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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. 

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Acknowledgements

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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Localtab Group



Localtab
activetrue
titleData Access

Data Access

Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.

Tcia head license access

Data TypeDownload all or Query/FilterLicense
Images and Segmentations (DICOM, 2.
7GB
7 GB)

Image Removed   Image Removed

 

Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/25789042/LGG-1p19qDeletion_v2_MRandSEG_Jun2020.tcia?version=1&modificationDate=1593205545466&api=v2



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labelSearch
urlhttps://www.cancerimagingarchive.net/nbia-search/?CollectionCriteria=LGG-1p19qDeletion




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Segmentations only (
NIfTI, 2.9GB) Image Removed (Redirects to large-file storage "Box")
DICOM, 10 kB)


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urlhttps://wiki.cancerimagingarchive.net/download/attachments/25789042/LGG-1p19qDeletion_v2_SEGonly_Jun2020.tcia?version=1&modificationDate=1593205562927&api=v2



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1p19q Status and Histologic Type
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(XLS, 53 kB)


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urlhttps://wiki.cancerimagingarchive.net/download/attachments/25789042/TCIA_LGG_cases_159.xlsx?version=1&modificationDate=1509045953290&api=v2



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Localtab
titleDetailed Description

Detailed Description


, NIfTI

Collection StatisticsUpdated

2020/06/26Radiology image statistics

ModalitiesMRI

MR, SEG

Number of Participants

159

Number of Studies

160

Number of Series

478

Number of Images17519

17,519

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.





Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy 

publictcia-collectionlimited-license-policy

Info
titleData Citation

Erickson, B., Bradley; Akkus, Z., Zeynettin; Sedlar, J., Jiri; & Korfiatis, PanagiotisP.   (2017). Data From from LGG-1p19qDeletion (Version 2) [Data set]. The Cancer Imaging Archive. DOI: https://doi.org/10.7937/K9/TCIA.2017.dwehtz9vDWEHTZ9V


Info
titlePublication Citation

Zeynettin Akkus,  Issa Z., Ali,  Jiří I., Sedlář,  Jay J., Agrawal, J. P. Agrawal, Parney,  Ian I. F. Parney,  Caterina Giannini, and Bradley , C., & Erickson, B. J. Erickson(2017). 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:   In Journal of Digital Imaging (Vol. 30, Issue 4, pp. 469–476). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-017-9984-3 . PMCID: PMC5537096


Info
titlePublication Citation

Erickson, B. J., Korfiatis, P., Akkus, Z., Kline, T., & Philbrick, K. (2017). Toolkits and Libraries for Deep Learning. In Journal of Digital Imaging (Vol. 30, Issue 4, pp. 400–405). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-017-9965-6


Info
titleTCIA 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 , Volume (Vol. 26, Number Issue 6 pp 1045-1057. DOI: , pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7

Other Publications Using This Data

TCIA maintains a list of publications which leverage our data. If you have a publication you'd like to add, pleasecontact TCIA's Helpdesk.

