K., Petrovic, B. D., Shah, C., Shah, L. M., Sharma, M., Simsek, O., Singh, A. K., Soman, S., Statsevych, V., Weinberg, B. D., Young, R. J., Ikuta, I., Agarwal, A. K.,Cambron, S. C., Silbergleit, R., Dusoi, A., Postma, A. A., Letourneau-Guillon, L., Guzman Perez-Carrillo, G. J., Saha, A., Soni, N., Zaharchuk, G., Zohrabian, V. M., Chen, Y., Cekic, M. M., Rahman, A., Small, J. E., Sethi, V., Davatzikos, C., Mongan, J., Hess, C., Cha, S., Villanueva-Meyer, J., Freymann, J. B., Kirby, J. S., Wiestler, B., Crivellaro, P., Colen, R. R., Kotrotsou, A., Marcus, D., Milchenko, M., Nazeri, A., Fathallah-Shaykh, H., Wiest, R., Jakab, A., Weber, M-A. Mahajan ,A., Menze, B., Flanders, A. E., Bakas, S. (2021). The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification (Version 2). arXiv. DOI: 10.48550/arXiv.2107.02314


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


    Info
    titlePublication Citation

    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). DOI:  10.1109/tmi.2014.2377694


    Info
    titlePublication Citation

    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


    Info
    titleTCIA 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

    Additional Publication Resources:

    The Collection authors suggest the below will give context to this dataset:


    You are free to use and/or refer to the BraTS datasets in your own research. 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) (Version 1) [Data set].  The Cancer Imaging Archive.  https://doi.org/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

    Other Publications Using This Data

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


    Localtab
    titleVersions

    Version 1 (Current): Updated 2023/08/xx



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