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locationhttps://doi.org/10.7937/jc8x-9874

Summary

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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.

Column

A note about available TCIA data which

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were converted for use in this

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Challenge: (Training, Validation, Test)

Dr. Bakas's group has provided skull-stripped challenge TRAINING here provides brain-extracted Segmentation task BraTS 2021 challenge TRAINING and VALIDATION set data in NIfTI that do not pose DUA-level risk of potential facial reidentification, and segmentations to go with them.
This group has provided some of the skullbrain-stripped extracted BraTS challenge TEST data in NIfTI, and segmentations to go with them , available upon request through the helpdesk. If you want the DICOM-format (here and here, from the 2018 challenge, request via TCIA's Helpdesk.

This group here provides brain-extracted Classification task BraTS 2021 challenge TRAINING and VALIDATION set data includes DICOM→ NIfTI→ dcm files, registered to original orientation, data files that do not strictly adhere to the DICOM standard. BraTS 2021 Classification challenge TEST files are unavailable at this time.

You may want the original corresponding DICOM-format files drawn from TCIA Collections; please note that these original data are not brain-extracted and may files, then as part of page 1 of this Agreement form TCIA needs to have the citation(s) for the (TCGA-GBM, CPTAC-GBM, Ivy GAP, TCGA-LGG, UPENN-GBM, UCSF-PDGM, ACRIN-FMISO-Brain) filled in. These earlier data that pose enough reidentification risk that TCIA must keep them behind an explicit usage agreement follow this restriction.


Please also note that specificity of the which exact series in DICOM became which exact volume in NIfTI has, unfortunately, been lost to time but the available lists below (in most cases, by Collection, TCIA Patient ID, Study date) represent our best effort at reconstructing the BraTS input imaginglink to the BraTS source files.


Acknowledgements

We would like to acknowledge the individuals and institutions that have provided data for this collection:

...

(Download and

batch1 (processed from ACRIN-FMISO-Brain) 

Localtab Group


Download

Complete dataset all tasks

Localtab
activetrue
titleData Access

Data Access

Tcia license 4 international

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

Tcia head license access


Data TypeDownload
Data Type
all or Query/FilterLicense

Images and Segmentations (NIfTI, 1.4 TB)

Challenge data both tasks (142 GB, 1480 patients, NIfTI, DICOM)


Tcia button generator

complete data on faspex

urlhttps://faspex.cancerimagingarchive.net/aspera/faspex?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjYzNiIsInBhc3Njb2RlIjoiNDM5YTVhZjM3NGRhYjk3OGExYjExMzA4MTcyZDhlMDdkY2Q5OWMzMSIsInBhY2thZ2VfaWQiOiI2MzYiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=

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

BraTS 2021 Segmentation Training set (NIfTI, XX GB )




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


Tcia button generator

batch1

batch2 (processed from CPTAC GBM)

Tcia button generator

batch2

  • batch3 (processed from IvyGAP)

  • batch4 (processed from TCGA-GBM)

  • batch5 (processed from TCGA-LGG)
  • batch6 (processed from UPENN-GBM)
    • batch7 (processed from UCSF-PDGM)
    (Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) 

    Tcia cc by 4

    BraTS 2021 Segmentation Validation set (NIfTI, XX GB)

      1. batch1 (processed from CPTAC GBM)

      2. batch2 (processed from IvyGAP)

      3. batch3 (processed from TCGA-GBM)

      4. batch4 (processed from TCGA-LGG)
      5. batch5 (processed from UPENN-GBM)
      6. batch6 (processed from UCSF-PDGM)

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

    Tcia cc by 4

    BraTS 2021 Segmentation Validation 

    Brand new data files not elsewhere on TCIA

    (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 Training 
    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 Training 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

    Complete training ( highest faspex folder) 
        ( folder of BRats2021-CPTAC-GBM) involved in Seg task training 
        ( folder of Brats2021-TCGA-GBM) involved in seg task training
        
    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.

    Collections Used in this Third Party Analysis

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

    Tcia head license access
    Be sure to include "RSNA-ASNR-MICCAI-BraTS-2021 DOI: 10.7937/jc8x-9874" in the COLLECTION section of your form to assure the request is processed appropriately. 

