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Data Access

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Collections Used in this Third Party Analysis

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

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

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.

• Imaging Data Commons (IDC) (Imaging Data)
  • CPTAC-GBM , TCGA-GBM , TCGA-LGG , UPENN-GBM
• Genomic Data Commons (GDC) (Genomic, Digitized Histopathology & Clinical Data)
  • CPTAC-GBM , TCGA-GBM , TCGA-LGG
• Proteomic Data Commons (PDC) (Proteomic & Clinical Data)
  • 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.

• IvyGAP provides access to additional resources for this data:

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

Detailed Description

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:

• 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).

Citations & Data Usage Policy

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:

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.

Version 1 (Current): Updated 2023/08/25