  1. Banerjee, S., Mitra, S., Masulli, F., & Rovetta, S. (2020). Glioma Classification Using Deep Radiomics. SN Computer Science, 1(4), 209. doi:10.1007/s42979-020-00214-y
  2. Bhattacharya, D., Sinha, N., & Saini, J. (2020). Radial Cumulative Frequency Distribution: A New Imaging Signature to Detect Chromosomal Arms 1p/19q Co-deletion Status in Glioma. Paper presented at the International Conference on Computer Vision and Image Processing.
  3. Casale, R., Lavrova, E., Sanduleanu, S., Woodruff, H. C., & Lambin, P. (2021). Development and external validation of a non-invasive molecular status predictor of chromosome 1p/19q co-deletion based on MRI radiomics analysis of Low Grade Glioma patients. Eur J Radiol, 139, 109678. doi:10.1016/j.ejrad.2021.109678
  4. Du, R., & Vardhanabhuti, V. (2020, 06-08 July 2020). 3D-RADNet: Extracting labels from DICOM metadata for training general medical domain deep 3D convolution neural networks. Paper presented at the Third Conference on Medical Imaging with Deep Learning (MIDL 2020), Montréal, QC, Canada. Available from https://proceedings.mlr.press/v121/du20a.html.
  5. Gore, S., & Jagtap, J. (2021). Radiogenomic analysis: 1p/19q codeletion based subtyping of low-grade glioma by analysing advanced biomedical texture descriptors. Journal of King Saud University - Computer and Information Sciences. doi:10.1016/j.jksuci.2021.08.024
  6. Kobayashi, T. (2022). RadiomicsJ: a library to compute radiomic features. Radiol Phys Technol, 15(3), 255-263. 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 .
  1. :10.1007/s12194-022-00664-4
  2. Kocak, B., Durmaz, E. S., Ates, E., Sel, I., Turgut Gunes, S., Kaya, O. K., . . . Kilickesmez, O. (2019). Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status. Eur Radiol. doi:10.1007/s00330-019-06492-2
  3. Ning, Z., Luo, J., Xiao, Q., Cai, L., Chen, Y., Yu, X., . . . Zhang, Y. (2021). Multi-modal magnetic resonance imaging-based grading analysis for gliomas by integrating radiomics and deep features. Ann Transl Med, 9(4), 298. doi:10.21037/atm-20-4076
  4. Öksüz, C., Urhan, O., & Güllü, M. K. (2022). Brain tumor classification using the fused features extracted from expanded tumor region. Biomedical Signal Processing and Control, 72, 103356. doi:10.1016/j.bspc.2021.103356
  5. Parekh, V. S., Pillai, J. J., Macura, K. J., LaViolette, P. S., & Jacobs, M. A. (2022). Tumor Connectomics: Mapping the Intra-Tumoral Complex Interaction Network Using Machine Learning. Cancers (Basel), 14(6). doi:https://doi.org/10.3390/cancers14061481
  6. Rathore, S., Chaddad, A., Bukhari, N. H., & Niazi, T. (2020). Imaging Signature of 1p/19q Co-deletion Status Derived via Machine Learning in Lower Grade Glioma. In Radiomics and Radiogenomics in Neuro-oncology (Vol. 11991, pp. 61-69): Springer International Publishing.
  7. van der Voort, S. R., Incekara, F., Wijnenga, M. M., Kapsas, G., Gardeniers, M., Schouten, J. W., . . . French, P. J. (2019). Predicting the 1p/19q co-deletion status of presumed low grade glioma with an externally validated machine learning algorithm. Clinical Cancer Research, clincanres. 1127.2019. doi:10.1158/1078-0432.CCR-19-1127
  8. Yogananda, C. G. B. (2021). Non-invasive Profiling of Molecular Markers in Brain Gliomas using Deep Learning and Magnetic Resonance Images. (Ph.D. Doctor of Philosophy in Biomedical Engineering Dissertation). The University of Texas at Arlington, Proquest. Retrieved from http://hdl.handle.net/10106/29765
  9. Yogananda, C. G. B., Shah, B. R., Nalawade, S. S., Murugesan, G. K., Yu, F. F., Pinho, M. C., . . . Maldjian, J. A. (2021). MRI-Based Deep-Learning Method for Determining Glioma <em>MGMT</em> Promoter Methylation Status. American Journal of Neuroradiology, 1-8. doi:10.3174/ajnr.A7029




Localtab
titleVersions

Version 2 (Current): Updated

6

2020/06/26

/2020

Data TypeDownload all or Query/Filter
Images and Segmentations (2.7GB)

 

Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/25789042/LGG-1p19qDeletion_v2_MRandSEG_Jun2020.tcia?version=1&modificationDate=1593205545466&api=v2



Tcia button generator
labelSearch
urlhttps://www.cancerimagingarchive.net/nbia-search/?CollectionCriteria=LGG-1p19qDeletion


    Image Removed   Image Removed

(Download requires NBIA Data Retriever )

Segmentations only (DICOM)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/25789042/LGG-1p19qDeletion_v2_SEGonly_Jun2020.tcia?version=1&modificationDate=1593205562927&api=v2
Image Removed



(Download requires NBIA Data Retriever )

Segmentations (NIfTI, 2.9GB)

Image Removed

(Redirects to large-file storage "Box")

1p19q Status and Histologic Type


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/25789042/TCIA_LGG_cases_159.xlsx?version=1&modificationDate=1509045953290&api=v2
Image Removed



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 TypeDownload all or Query/Filter
Images (2.7GB)

 

Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/25789042/LGG-1p19qDeletion-doiJNLP-Zr9PZSDF.tcia?version=1&modificationDate=1534787036556&api=v2


  Image Removed  

(Download requires NBIA Data Retriever )

Segmentations (NIfTI, 2.9GB)


Tcia button generator
urlhttps://app.box.com/s/d0ew9t885nktg163ia4r8qntav9boevj
Image Removed



(Redirects to large-file storage "Box")

1p19q Status and Histologic Type


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/25789042/TCIA_LGG_cases_159.xlsx?version=1&modificationDate=1509045953290&api=v2
Image Removed