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

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


    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 corresponding 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

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


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

    seg valid

    Download requires the NBIA Data Retriever

    Tcia restricted license

    Original corresponding 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 corresponding imaging from UCSF-PDGM v1

    complete validation involved in classifier from xx collection forms a batch here) 

    Clinical data (CSV)

    Tcia button generator

    Tcia cc by 4

    Tcia restricted license

    Feature matrices (format, ##GB)

    link or attachment

    Tcia cc by 4

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


    Tcia button generator
    urlhttps://
    wiki
    faspex.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.

    Additional Resources for this Dataset

    Nci_crdc additional resources

    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 TypeDownloadLicenseDICOM Used in BraTS 2021 Segmentation Training set from 

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

    1. BraTS2021_ACRIN-FMISO-Brain_Seg-Task-Training.tcia
    2. BraTS2021_TCGA-LGG_Seg-Task-Training.tcia
    3. BraTS2021_TCGA-GBM_Seg-Task-Training.tcia
    4. BraTS2021_IvyGAP_Seg-Task-Training.tcia
    5. BraTS2021_CPTAC-GBM_Seg-Task-Training.tcia
    6. BraTS2021_UPENN-GBM_Seg-Task-Training.tcia

    Note: Limited Access.

    Download requires the NBIA Data Retriever

    Tcia restricted license

    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 , IvyGAP , UPENN-GBM

  • BraTS2021_CPTAC-GBM_Class-Task-Training.tcia
  • BraTS2021_TCGA-GBM_Class-Task-Training.tcia
  • BraTS2021_IvyGAP_Class-Task-Training.tcia
    • BraTS2021_UPENN-GBM_Class-Task-Training.tcia-

    Note: Limited Access.
    Download requires the NBIA Data Retriever

    Tcia restricted license

    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 , IvyGAP , UPENN-GBM

    1. BraTS2021_UPENN-GBM_Seg-Task-Validation.tcia
    2. BraTS2021_IvyGAP_Seg-Task-Validation.tcia

    3. BraTS2021_TCGA-LGG_Seg-Task-Validation.tcia

    4. BraTS2021_CPTAC-GBM_Seg-Task-Validation.tcia

    5. BraTS2021_TCGA-GBM_Seg-Task-Validation.tcia

    Note: Limited Access.
    Download requires the NBIA Data Retriever

    Tcia restricted license

    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 ,  IvyGAP , UPENN-GBM

    1. BraTS2021_UPENN-GBM_Class-Task-Validation.tcia
    2. BraTS2021_IvyGAP_Class-Task-Validation.tcia
    3. BraTS2021_TCGA-GBM_Class-Task-Validation.tcia
    4. BraTS2021_CPTAC-GBM_Class-Task-Validation.tcia

    Note: Limited Access.
    Download requires the NBIA Data Retriever

    Tcia restricted license

    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  nonDICOM. New package?

    Data retriever or Faspex?

    Tcia cc by 4

    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>
    Localtab
    titleDetailed Description

    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:

  • Manifests 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: 

    •  Here are some data splits that you might find useful. 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

    aspera/faspex?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjY3OSIsInBhc3Njb2RlIjoiZmEwODZjMDQyNGNkOGM4OTllZTRjY2VmZTE0ZGUyM2FkMjA3N2M5NSIsInBhY2thZ2VfaWQiOiI2NzkiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=


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

    		CC BY 4.0

    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.

    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.



    Localtab
    titleDetailed Description

    Detailed Description

    Image Statistics

    		Radiology Image Statistics

    Modalities

    MR, Segmentations

    Number of Patients

    1,480

    Number of Studies


    Number of Series

    7,131

    Number of Images

    407,245

    Images Size (GB)140


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

    NOTE: Segmentation task nifti: Number of Images  7,131 (Seg) , Images Size (GB)12 (Seg) 

    NOTE: Classification task nifti+DICOM: Number of Images 400,114 (Class), Images Size (GB) 128 (Class)

    Segmentation labels of the different glioma sub-regions considered for evaluation are the "enhancing tumor" (ET), the "tumor core" (TC), and the "whole tumor" (WT). The ET is described by areas that show hyper-intensity in T1Gd when compared to T1, but also when compared to “healthy” white matter in T1Gd. The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (NCR) parts of the tumor. The appearance of NCR is typically hypo-intense in T1-Gd when compared to T1. The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edematous/invaded tissue (ED), which is typically depicted by hyper-intense signal in FLAIR. The provided segmentation labels have values of 1 for NCR, 2 for ED, 4 for ET, and 0 for everything else.

