Summary
The Burdenko's Glioblastoma Progression Dataset (B-GBM-PD) is a systematic data collection of pre-radiotherapy MRI images of 180 patients with primary glioblastoma treated at the Burdenko National Medical Research Center of Neurosurgery between 2014 and 2020. We provide four MRI sequences for all patients - T1, T1c, T2, FLAIR; topometric CT scan; and annotations of the GTV area made for radiotherapy planning. Additionally, the data collection contains follow-up studies (from 1 to 7-time points per patient). Each time-point includes 2-4 MRI sequences (with a minimal set of T1c, FLAIR per patient) with contours of the irradiated volumes. The RTDOSE, RTPLAN files; additional genetic information (IDH1/2, MGMT mutations); and treatment response status (tumor progression, tumor pseudoprogression, treatment response) are available for a subset of subjects. MRI studies were performed on various scanners from different vendors. CT studies were conducted in Burdenko National Medical Research Center of Neurosurgery on a single scanner.
Dataset was collected in the radiation therapy department. The benchmark on the current dataset might be closer to clinical practice owing to data minimal preprocessing, heterogeneity in scanning modalities, sufficient size of the dataset, and longitudinal patient imaging data. It is a novel dataset compared to other TCIA collections. It contains four MRI imaging sequences for each patient (and a CT scan) and manual annotations of the glioblastoma gross tumor volumes (GTV) for radiation treatment planning, thus, complementing BraTS and TCIA-GBM datasets for brain tumor segmentation. The task of delineating GTV currently lacks large publicly available data. Availability of follow-up images allows for assessment of treatment response and development of disease progression models. We provide biological and clinical information along with imaging data: sex, age, idh1/2, mgmt mutations, and treatment response.
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.
- Continue with any names from additional submitting sites if collection consists of more that one.
Data Access
Data Type | Download all or Query/Filter | License |
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Images, Segmentations, and Radiation Therapy Structures/Doses/Plans (DICOM, XX.X GB) | (Download requires NBIA Data Retriever) | |
Clinical data (CSV) |
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Detailed Description
Image Statistics | Radiology Image Statistics |
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Modalities | CT, MRI, RTSTRUCTS |
Number of Patients | 180 |
Number of Studies | 2000 |
Number of Series | |
Number of Images | |
Images Size (GB) |
Citations & Data Usage Policy
Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:
Data Citation
Draft DOI: https://doi.org/10.7937/e1qp-d183
Publication Citation
We ask on the proposal form if they have ONE traditional publication they'd like users to cite.
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
Other Publications Using This Data
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Version 1 (Current): Updated yyyy/mm/dd
Data Type | Download all or Query/Filter | License |
---|---|---|
Images, Segmentations, and Radiation Therapy Structures/Doses/Plans (DICOM, XX.X GB) | (Download requires the NBIA Data Retriever) | |
Clinical data (CSV) |