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 is curated for computational image analysis of segmentation and prediction of the MGMT promoter methylation status.

Data Access

Click the Versions tab for more info about data releases.

• 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

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.

• IvyGAP provides access to additional resources for this data:

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

TCIA Collections Used (in part) in these analyses:

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:

Detailed Description

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:

• Manifest of case identifiers between BraTS and TCIA, NOTE: includes new series files with no TCIA DICOM 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 for the BraTS Collection:

  • Segmentation Task (Training sets)

    • 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_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_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_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/xx