DFCI-BCH-BWH-PEDs-HGG | MR imaging of pediatric subjects with high-grade gliomas
DOI: 10.7937/v8h6-bg25 | Data Citation Required | 1.1k Views | Image Collection
| Location | Species | Subjects | Data Types | Cancer Types | Size | Status | Updated |
|---|---|---|---|---|---|---|---|
| Brain | Human | 61 | MR, Other | High Grade Glioma, Diffuse Midline Glioma | Public, Complete | 2024/07/16 |
Summary
Background: Pediatric tumors of the central nervous system are the leading cause of cancer-related death among children. High-grade gliomas (HGGs) in children have a five-year survival rate of less than 20%. Due to their rarity, diagnosis is often delayed, treatment strategies rely on historical protocols, and clinical trials necessitate collaboration across multiple institutions. Summary: The DFCI-BCH-BWH-PEDs-HGG 2023 dataset is a comprehensive database of pediatric high-grade gliomas. This dataset includes a retrospective, multi-institutional cohort of conventional/structural magnetic resonance imaging (MRI) sequences, featuring pre- and post-gadolinium T1-weighted (labeled as T1 and T1CE), T2-weighted (T2), and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) images. These data were collected from Boston Children’s Hospital, Dana-Farber Cancer Institute, and Brigham and Women’s Hospital, encompassing a total of 61 cases. The DFCI-BCH-BWH-PEDs-HGG 2023 dataset is a subset of the BraTS-PEDs 2023 challenge as described in this manuscript. Exclusion criteria consisted of (1) Images assessed to be of low quality or with artifacts that would not allow for reliable tumor segmentation, (2) Infants younger than one month of age. Defacing was done using the Pydeface Python algorithm. The corresponding T1, T1CE, T2, and T2-FLAIR expert segmentations will be released as part of a future publication from the BRATS-PEDs organizers.
Inclusion criteria consisted of (pediatric, adolescent, young adult) subjects with histologically-approved high-grade glioma, i.e., high-grade astrocytoma and diffuse midline glioma (DMG), including radiologically or histologically-proven diffuse intrinsic pontine glioma (DIPG), diagnostic or pretreatment imaging.
Data Access
Version 1: Updated 2024/07/16
| Title | Data Type | Format | Access Points | Subjects | License | |||
|---|---|---|---|---|---|---|---|---|
| Images | MR | NIFTI | Download requires IBM-Aspera-Connect plugin |
61 | 245 | CC BY 4.0 | ||
| MR Sequence to Series Description map | Other | XLSX | CC BY 4.0 |
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 the researchers utilizing this collection:
- 2023 Challenge website: https://www.synapse.org/Synapse:syn51156910/wiki/622461
- 2024 Challenge website: https://www.synapse.org/Synapse:syn53708249/wiki/627505
- The recommended viewer for these data is ITK-SNAP version 3.8
- ITK-SNAP software citation: Yushkevich, et al.(2006). User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. In NeuroImage (Vol. 31, Issue 3, pp. 1116–1128). https://doi.org/10.1016/j.neuroimage.2006.01.015
Citations & Data Usage Policy
Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:
Data Citation |
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Zapaishchykova, A.,Vajapeyam, S., Liu, K.X., Poussaint, T.Y., Kann, B.H.. (2024) MR imaging of pediatric subjects with high-grade gliomas (DFCI-BCH-BWH-PEDs-HGG) [Dataset] (Version 1). The Cancer Imaging Archive. https://doi.org/10.7937/v8h6-bg25 |
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
- Dana-Farber/ Boston Children’s Cancer and Blood Disorders Center - Special thanks to Tina Pouissaint, MD and Sri Vajapeyam, PhD from the Department of Radiology.
- Brigham and Women’s/Dana-Farber Cancer Center, MA - Special thanks to Kevin Liu, MD, Anna Zapaishchykova, MS, and Benjamin Kann, MD from the Department of Radiation Oncology and Artificial Intelligence in Medicine Program.
- The project was supported by funding from NIH U54 CA274516.
Related Publications
Publications by the Dataset Authors
The authors recommended the following as the best source of additional information about this dataset:
Publication Citation |
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Kazerooni, A.F., Khalili, N., Liu, X., Haldar, D., Jiang, Z., Anwar, S.M., Albrecht, J., Adewole, M., Anazodo, U., Anderson, H., Bagheri, S., Baid, U., Bergquist, T., Borja, A.J., Calabrese, E., Chung, V., Conte, G.-M., Dako, F., Eddy, J., Ezhov, I., Familiar, A., Farahani, K., Haldar, S., Iglesias, J.E., Janas, A., Johansen, E., Jones, B.V., Kofler, F., LaBella, D., Lai, H.A., Van Leemput, K., Li, H.B., Maleki, N., McAllister, A.S., Meier, Z., Menze, B., Moawad, A.W., Nandolia, K.K., Pavaine, J., Piraud, M., Poussaint, T., Prabhu, S.P., Reitman, Z., Rodriguez, A., Rudie, J.D., Sanchez-Montano, M., Shaikh, I.S., Shah, L.M., Sheth, N., Shinohara, R.T., Tu, W., Viswanathan, K., Wang, C., Ware, J.B., Wiestler, B., Wiggins, W., Zapaishchykova, A., Aboian, M., Bornhorst, M., de Blank, P., Deutsch, M., Fouladi, M., Hoffman, L., Kann, B., Lazow, M., Mikael, L., Nabavizadeh, A., Packer, R., Resnick, A., Rood, B., Vossough, A., Bakas, S., Linguraru, M. G. (2023). The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs) (Version 6). arXiv. https://doi.org/10.48550/ARXIV.2305.17033 |
No other publications were recommended by dataset authors.
Research Community Publications
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