Summary
These MRIs are pre-operative examinations performed in 159 subjects with Low Grade Gliomas (WHO grade II & III). Segmentation of tumors in three axial slices that include the one with the largest tumor diameter and ones below and above are provided in NiFTI format. Tumor grade and histologic type are also available. All of these subjects have biopsy proven 1p/19q results, performed using FISH. For the 1p/19q status "n/n" means neither 1p nor 19q were deleted. "d/d" means 1p and 19q are co-deleted.
Data Access
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| Data Type | Download all or Query/Filter |
|---|---|
| Images (DICOM, 2.7GB) | (Download requires NBIA Data Retriever ) |
| Segmentations (NiFTI, 2.9GB) | |
| 1p19q Status and Histologic Type |
|
Click the Versions tab for more info about data releases.
Detailed Description
Collection Statistics | Updated 2020/06/26 |
|---|---|
Modalities | MRI, SEG |
Number of Participants | 159 |
Number of Studies | 160 |
Number of Series | 319 |
Number of Images | 17360 |
| Image Size (GB) | 2.7 |
Supporting Documentation and Metadata
For the 1p/19q status "n/n" means neither 1p nor 19q were deleted. "d/d" means 1p and 19q are co-deleted.
Citations & Data Usage Policy
Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 3.0 Unported License under which it has been published. Attribution should include references to the following citations:
Data Citation
Erickson, Bradley; Akkus, Zeynettin; Sedlar, Jiri; Korfiatis, Panagiotis. (2017). Data From LGG-1p19qDeletion. The Cancer Imaging Archive. DOI: https://doi.org/10.7937/K9/TCIA.2017.dwehtz9v
Publication Citation
Zeynettin Akkus, Issa Ali, Jiří Sedlář, Jay P. Agrawal, Ian F. Parney, Caterina Giannini, and Bradley J. Erickson.Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence. J Digit Imaging. 2017 Aug; 30(4): 469–476. Published online 2017 Jun 9. DOI: https://doi.org/10.1007/s10278-017-9984-3. PMCID: PMC5537096
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, Journal of Digital Imaging, Volume 26, Number 6 pp 1045-1057. DOI: https://doi.org/10.1007/s10278-013-9622-7
Other Publications Using This Data
- https://doi.org/10.1007/s10278-017-9965-6 Bradley J. Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy Kline, Kenneth Philbrick. Toolkits and Libraries for Deep Learning. Journal of Digital Imaging 2017 p1618-1627.
TCIA maintains a list of publications that leverage our data. If you have a publication you'd like to add, please contact the TCIA Helpdesk .
Version 2: Updated 6/26/2020
| Data Type | Download all or Query/Filter |
|---|---|
| Images and Segmentations (2.7GB) | |
| Segmentations only (DICOM) |
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| 1p19q Status and Histologic Type |
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Previously the segmentations of the tumors were provided in NIfTI format and only included three axial slices (the one with the largest tumor diameter and ones below and above). In version 2 segmentations of the entire T2 signal abnormality are provided in DICOM-SEG format.
Version 1 (Current): Updated 2017/09/30
| Data Type | Download all or Query/Filter |
|---|---|
| Images (2.7GB) | |
| Segmentations (NiFTi, 2.9GB) | |
| 1p19q Status and Histologic Type |
|