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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.
Acknowledgement:
Harmonization of the components of this dataset, including into standard DICOM representation, was supported in part by the NCI Imaging Data Commons consortium. NCI Imaging Data Commons consortium is supported by the contract number 19X037Q from Leidos Biomedical Research under Task Order HHSN26100071 from NCI.
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active | true |
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title | Data Access |
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| Data AccessClick the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.
Data Type | Download all or Query/Filter |
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Images (DICOM, 2.7GB) | (Download requires NBIA Data Retriever ) | Segmentations (NiFTINIfTI, 2.9GB) | (Redirects to large-file storage "Box") | 1p19q Status and Histologic Type | |
Click the Versions tab for more info about data releases. |
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title | Detailed Description |
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| Detailed Description
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Modalities | MRI, SEG, NIfTI | Number of Participants | 159 | Number of Studies | 160 | Number of Series | 319478 | Number of Images | 1736017519 | Image Size (GB) | 2.7 |
For the 1p/19q status "n/n" means neither 1p nor 19q were deleted. "d/d" means 1p and 19q are co-deleted.
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title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy Public collection license |
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title | Publication Citation |
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| 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 |
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| 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 . |
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| Version 2: Updated 6/26/2020 Data Type | Download all or Query/Filter |
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Images and Segmentations (2.7GB) | (Download requires NBIA Data Retriever ) | Segmentations only (DICOM) | | Segmentations (NIfTI, 2.9GB) | Image Modified (Redirects to large-file storage "Box") | 1p19q Status and Histologic Type | | 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 |
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Images (2.7GB) | | Segmentations (NiFTiNIfTI, 2.9GB) | (Redirects to large-file storage "Box") | 1p19q Status and Histologic Type | |
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