    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).



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

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

    44 authors, most with ORCiD. 

    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:

    Source Data TypeDownloadLicense
    Localtab
    titleCitations & Data Usage Policy

    Citations & Data Usage Policy

    Tcia limited license policy

    Info
    titleData Citation
    Info
    titlePublication Citation

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

    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
    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

    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:

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

    1. Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.
      2. S.
    2. , Freymann, J.
      3. B.
    3. , Farahani, K., & Davatzikos, C. (2017).
      4. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. In Scientific Data (Vol. 4, Issue 1)
    4. Segmentation Labels for the Pre-operative Scans of the TCGA-GBM collection [Data set]. The Cancer Imaging Archive. https://doi.org/10.
      5. 1038
    5. 7937/K9/
      6. sdata
    6. TCIA.2017.
      7. 117
      In addition, please be specific and also cite the following datasets that were part of this Challenge:
    7. KLXWJJ1Q
    8. 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-
      9. GBM
    9. LGG collection [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/
      10. TCIA.2017.KLXWJJ1QBakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.
    10. TCIA.2017.GJQ7R0EF
    11. 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)
      12. , Freymann, J., Farahani, K., & Davatzikos, C. (2017). Segmentation Labels for the Pre-operative Scans of the TCGA-LGG collection
    12. [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.
      13. 2017
    13. 2016.
      14. GJQ7R0EF
    14. RNYFUYE9
      15. Scarpace
    15. Pedano,
      16. L
    16. N.,
      17. Mikkelsen
    17. Flanders,
      18. T., Cha, S
    18. A. E.,
      19. Rao
    19. Scarpace,
      20. S
    20. L.,
      21. Tekchandani
    21. Mikkelsen,
      22. S
    22. T.,
      23. Gutman
    23. Eschbacher,
      24. D., Saltz,
    24. J.
      25. H
    25. M.,
      26. Erickson
    26. Hermes, B
      27. . J
    27. .,
      28. Pedano, N., Flanders, A. E.
    28. Sisneros, V., Barnholtz-Sloan, J., & Ostrom, Q.
      29. , Barboriak, D., & Pierce, L. J.
    29. (2016). The Cancer Genome Atlas
      30. Glioblastoma Multiforme
    30. Low Grade Glioma Collection (TCGA-
      31. GBM
    31. LGG) (Version
      32. 4
    32. 3) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.
      33. RNYFUYE9
    33. L4LTD3TK 
      34. Pedano
    34. Calabrese,
      35. N
    35. E.,
      36. Flanders
    36. Villanueva-Meyer,
      37. A
    37. J.
      38. E.
    38. ,
      39. Scarpace
    39. Rudie,
      40. L
    40. J.,
      41. Mikkelsen
    41. Rauschecker,
      42. T
    42. A.,
      43. Eschbacher
    43. Baid,
      44. J. M
    44. U.,
      45. Hermes
    45. Bakas,
      46. B
    46. S.,
      47. Sisneros
    47. Cha,
      48. V
    48. S.,
      49. Barnholtz-Sloan
    49. Mongan, J., &
      50. Ostrom
    50. Hess,
      51. Q
    51. C. (
      52. 2016
    52. 2022). The
      53. Cancer Genome Atlas Low Grade Glioma Collection (TCGA-LGG
    53. University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) (Version
      54. 3
    54. 1) [Data set].
      55. The
    55.   The Cancer Imaging Archive.  https://doi.org/10.7937/
      56. K9/TCIA.2016.L4LTD3TKCalabrese, E., Villanueva-Meyer, J
    56. tcia.bdgf-8v37 
    57. Bakas, S., Sako, C., Akbari, H., Bilello, M., Sotiras, A., Shukla, G., Rudie, J. D.,
      58. Rauschecker
    58. Flores Santamaria,
      59. A
    59. N.,
      60. Baid
    60. Fathi Kazerooni,
      61. U
    61. A.,
      62. Bakas
    62. Pati, S.,
      63. Cha
    63. Rathore, S.,
      64. Mongan
    64. Mamourian,
      65. 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 
    65. 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
    66. 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

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