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Collections Used in this Third Party Analysis

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

Source Data Type	Download	License
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

batch1

Tcia button generator
batch2

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

Tcia cc by 4

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

1. link to faspex for nifti
2. 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)

1. download JSON/XLS/CSV spreadsheet  or 
2. external link to RSNA/Kaggle's accompanying molecular marker table , depending on how they want to do that

( is this open license stuff?)

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

url https://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 url https://wiki.cancerimagingarchive.net/download/attachments/133073473/BraTS2021_TCIAderived_Class-Task-Training.tcia?api=v2 class train Download requires the NBIA Data Retriever

Image Removed 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 . Click the Download  button to save a ".tcia" manifest file to your computer, which you must open with the  

Tcia restricted license

Images from TCGA-GBM that have been transformed for use in this challenge- 135 subjects (DICOM, 6 GB)Image Removed Note: Limited Access.

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

Tcia button generator url https://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

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

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

Tcia button generator

Ivy GAPClick the Download  button to save a ".tcia" manifest file to your computer, which you must open with the  

url

Note: Limited Access.

https://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

CPTAC-GBM DICOM

Original corresponding imaging from UCSF-PDGM v1

tcia-

restricted-licenseUCSF-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)

Tcia button generator

Tcia cc by 4

Clinical data (CSV)

Tcia button generator

Tcia cc by 4

Tcia restricted license

 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.

n/a

• CPTAC-GBM
• TCGA-GBM
• TCGA-LGG
• ACRIN-FMISO-Brain (ACRIN 6684)
• IvyGAP
• UPENN-GBM
• UCSF-PDGM

Additional Resources for this Dataset

Note 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-resourcesThe 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.

• Imaging Data Commons (IDC) (Imaging Data)
Proteomic
• • CPTAC-GBM , TCGA-GBM , TCGA-LGG , UPENN-GBM
• Genomic Data Commons (
PDC
• GDC)
(Proteomic
•  (Genomic, Digitized Histopathology & Clinical Data)
• • CPTAC-GBM , TCGA-GBM , TCGA-LGG
• Proteomic Genomic Data Commons (GDCPDC) (Genomic Proteomic & Clinical Data)
  Note to curators! Below are examples for what to do with other external resources/links that don't fit into the above categories.
  • • CPTAC-GBM

  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

  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>

  • IvyGAP provides access to additional resources for this data:

    • Summary ISH, RNA, gene expression and clinical data
    • Detailed clinical, genomic, and expression array data

  
  

  
  

  Localtab
  title Detailed Description
  Detailed Description Modalities Image Statistics Radiology Image Statistics Pathology 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) << Add any additional information that didn't fit or belong in the Summary section. >> 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: Manifest of case identifiers between BraTS and TCIA, NOTE: includes new series files with no TCIA equivalent: BraTS2021_MappingToTCIA.xlsx Spreadsheet list of cases and series used in prior year BraTS Challenges may also refer to these: Multimodal Brain Tumor Segmentation Challenge 2018 (BraTS) Multimodal Brain Tumor Segmentation Challenge 2019 Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection (BraTS-TCGA-GBM) Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection (BraTS-TCGA-LGG) Spreadsheet list of new (NIfTI) series files with no TCIA DICOM equivalent: NotPreviouslyInTCIA.csv You might find these splits useful to navigate accidental duplication while making superset cohorts. These were processed as input to the BraTS Collection, and will require a Usage Agreement on file. Segmentation Task (Training sets) BraTS2021_TCIAderived_Seg-Task-Training.tcia BraTS2021_ACRIN-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 Classification Task (Training sets)  BraTS2021_TCIAderived_Class-Task-Training.tcia 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 Segmentation Task (Validation sets) BraTS2021_TCIAderived_Seg-Task-Validation.tcia  BraTS2021_UPENN-GBM_Seg-Task-Validation.tcia BraTS2021_IvyGAP_Seg-Task-Validation.tcia BraTS2021_TCGA-LGG_Seg-Task-Validation.tcia BraTS2021_CPTAC-GBM_Seg-Task-Validation.tcia BraTS2021_TCGA-GBM_Seg-Task-Validation.tcia Classification Task (Validation sets)  BraTS2021_TCIAderived_Class-Task-Validation.tcia BraTS2021_UPENN-GBM_Class-Task-Validation.tcia BraTS2021_IvyGAP_Class-Task-Validation.tcia BraTS2021_TCGA-GBM_Class-Task-Validation.tcia BraTS2021_CPTAC-GBM_Class-Task-Validation.tcia We didn't split the UCSF-PDGM v1 data by BraTS task, but excerpted series in 299 cases are here as a faspex package:  BraTS2021_UCSF-PDGMv1  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
  title Citations & Data Usage Policy
  Citations & Data Usage Policy Tcia limited license policy Info title Data 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 title 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/."DOI goes here. Create using Datacite with information from Collection Approval form Info title Publication Citation 1. Baid, U.Baid, et al., "., 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", arXiv:2107.02314, 2021.  (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 title Publication 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 title Acknowledgement Publication 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.2017.117Required 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. Info title 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 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: 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 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 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 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  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  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 TCIA our data. If you have a manuscript you'd like to add please contact the TCIA's Helpdesk.

  
  

  Localtab
  title Versions
  Version X 1 (Current): Updated yyyy/mm/dd copy Access tab table here << One or two sentences about what you changed since last version.  No note required for version 1. >>  2023/08/25 Data Type Download all or Query/Filter License Challenge data (both tasks, 142 GB, *.nii.gz or *.dcm) Tcia button generator url https://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 url https://wiki.cancerimagingarchive.net/download/attachments/133073473/BraTS2021_MappingToTCIA.xlsx?api=v2 BraTS2021_MappingToTCIA Tcia cc by 4