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  • RSNA-ASNR-MICCAI-BraTS-2021

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delay5
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 pose enough 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 link to the BraTS input imagingsource files.


Acknowledgements

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

...

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

Clinical data (CSV)

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.  

Localtab Group


Complete dataset all tasks
Localtab
activetrue
titleData Access

Data Access

Tcia license 4 international


Data TypeDownload all or Query/FilterLicense

Images and Segmentations (NIfTI, 1.4 TB)

11.731 GB

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


Tcia button generator
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 Training set (NIfTI, XX GB )




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

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



Tcia cc by 4

BraTS 2021 Validation set (NIfTI, XX GB)

Tcia cc by 4

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

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

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)

urlhttps://wiki.cancerimagingarchive
Tcia button generatorurlhttps://wiki.cancerimagingarchive
.net/download/attachments/133073473/BraTS2021
_MappingToTCIA.xlsx
_TCIAderived_Seg-Task-Training.tcia?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:

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
Source Data TypeDownloadLicenseDICOM Used in BraTS 2021 Segmentation Training
set from 

CPTAC-GBM ,

 TCGA

 TCGA-GBM ,

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

 IvyGAP , UPENN-GBM

BraTS2021_ACRIN


tcia-
FMISO-Brain_Seg-Task-Training.tcia
  • BraTS2021_TCGA-LGG_Seg-Task-Training.tcia
  • BraTS2021_TCGA-GBM_Seg-Task-Training.tcia
  • BraTS2021_IvyGAP_Seg-Task-Training.tcia
  • BraTS2021_CPTAC-GBM_Seg-Task-Training.tcia
  • BraTS2021_UPENN-GBM_Seg-Task-Training.tcia

    • Note: Limited Access.

    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

    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 , 

    Original corresponding imaging from UCSF-PDGM v1


    Tcia button generator
    urlhttps://faspex.cancerimagingarchive.net/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.

    DICOM Used in BraTS 2021 Segmentation Validation set from  ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP , 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

    new trapd-lnk files of only already-used UCSF-PDGM files

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

    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:

    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.



    •  the "challenge test set dataset" is sequestered on synapse.org as a tarball of nii & seg & age & diagnosis. Please see their 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:


    • Spreadsheet list of new (DICOM and NIFTINIfTI) series files with no TCIA DICOM equivalent: NotPreviouslyInTCIA.csv


    • You  Here are some data splits that you might find these splits 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

    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).

    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
    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

    Localtab
    titleDetailed Description

    Detailed Description

    Image Statistics

    		Radiology Image Statistics

    Modalities

    MR, Segmentations

    Number of Patients

    2,040

    Number of Studies

    Number of Series

    7,131

    Number of Images

    Images Size (GB)11.731


    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,

    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 CitationPublication Citation

    1. Baid

    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., 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

    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 The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) [Dataset(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., 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.

    1. , 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 2023/08/25

    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?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




    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

    Localtab
    titleVersions
    Version 1 (Current): Updated 2023/05/23